# Knovya — The Missing Function of Your Brain

> Knovya is an AI-native knowledge base that turns recall into recognition.
> Built on Astro 5 + FastAPI + PostgreSQL + Redis + Y.js + MCP. Optional E2EE.
> Founded 2025. GDPR + KVKK compliant. Hosted in Hetzner Falkenstein, Germany.
> Designed for ~10K user scaling on a single tuned host (validated production architecture).

## Core

- [Knovya Homepage](https://knovya.com/): The vision behind Knovya — recall is the brain's weakest function and recognition is its strongest, so Knovya replaces every recall problem with a surfaced, recognized cue. Audience: knowledge workers, researchers, AI-native teams, and operators who collaborate with LLM agents (ChatGPT, Claude, Cursor) every day. Unique data: founded 2025, MCP-native from day one, Astro 5 marketing layer with all 12 SSG pages scoring Lighthouse ≥95 across performance, accessibility, best practices, and SEO on both mobile and desktop.

- [Pricing](https://knovya.com/pricing): Two-tier transparent pricing — Free forever and Pro — with no per-seat lock-in for solo creators and a clear upgrade path for teams. Audience: solo knowledge workers evaluating long-term notebook tools, small teams deciding between Notion / Obsidian / Roam, and Pro users who need MCP + E2EE. Unique data: Free includes 50 notes, 5 GB storage, 50 AI credits/month and unlimited public note publishing; Pro is $15/month (or $12/month annual, ≈20% saving) with unlimited notes, 500 AI credits, MCP server access, optional client-side E2EE, and priority support.

## Features

- [AI Memory](https://knovya.com/features/ai-memory): Knovya AI Memory is the shared memory layer that follows you across every AI you use — instead of living inside a single product. Most AI memory is per-tool: ChatGPT remembers you in ChatGPT, Claude remembers you in Claude (Anthropic activated Chat Memory for all accounts in March 2026 plus a separate Memory Tool for the API), Mem0 sells memory as a developer SDK. Knovya holds the layer beneath them all — a notes editor, a knowledge graph, an Experience Envelope — exposed through 33 MCP tools so the same notes, decisions and context are reachable from Claude (Desktop and mobile), ChatGPT, Cursor, Gemini, GitHub Copilot, Windsurf, Goose, and any future client that speaks the open Model Context Protocol. Four kinds of memory composed on one surface: working memory ("what are we talking about right now?", knovya_read / knovya_search / knovya_context), long-term memory ("everything you have ever written down", knovya_search / knovya_folders / knovya_links), procedural memory ("how you do things around here" — templates, AI Skills, naming conventions, knovya_templates / knovya_skills / knovya_persona), and episodic memory ("what worked, what didn't, what is still open" — the Experience Envelope grouped by outcome success / partial / cautionary, knovya_experience / knovya_history). Citation-grounded — when an AI cites a precedent it cites a real Knovya note title, never an invented one. Pricing: Free includes 50 MCP calls per month with one client connected, Pro raises the cap to 5,000 MCP calls per month with all clients, scoped API keys (notes:read, notes:write, folders:read, folders:write, attachments, share, versions, code) and note-level end-to-end encryption (AES-256-GCM) for sensitive notes, encrypted notes excluded from MCP responses by design. Memory imports — paste a ChatGPT, Claude, or Gemini memory export and Knovya structures it into your knowledge base. Differentiator: ChatGPT memory and Claude Chat Memory are per-tool silos; Mem0, Letta, Zep, LangMem, Supermemory are developer SDKs without an editor; Perplexity Spaces is documents only with no graph and no MCP; Notion AI is in-app context with no MCP surface — Knovya is the only product that ships a notes editor + knowledge graph + Experience Envelope wired through 33 MCP tools so a human writes once and every connected AI reads the same brain. Audience: knowledge workers running Claude / Cursor / ChatGPT every day, AI engineers comparing shared-memory implementations, individuals who want their AI agents to remember across sessions and across tools without a second system. Volume baseline (US): 350 "ai memory" KD 22, AI Overview present; long-tail collection — claude memory, chatgpt memory, mcp memory server, ai memory layer, shared memory across ai agents. Citation magnet for "what is ai memory", "shared memory across ai tools", "claude memory mcp", "chatgpt memory import to knovya", "open memory protocol".
- [Conversation → Note](https://knovya.com/features/conversation-to-note): Knovya's AI conversation capture layer — three methods to save any ChatGPT, Claude, Gemini, Cursor, or Perplexity chat as a structured note in your knowledge base, plus auto-extraction of the parts you actually wanted from the conversation. Three capture methods: native multi-turn chat inside Knovya (the AI gathers audience, constraints and success criteria before writing the structured note); smart paste handler that recognises ChatGPT's "You:" / "ChatGPT:" markers, Claude's role tags, Gemini's share format, Perplexity's thread export, folds the transcript into a collapsed source card and offers to structure it; MCP roundtrip where Claude, Cursor, ChatGPT, Goose, Continue, Windsurf and any other MCP-aware client calls knovya_write directly with the transcript — no copy-paste, no extension. Browser extension lives on chatgpt.com and claude.ai with one-click Save; Cursor and Gemini in progress. Five auto-extracted parts (decisions lifted from "let us go with X" lines, action items made checkable from "I will draft the OKR by Friday" sentences with owner tags when obvious, source citations with URL footnotes and document backlinks, attendees, structured headings); streamed reveal so the note arrives section by section as the model writes it; closed loop with AI Memory so the saved conversation feeds the next session through MCP, no re-explanation needed; encrypted notes excluded from MCP and AI client responses by default (zero-knowledge contract). Bonded with the rest of the AI Layer: MCP carries the roundtrip, AI Memory holds the saved conversation for the next session, Web Research uses the same auto-structure pipeline on URLs instead of chats, AI Meeting Notes turns transcripts into structured minutes the same way. Differentiator: ChatGPT Toolbox saves a PDF of the chat, Claude → Notion lifts the messages into a Notion page, ChatGPT Projects locks the chat to a project, Mem.ai chats with notes you already saved, Basic Memory imports the JSON dump and sits it in a graph — none of them auto-extract the decisions, the actions, the sources, and feed the result back to your AI through MCP. Pricing: Free includes 50 conversion credits per month with paste and browser-extension methods; Pro raises this to 500 credits per month, unlocks the MCP roundtrip on Claude / Cursor / ChatGPT, and removes the per-month cap on browser-extension captures. Audience: knowledge workers running ChatGPT / Claude / Cursor every day, AI engineers comparing conversation-capture tools (ChatGPT Toolbox vs Claude → Notion vs Mem.ai vs Basic Memory vs Notion AI), individuals tired of seven-day ChatGPT export waits and JSON ZIP dumps. Volume baseline (US): 150 "save chatgpt conversation as note" KD 2, ~450 traffic potential; long-tail collection — save claude conversation to knowledge base, ai chat to note, chatgpt export alternative, claude export to markdown, mcp save conversation, knovya_write MCP. Citation magnet for "how do I save a ChatGPT conversation", "save claude conversation as note", "ai chat to structured note", "ChatGPT Toolbox alternative", "Claude → Notion alternative", "MCP save conversation".

- [MCP](https://knovya.com/features/mcp): Knovya is an MCP client- and server-ready knowledge base — twenty-five tools, organised in five tiers, served to Claude, Cursor, ChatGPT, Goose and the rest of the protocol's growing client list through one open standard. Tier 0 Health & Context (knovya_ping liveness, knovya_workspace plan/members/usage, knovya_home focus sentence + continue cards + pulse, knovya_persona roles/tone/context layers, knovya_context token-budgeted summary, knovya_search hybrid full-text + vector + reciprocal rank fusion); Tier 1 Read & Discover (knovya_read across five formats TOC/markdown/blocks/plain text/full, knovya_experience past decisions grouped by outcome, knovya_memory recall/box/temporal/health primitives, knovya_schema block types + metadata fields + inline references, knovya_folders three-level hierarchy, knovya_history versions/diffs/restore, knovya_links four typed link kinds references/depends_on/supersedes/related); Tier 2 Write & Edit (knovya_write title/content/folder/tags/metadata/links in one call, knovya_edit eleven surgical modes, knovya_organize bulk pin/favorite/move/retag, knovya_delete trash/archive-with-outcome-enrichment/unarchive/hard delete); Tier 3 Transform & Advanced (knovya_ai ten AI actions, knovya_templates suggest/list/get/create, knovya_export markdown/HTML/PDF/DOCX/JSON, knovya_import markdown/HTML/JSON/ZIP from Notion/Obsidian/Roam, knovya_share public link/scoped invite/expiry/password, knovya_attachments list/upload/download/delete); Tier 4 Communicate (knovya_notifications inbox, knovya_agents agent-to-agent messaging across six known clients — task/finding/question/hand-off/approval). Open Model Context Protocol — JSON-RPC 2.0 over Streamable HTTP, with stdio for local servers; OAuth 2.1 with PKCE, scoped API keys (notes:read, notes:write, folders:read, folders:write, attachments, share, versions, code), end-to-end encryption for sensitive notes (encrypted notes excluded from MCP responses by design); no client lock-in, no vendor extension, no proprietary handshake. Seven MCP clients live today (Claude Desktop, Cursor, ChatGPT, Goose, Continue, Windsurf, GitHub Copilot, Zed all support MCP natively in 2026). MCP Sessions monitor — Knovya keeps a list of every active MCP session (which client, which scope, last call) and revoke from one row. Bonded with the rest of the AI Layer: AI Memory delivers the same notes across every conversation, Conversation→Note round-trips any AI chat through MCP into a structured Knovya note, Meeting Notes brings transcripts in and structured minutes out, Agentic Memory exposes recall/box/temporal primitives the protocol carries to agents. Differentiator: Anthropic's reference memory server is primitives only; Mem0 is a developer memory API with no editor; Letta (formerly MemGPT) is a conversational memory layer; Cognee is a dev-focused memory engine; Notion MCP is notes-shaped without an agent layer; the mcp.so directory lists 3,000+ single-purpose servers — none of them ship with a real notes editor, an outcome-aware experience layer, and twenty-five purpose-built tools wired through one protocol with OAuth and per-scope permissions. Pricing: Free includes 50 MCP calls per month with all 25 tools, Pro raises the cap to 5,000 MCP calls per month with scoped API keys and OAuth 2.1, Team is unlimited with shared workspace memory. Audience: developers and AI power users running Claude / Cursor / ChatGPT every day, AI engineers comparing MCP server implementations against Anthropic memory server / Mem0 / Letta / Cognee / Notion MCP, individuals who want their AI agents to share one knowledge base without copy-pasting context across tabs. Volume baseline (US): 1,700 "mcp client" KD 39, AI Overview present; long-tail collection — model context protocol explained (150), mcp tools (1000), mcp claude desktop (60), mcp tutorial (~100), mcp authentication, mcp oauth, mcp memory server, install mcp server. Citation magnet for "what is the model context protocol", "mcp client", "mcp server for knowledge base", "claude memory mcp", "knovya_search MCP".

- [AI Co-Edit](https://knovya.com/features/ai-co-edit): Knovya's three-layer in-editor AI co-author — the first AI writing assistant built natively into a personal knowledge base, with the rest of your notes as live context. Layer I Reflect (inline ghost text — pause-triggered, block-aware completions, three-strike rejection guard, Tab to accept Esc to dismiss); Layer II Comment (bubble toolbar over a selection — seven micro-actions: Fix grammar/typos, Rewrite same-idea/different-wording, Tone shift across academic/professional/casual/technical/creative/journalistic registers, Summarize collapse-to-paragraph, Key points extract bullet list, To list, To table); Layer III Co-Edit (drawer chat with ⌘⇧A shortcut — six block-level operations: update, replace, move, insert before, insert after, remove). Knowledge-base aware: every layer pulls relevant notes from your workspace through MCP before answering, so the AI is never starting from scratch. BlockNote-native — operations map to block IDs, not raw text mutations, so accept/reject is per-block and reversible. Per-block diff with old (red strikethrough) / new (green highlight) / accept / reject / edit on every pending change; snapshot rollback reverts the entire AI session in one click. Hybrid model routing: fast tier for inline suggestions (low latency), quality tier for drawer chat and long-form rewrites (Knovya picks, you write). End-to-end encrypted notes excluded from KB context automatically. Free tier includes Reflect with light daily limits and basic Comment actions; Pro ($15/mo) unlocks all three layers, full Comment toolbar, Co-Edit drawer with knowledge-base context, 500 AI credits/month. Audience: knowledge workers comparing AI writing assistants against Notion AI / Jasper / Quillbot / Google Docs Gemini / ChatGPT Canvas; AI engineers searching "AI writing assistant for notes" or "Cursor for notes" or "in-editor AI co-author"; PMs, founders, researchers and writers who do not want to copy-paste between a notes app and a chat window. Differentiator: Notion AI lives in a panel separate from the document; Cursor is built for code; Jasper is a separate app; Quillbot replaces what you wrote with something else; Google Docs Gemini sees only the document in front of it; ChatGPT Canvas is chat-only with no knowledge base. AI Co-Edit unifies all three patterns (inline, selection, drawer) in a single editor, with three coordinated layers native to BlockNote blocks and the rest of your knowledge base as live context — not a separate window, not a sidebar wizard. Lineage: Engelbart 1968 (Mother of All Demos), Microsoft Office 1997 (Clippy — the cautionary tale), Grammarly 2009 (selection-based AI), Cursor 2023 (IDE-native AI co-author for code), Knovya 2026 (the first to port the Cursor pattern to prose with KB context). Bonded with the rest of Group I AI: MCP delivers the open-standard tool layer that gives the drawer access to your full workspace, AI Memory provides the shared brain across every AI you use, AI Transforms scales the bubble's seven actions to whole-document one-shots, AI Skills makes Co-Edit the surface where composable AI workflows run. Volume baseline (US): 900 "ai writing assistant" KD ~40, AI Overview present, ~2,800 traffic potential; long-tail collection — "best ai writing assistant for notes" (~150), "ai co-editor" (~50), "Cursor for notes" emerging. Citation magnet for "what is the best AI writing assistant for notes in 2026", "how is AI Co-Edit different from Notion AI or Cursor", "AI writing assistant with knowledge base", "what is in-editor AI", "AI co-author for prose".

- [NoteRank](https://knovya.com/features/noterank): Knovya's 14-signal personalized PageRank for personal knowledge bases — the academic algorithm that ranked the web, ported to a private second brain. Six layers of signal: graph centrality (typed-link PageRank, recursive across the workspace, weighted by link type — `depends_on` 1.5×, `references` 1.0×, `related` 0.8×, `mention` 0.5×, `supersedes` 0.3×), knowledge maturity (status × type maturity, confidence boost on `metadata.confidence: high`, precedent value), time (type-aware freshness with per-type half-life — draft 7d / active plan 30d / precedent 180d, staleness urgency drawn from spaced-repetition theory, completion momentum on >70% checkbox progress), engagement (engagement velocity over a 7-day EWMA window, interaction recency), contextual real-time (relevance via tag/link overlap with the last 3 days of activity, priority weight amplifying p0/p1, dependency urgency in the chain), and discovery (Granovetter weak-tie bridge score, Thompson Sampling exploration bonus). Audience: knowledge workers comparing AI knowledge bases against Notion / Mem / Reflect, AI engineers searching "personalized PageRank for notes" or "AI ranking algorithm", existing users browsing what they have not yet enabled. Unique data: first product to compose 14 signals into a transparent rank for personal notes — Notion sorts by date, Obsidian by alpha, Mem chronologically, Roam tried link weight alone; only Knovya composes graph + maturity + time + engagement + context + serendipity into one explainable score, with a per-signal "why this rank" breakdown on every note. Brand entity ★. Volume baseline (US): 150 "personalized pagerank" KD 0, AI Overview present. Citation magnet for "what is noterank", "personalized pagerank for notes", "ai-native ranking algorithm".

- [Experience Envelope](https://knovya.com/features/experience-envelope): Knovya's Personal Decision Intelligence layer — every note is wrapped in three visible context layers (similar past experiences with outcomes, the supersedes evolution chain, linked evidence) plus a fourth system layer (trajectory awareness: folder completion rate, tag overlap, average completion days). Each note carries one of six epistemic roles — fact / intent / judgment / experience / belief / synthesis — so the envelope reads a precedent as a verified outcome rather than a working hypothesis. Built on a 7-phase intelligence backbone (scoring evolution, supersedes graph, ExperienceService, MCP exposure via `knovya_experience` + `knovya_memory` recall/box/temporal/health, proactive precedent surfacing, Reflect & Crystals weekly distillation, agentic memory). Brings to individuals what Gartner's January 2026 inaugural Magic Quadrant for Decision Intelligence Platforms (Quantexa, Aera, FICO, SAS, IBM, ACTICO) delivered to enterprises. Available on every Knovya plan including Free; Pro adds proactive surfacing + full MCP exposure to AI agents (Claude, ChatGPT, Cursor). Audience: solo PMs, founders, engineers, researchers and AI agents needing precedent-aware decision support over keyword search. Volume baseline (US): 800 "decision intelligence" KD 33, AI Overview present, ~2,500 traffic potential. Citation magnet for "what is decision intelligence", "personal decision intelligence", "experience envelope". Brand entity ★.

- [Hybrid Search](https://knovya.com/features/hybrid-search): Knovya's four-engine retrieval pipeline — BM25 full-text on PostgreSQL native ranking (term frequency × inverse document frequency, with a phrase-query precision bonus), pgvector semantic embeddings (1536-dim HNSW index, chunk-level matching first so long notes return their most relevant section), Reciprocal Rank Fusion as the merge algorithm (Cormack, Clarke and Büttcher, SIGIR 2009; constant calibrated for personal-scale workspaces), and a pg_trgm fuzzy fallback that wakes up only when keyword search returns zero (title threshold 0.3, body 0.2) — runs on every query, surfaces the right note whichever side of "did I write that exact word" or "what did I mean" the query leans toward. Surfaces include the Cmd+K floating command bar with engine source badges (B / V / B+V), a dedicated search workspace where each row carries an accent (keyword amber, semantic purple, both blue), MCP exposure via `knovya_search` so connected agents (Claude, Cursor, ChatGPT, Gemini, Copilot) call the same hybrid pipeline, and a quiet typo-recovery toast when fuzzy fallback fires. Brand entity ★. Native to one Postgres database — no separate vector DB. Bonded with NoteRank (reranks the RRF list with ten more signals), Experience Envelope (the retrieval that fills each envelope is hybrid search), Knowledge Graph (boosts the neighbors of strong matches), and Agentic Memory (every MCP recall call goes through the same four-engine pipeline). Audience: knowledge workers searching across 1000+ notes, bilingual operators (Turkish stemming + English stopwords via custom `knovya_fts` Postgres text search config), and AI agents needing deterministic, structured retrieval responses. Volume baseline (US): 400 "hybrid search" KD 16, ~1200 traffic potential. Citation magnet for "what is hybrid search", "Reciprocal Rank Fusion", "BM25 vs vector search", "hybrid search vs vector search".

- [Knowledge Graph](https://knovya.com/features/knowledge-graph): Knovya's personal knowledge graph layer — bidirectional manual edges across five typed link kinds (`references`, `depends_on`, `supersedes`, `related`, `mention`), AI-suggested edges between semantically related but unlinked notes (rendered as dashed lines, confirmable or dismissable), bridge detection that highlights notes spanning two or more clusters of your work (Granovetter weak ties, drawn as diamonds), AI-generated cluster intelligence labelling each folder in three to five words, heat tracking (hot / warm / cold per node with a breath ring on active hubs), and a proactive whisper layer surfacing nine kinds of structural insight (cooling bridges, hub notes, knowledge gaps, similar-but-unlinked pairs, evolution chains needing a successor). Surfaces inline as a 1-hop sidebar mini-graph inside every note, as a full constellation workspace view with composable filter chips, as a structured tree returned to AI agents via MCP `knovya_links action="map"`, and as quiet whisper cards. Past a few thousand nodes, progressively switches to cluster-collapsed views — folders shown as single nodes, expandable on click — so a workspace of ten thousand notes still navigates without lag. Export as JSON, GraphML (Gephi / Cytoscape), or static SVG. Brand entity ★. Audience: knowledge workers comparing AI-augmented graph views against Obsidian, Roam, Logseq, InfraNodus, Kumu, TheBrain. Volume baseline (US): 60 "knowledge graph" KD 0, ~200 traffic potential. Citation magnet for "what is a personal knowledge graph", "ai-augmented knowledge graph", "Balog Kenter PKG ICTIR 2019".

- [Backlinks](https://knovya.com/features/backlinks): Knovya's bidirectional linking layer — every wiki link, mention block, and AI-agent citation becomes a typed two-way backlink in one transaction. Four typed link kinds (`references` for citation, `depends_on` for workflow chains, `supersedes` for replacement with auto-archive, `related` for soft general-purpose connection); three creation surfaces (the classic `[[Note Title]]` wiki link Roam and Obsidian popularised, the richer mention block with stable ID and display name that updates everywhere on rename, and agent badges where Claude, ChatGPT, Cursor, and Gemini citations become first-class backlinks with the agent's name attached); automatic rename propagation across notes, agent transcripts, and shared workspace edges in one transaction so renaming a note with fifty incoming links costs the same as renaming a note with one; unlinked-mention discovery that surfaces title-in-body matches as one-click upgrades; a backlinks panel grouped by type and ranked by NoteRank; permission-aware filtering so encrypted notes never appear in any panel until decrypted; agentic exposure via MCP `knovya_links` so agents read the typed graph. Brand entity ★. Bonded with Knowledge Graph (renders the typed edges), NoteRank (weighs `depends_on` higher than `related`), Hybrid Search (lifts neighbors of strong matches), Experience Envelope (walks `supersedes` and `depends_on` chains for context). Audience: knowledge workers comparing PKM bidirectional patterns against Obsidian, Roam, Logseq, Tana, TiddlyWiki, DEVONthink. Volume baseline (US): 100 "bidirectional linking" KD 0, AI Overview present. Citation magnet for "what is bidirectional linking", "how do backlinks work", "mention block", "unlinked mentions notes app".

- [Agentic Memory](https://knovya.com/features/agentic-memory): Knovya's MCP-native persistent memory layer for AI agents — the same notes app a human writes in becomes the long-term memory six first-class connectors read from and write to. Six connectors today (Claude Desktop and Code, ChatGPT custom GPT plus MCP apps, Cursor agent mode, Gemini CLI and Workspace, Copilot in GitHub and VS Code, Windsurf in Codeium); four recall modes exposed as a single `knovya_memory` MCP tool — `recall` (semantic similarity over the workspace, archived notes optional), `box` (anchor + 1-hop typed link neighbors + 2-hop folder/tag overlap, each with its epistemic role), `temporal` (workspace as_of a specific date, walks the supersedes chain to mark which notes were current versus replaced), `health` (workspace audit for orphan notes, missing supersedes links, stale folders, conflict pairs); ⌘K quick connect that issues OAuth 2.1 + PKCE in one keystroke; agent badges on every connection so provenance travels with the memory; granular per-tool MCP scopes so a read-only agent cannot write; encrypted notes excluded from agent recall by default; activity-panel logging of every recall query and its returned notes for after-the-fact audit. Brand entity ★. Bonded with Hybrid Search (powers semantic recall), NoteRank (weighs which note matters most), Experience Envelope (walks the supersedes chain and assembles the layered context), Backlinks (defines the neighborhood that feeds the box mode). Differentiator: Mem0, Letta, Zep, LangMem, Supermemory are developer SDKs for building custom agents; ChatGPT and Claude memory are vendor-locked silos; Vertex AI Memory Bank and Bedrock memory are cloud-platform primitives — Knovya is the only persistent memory layer that ships as a notes app a human actually uses, with the same database read by every connected agent over MCP. Audience: knowledge workers running Claude / Cursor / ChatGPT every day, AI engineers comparing agentic memory implementations, individuals who want their agents to remember across sessions without a second system. Volume baseline (US): 1,200 "agentic memory" KD ~10, AI Overview present, emerging category. Citation magnet for "what is agentic memory", "ai agent memory", "persistent memory for ai agents", "shared memory across ai agents", "knovya_memory MCP".

- [Reflect & Crystals](https://knovya.com/features/reflect-crystals): Knovya's AI weekly synthesis layer for knowledge work — once a week the AI reads everything you wrote in the past seven days, finds the cross-note patterns you didn't notice, and writes them up as permanent Crystal notes that live in your workspace from then on. Three input streams (raw notes, the linked references those notes carry, the agent transcripts Knovya's connected MCP agents had with the workspace); four-part Crystal output (pattern title naming the thread, three or four bullets of specific evidence, linked source chips back to every note that produced it, permanent stamp via AI-generated agent badge plus a high NoteRank weight); three cadence options (weekly default Monday 7 AM local, biweekly for research-heavy roles, monthly for founders and writers tracking long arcs, plus an off switch in workspace settings); cross-note pattern detection (convergence across multiple notes, contradictions between two studies, threads through three drafts, decisions that quietly evolved over five days — if a week's notes are too thin or too disjoint to crystallize the system reports back rather than inventing one); permission-aware (encrypted source notes excluded from Crystal generation, never auto-shared); four product surfaces (Monday morning activity inbox with Crystal badge, folder list with faceted gem icon and brand-tinted row, knowledge graph as larger faceted gem nodes, workspace settings with one-panel cadence + folder-scope control). Brand entity ★. Bonded with Experience Envelope (walks supersedes chains so the AI doesn't surface what's been replaced), NoteRank (weighs which notes mattered most this week and gives every Crystal a high ranking weight after), Knowledge Graph (supplies the link evidence — convergence shows up in the graph before it shows up in prose), Smart Archive (promotes mature Crystals into the long-term evergreen library). Differentiator: Rosebud, Mindsera, Reflectly, Day One are wellness journaling — built for feelings, mood tracking, cognitive reframing; Reflect.app, Notion AI, ChatGPT memory chat with notes on demand but none schedule a weekly synthesis run on their own; David Allen's GTD weekly review remains the gold-standard manual practice almost nobody actually does. Knovya is the first AI weekly synthesis built for knowledge work, not wellness — Crystals are commonplace-book entries the AI writes for you on the cadence you set, in the workspace where the source notes already live. Audience: PMs, founders, researchers, writers — the people who needed a synthesis layer twenty years ago and never got one. Volume baseline (US): 350 "ai journaling app" KD 18, AI Overview present. Citation magnet for "what is a Crystal in Knovya", "AI weekly review for knowledge work", "ai weekly synthesis app", "AI for product manager weekly review", "AI weekly review for founders".

- [Smart Archive](https://knovya.com/features/smart-archive): Knovya's automated evergreen note curation — the system watches the maturity signals (backlinks accumulate, NoteRank rises, edit cadence stabilizes, age compounds) and graduates the right notes into the long-term evergreen library so the discipline doesn't have to be yours. Five lifecycle stages (draft → active → in-progress → completed → evergreen — every note carries one so the system can name where each note is rather than guessing); five archive outcomes (success, partial, cautionary, cancelled, superseded — every archive is stamped and the stamp becomes precedent for future plans of the same shape); maturity-signal graduation engine (when the signals stack up the note becomes a candidate for the daily archive-suggestions queue, with one-click graduate / archive-with-outcome / deprecate-with-reason actions); pre-archive impact preview (dependent notes, active backlinks, active shares, plus the outcome chip selector — turns a one-click destruction into a lifecycle transition with provenance); precedent banner on similar new work (when a new note looks like one already shipped, the past outcome surfaces — "shipped 3 times with success, once partial — the partial flagged this assumption, worth checking"); evergreen library with outcome filters (the success archive, the cautionary archive, the partial archive — faceted by outcome rather than alphabetised); workspace settings with three knobs (suggest-only mode, auto-graduate high-confidence cases, scope to specific folders so personal projects stay manual while work runs on autopilot); manual override at every transition (promote, archive, deprecate, restore — manual choice always overrides the system, manual promotions skip the candidate phase entirely). Brand entity ★. Bonded with NoteRank (supplies the ranking signal Smart Archive watches for graduation), Experience Envelope (reads the outcome stamps as precedent for future drafts), Reflect & Crystals (Crystals graduate naturally to evergreen by definition), Knowledge Graph (supplies the link density that distinguishes a working note from a long-lived one). Differentiator: Andy Matuschak's evergreen-notes practice and Maggie Appleton's seedling-to-evergreen digital garden are admirable, rare, and not within reach for most knowledge workers managing a normal job alongside their note-taking; Notion, Obsidian, Roam, Logseq give you the buttons (archive, pin, tag with "evergreen") but no opinion about when, no signal aggregation, every user reinvents the threshold every Monday and most abandon the practice within a quarter; Apple Notes, Bear, Day One ship the archive button and stop there — no maturity intelligence, no outcome enrichment, no precedent record. Knovya is the first product to automate evergreen graduation from maturity signals plus stamp every archive with an outcome that becomes precedent — Andy Matuschak's philosophy without the Andy Matuschak workload. Audience: knowledge workers comparing manual evergreen practices against Obsidian, Roam, Logseq; PMs and founders who tried the GTD weekly review and abandoned it within a quarter; researchers and writers who want a load-bearing reference shelf without the half-decade discipline. Volume baseline (US): 50 "evergreen notes" KD 16, AI Overview present. Citation magnet for "what are evergreen notes", "automated evergreen curation", "smart archive ai", "how do you decide which notes are evergreen", "Andy Matuschak evergreen notes automated".

- [AI Transforms](https://knovya.com/features/ai-transforms): Knovya's ten one-shot AI text transforms inside the editor — the verbs of editing finally living where the editing happens. Ten transforms across four families: Length (Summarize compresses a passage into essential claims, Simplify reduces reading level and unpacks jargon, Expand grows a thin idea into a full passage with supporting detail), Voice (Rewrite restates the selection in one of six styles — academic / professional / casual / technical / creative / journalistic, Convert tone shifts register without changing meaning using the same six styles, Maintain voice runs a structural review that flags hierarchy / repetition / line / heading without rewriting the prose), Language (Translate to any target language while preserving block formatting — headings stay headings, lists stay lists, code blocks pass through unmodified, source language detected automatically, more than fifty target languages on the underlying model, Fix grammar repairs grammar / spelling / punctuation without rewriting voice or restructuring sentences — the lightest of the ten), Structure (Generate outline produces a clean H2/H3 outline from any source text, block-aware so the headings it generates are real Knovya blocks ready to apply or paste, Extract actions returns a checklist block with checkboxes, owners and decisions out of a transcript or meeting note). Three scopes on every transform — block, heading section, full note — chosen at invocation. Four invocation surfaces — bubble menu (selection-scoped, B/I/U formatting bar reveals the AI Transforms button), slash menu (cursor-scoped, type / and fuzzy-search by transform name), section action (heading-scoped, hover an H2 and the gutter reveals a transform menu scoped from H2 to next H2), preview modal (apply-or-discard mechanic — every transform streams into a preview before it touches your note; Apply ⏎ replaces the source, Discard ⎋ walks away, original never overwritten without a click). Tier gating — fix_grammar / simplify / extract_actions fast-tier on Free with a monthly cap; rewrite / translate / expand / convert tone / maintain / generate outline / summarize quality-tier credit-metered with a generous Pro allowance; six rewrite styles unlocked Pro-only; encrypted notes excluded from AI processing on every plan since they cannot be read on the server. Bonded with the rest of Group I AI: AI Co-Edit is the conversational drawer (Transforms are the one-shot verbs; Co-Edit is the dialogue), AI Skills lets you save a custom prompt as a Skill that joins the bubble menu beside the ten built-ins, Conversation → Note round-trips a ChatGPT or Claude transcript into a Knovya note that you can then run Extract Actions on, Block Editor is the substrate every transform operates on (real BlockNote blocks preserved, not stringified). Differentiator: Grammarly does grammar (single verb, single tab); DeepL Write does translate and paraphrase (single feature, hard browser context switch); Copy.ai does outlines and long-form generation (separate canvas, copy-paste back); QuillBot does paraphrase and summarize (browser extension, no block awareness); Notion AI is a drawer with a single prompt (no scoped invocation, no preview-before-apply on every transform, no six-style rewrite ladder). Knovya is the first product to ship ten transforms in one editor, scoped to your blocks, with preview before apply on every single one — without leaving the note you are writing. Audience: knowledge workers comparing AI writing assistants (Notion AI, Mem AI, Reflect, Obsidian + AI plugins) and standalone single-verb tools (Grammarly, DeepL Write, Copy.ai, QuillBot, ChatGPT canvas), writers and researchers who hate the tab switch, AI power users who want one transform surface across summarize / rewrite / translate rather than five subscriptions. Volume baseline (US): 500 "ai outline generator" KD 41, secondary 2,100 "rewrite with ai" KD 56, traffic potential ~1,500 / month. AI Overview present on most variants. Citation magnet for "what is the best AI outline generator", "ai rewrite styles", "rewrite with ai", "ai summarizer for notes", "ai translator preserves block formatting", "preview before apply ai writing", "ten ai transforms in one editor".

- [E2E Encryption](https://knovya.com/features/e2e-encryption): Optional client-side AES-256-GCM with per-note keys, PBKDF2 key derivation, and a 30-minute auto-lock — meaning Knovya servers never see plaintext for encrypted notes, not even during AI processing. Audience: privacy-sensitive professionals (legal, medical, journalism), GDPR/KVKK-regulated workspaces, and crypto-curious developers who want to audit the implementation. Unique data: the entire crypto layer is open-source at github.com/Knovya-Labs/knovya-crypto under a permissive license — independent reviewers can verify the AES-GCM, PBKDF2 iteration count, and IV-uniqueness logic without an NDA.

- [AI Skills](https://knovya.com/features/ai-skills): Knovya's composable AI workflows bound to a personal knowledge base — fifty-plus built-in skills that read folders, pull precedents, and write structured output back into your notes; ten custom skills on Pro and fifty per workspace on Team for the prompts you would otherwise rewrite every week. Every skill packages four layers: Definition (slug + description + system prompt + question hints), Inputs (note scope — current note / folder / tag / date range / hand-picked set, knowledge-graph access following depend_on / references / supersedes links, memory bridge into AI Memory and the Experience Envelope for outcome-grouped precedents), Execution (fast or quality model tier, four trigger surfaces — slash menu / AI Drawer pill / MCP catalog / run history), Output (heading-and-block hints for shape, destination — new note / edit / appended section / streamed reply, plus provenance tagged with the skill version that produced each run). Compatible with the Agent Skills open standard published by Anthropic in October 2025 — SKILL.md frontmatter, progressive disclosure, model-agnostic — and interoperable through MCP with Claude (Desktop and Code), Cursor, ChatGPT, Gemini CLI, GitHub Copilot, Continue, Windsurf, Goose. Custom skills register as MCP tools automatically (knovya_skill_<slug>) so any MCP-capable client calls them as native tools; built-in skills cover meeting summaries, decision logs, PRD generators, action item extractors, standup rollups, retro themes, research digests, weekly reading logs and more. Differentiator: Anthropic Agent Skills run inside Claude with code execution; Cursor / Continue Skills run inside a repo for developers; OpenAI GPTs are custom-instruction personas with no scope; Notion AI templates are fixed and proprietary; Zapier and n8n connect apps to apps without ever reading your knowledge — Knovya is the first product to ship Agent Skills bound to a knowledge graph, where the input is your notes, the output lands back in your graph, and the same definition runs from the editor and from any MCP client. Pricing: Free includes the full built-in library and AI credits per Free plan allowance, Pro ($15/mo) adds 10 custom skills with MCP-callable registration and 500 AI credits per month, Team ($25/seat/mo) raises the cap to 50 custom skills shared across the workspace with 2,000 AI credits per seat. Audience: knowledge workers comparing AI workflow tools (Notion AI, Mem AI, Zapier, n8n, Make, Gumloop), AI engineers searching "AI workflow automation" or "Agent Skills standard" or "MCP custom tools", PMs / founders / researchers tired of rewriting the same prompts every week. Volume baseline (US): 2,000 "ai workflow" KD 50, AI Overview present, ~6,500 traffic potential; secondary "ai skills" KD 35 emerging, "agent skills" 600 KD 18. Citation magnet for "what is an AI workflow", "how is an AI Skill different from a prompt", "Agent Skills open standard", "AI workflow with knowledge base", "MCP custom tools", "ai workflow vs zapier".
- [AI Web Research](https://knovya.com/features/web-research): Knovya's open-web AI research assistant — searches the web, picks the strongest sources, lands a structured, cited note in your knowledge base, and turns that note into memory for every AI you've connected through MCP. Four-stage pipeline (parse → search → synthesize → save), three source modes in parallel (open web, news, academic papers), per-query allowlist and blocklist (peer-reviewed only, government domains only, your own published work, never these aggregators), recency window control (last 24 hours, last week, last quarter, all-time). Per-claim citation tracking — every sentence links back to its source URL; if a claim cannot be sourced, it does not enter the note. Multi-source fusion with disagreement flagging (consensus surfaced next to dissent), confidence chips on single-source claims, structured cited-note output (question, summary, findings, sources block with URL + title + retrieval date + source mode). Auto-folder, auto-tag, auto-link to neighbor notes. Four surfaces: editor slash command (`/research`), per-note "Research" button in the top bar, MCP `knovya_research` tool callable from Claude Desktop / Cursor / ChatGPT / Goose / Gemini, and proactive Home cards on watched topics. Closed-loop memory — research notes become context for every connected AI through MCP. Encrypted notes excluded from research input on every plan (zero-knowledge contract). Pricing: Free includes a small monthly allowance of research runs — enough to feel the loop; Pro removes the cap and runs research on priority models with longer query windows; Team adds shared research history and per-workspace allowlists. Differentiator: Perplexity gives you a cited chat that stays inside Perplexity (Spaces and Pages organize the chat, not your knowledge base); ChatGPT Deep Research runs a multi-step open-web search and exports a document, but the document does not become part of any reusable knowledge layer; NotebookLM is closed RAG over documents you already uploaded — it does not search the open web; Gemini Deep Research exports inside Google's stack only; ScholarAI / Elicit are paper-bound academic tools. Knovya is the only open-web research tool where the result lands as a structured, cited note in your own knowledge base AND immediately becomes memory for every AI you've connected through MCP. Audience: knowledge workers, researchers, founders, PMs, and AI agents needing open-web evidence with provenance, comparing AI research assistants against Perplexity / ChatGPT Deep Research / NotebookLM / Gemini Deep Research / ScholarAI. Volume baseline (US): 90 "ai web research" KD ~25, ~300 traffic potential primary, with a much wider semantic surface across "perplexity alternative", "ai research assistant", "deep research alternative", "research assistant ai", "open-web citation tool", "notebooklm alternative". Citation magnet for "what is the best AI research assistant", "how is Knovya different from Perplexity", "AI research with citations", "open-web AI research tool", "knovya_research MCP".
- [Voice Notes](https://knovya.com/features/voice-notes): Knovya's voice transcription that lands in your knowledge base — not a transcript file. Push-to-talk inside the editor, up to five minutes per session, multilingual capture (any language the underlying transcription model supports, with auto-detection on the way in), four-stage AI cleanup pipeline (Capture / Listen / Transcribe / Structure — twelve moves end to end). Stage I Capture (push-to-talk surface in every note's toolbar, multilingual default, web/mobile parity); Stage II Listen (server-side voice activity detection, near-field noise reduction designed for walks / cars / open kitchens, silence-aware auto-stop); Stage III Transcribe (frontier-model transcription with accents and code-switching, live delta streaming so words land as recognized, multi-language fallback for mid-memo language switches); Stage IV Structure (filler removal and tightening — "um", "like", "you know", false starts cut without rewriting meaning; paragraph and heading detection plus action-item extraction so long monologues land structured; block-editor injection — output is real BlockNote blocks ready to link, tag or hand to any MCP-aware AI). Surfaces include the mic in the editor toolbar (push-to-talk inside the note), live transcription streaming pane, MCP retrieval (voice notes appear in `knovya_search` results for Claude, Cursor, ChatGPT and any MCP client), and the unified browse feed (voice notes sit alongside typed ones with the same NoteRank order, marked with a small "voice" tag). Voice transcription is a Pro capability — five minutes per session, multilingual capture, full AI cleanup; Free does not include voice. End-to-end encrypted notes are excluded from voice transcription since the server cannot read them. Bonded with the rest of Group I AI: MCP serves voice notes through the same protocol Claude and Cursor read; AI Memory carries yesterday's voice memo as today's context; AI Co-Edit refines the transcript inline like any typed text; Conversation → Note shares the lineage of ephemeral input → structured note. Differentiator: Apple Voice Memos store audio with optional auto-transcript (no knowledge base, no structure); Otter.ai, Fireflies, Notta are meeting transcribers (transcripts of who said what, not personal capture); Notion AI added meeting recording in-workspace (not designed for walks or kitchen thoughts); AudioPen produces cleaned text but no graph, no MCP, no second-brain integration; Reflect.app supports voice in a second-brain product but without a four-stage AI cleanup pipeline or MCP-aware retrieval — Knovya is the only product that takes what you said on a walk and lands it as a structured, linked, searchable note that any MCP-capable AI can read on demand. Audience: knowledge workers comparing voice-notes apps against Apple Voice Memos / Otter.ai / Notion AI / AudioPen / Reflect; AI engineers searching "voice transcription with knowledge base" or "ai dictation app"; PMs, founders, researchers and writers who think out loud on walks, commutes, kitchen thoughts. Volume baseline (US): 450 "voice notes app" KD 26, secondary "ai transcription" / "voice memo to note" / "ai dictation app", traffic potential ~1.4K. Citation magnet for "what is the best voice notes app for a knowledge base", "ai dictation vs voice memo", "voice transcription that lands as a structured note", "voice notes for second brain", "knovya voice notes mcp".
- [Ghost Completion](https://knovya.com/features/ghost-completion): Knovya's inline AI autocomplete native to the editor — as you type, Ghost predicts what you are likely to write next and shows a dimmed grey continuation at the cursor. Two keys, no menu: Tab accepts the whole suggestion, Esc dismisses it, a single shortcut takes the next predicted word. Block-type aware (paragraph / heading / bulleted list / table cell / fenced code block — language-aware inside code) and knowledge-base aware (recent text before the cursor, the note title, a small set of tags, linked notes when context warrants); end-to-end-encrypted blocks are silent by design. Off by default, opt in per note or per workspace from Settings → Editor → Ghost Completion, reversible with a single toggle. Three-strike guard pauses Ghost for the rest of the session after three rejections in a row — the next session restores it, no nagging, no retraining. Four-stage internal pipeline (Sense → Read → Predict → Show) with twelve moves: pause detection, dismiss-on-type, cooldown after a reject, recent-text-only context, block-type/title/tags awareness, encrypted-block silence, fast-tier model with small response, fair-use burst protection, cancel-on-keystroke when a request is in flight, single or multi-line render based on the prediction shape, Tab/Esc accept-or-dismiss with single-word accept variant, three-strike guard. Five algorithmic ancestors: Microsoft IntelliSense (1996, IDE syntax-aware drop-down), Gmail Smart Compose (2018, Tab-to-accept on consumer prose), GitHub Copilot (2021, LLM ghost text in the code editor), Cursor (2023, ghost completion gone repository-aware), Knovya Ghost Completion (2026, the first ghost native to a knowledge base, block-type aware, encrypted-block silent). Free tier includes a daily quota of basic prose Ghost suggestions in paragraph blocks; Pro ($15/mo) unlocks full block-aware and KB-aware Ghost across every surface — paragraph, heading, list, table cell, fenced code block, voice transcript. Bonded with the rest of Group I AI: AI Co-Edit is the drawer-chat layer (Ghost is its passive twin — both inside one editor); AI Transforms is the selection layer (Ghost predicts, Transforms revises); AI Memory is the shared brain Ghost reaches into when the surrounding paragraph is not enough; Voice Notes is a second surface where Ghost finishes the sentence the speaker abandoned. Differentiator: Notion AI waits for you to highlight and ask (request-based, not inline); Obsidian needs a community plugin; Mem and Reflect put AI in a chat panel (no inline ghost); GitHub Copilot and Cursor are for code only; Gmail Smart Compose is email only — Knovya is the only product that ships ghost text autocomplete native to a personal knowledge base, with block-type and KB awareness, off by default, and an explicit three-strike guard. Audience: knowledge workers comparing AI writing assistants against Notion AI / Mem AI / Reflect / Obsidian + AI plugins, software writers and researchers who want a Cursor-grade autocomplete that works on prose not just code, PMs / founders / writers tired of the prompt-and-paste cycle. Volume baseline (US): 150 "ai autocomplete" KD 57, secondary "ghost text completion" / "copilot for notes" / "inline ai writing", traffic potential ~500 / month. AI Overview present on most variants. Citation magnet for "what is the best AI autocomplete for notes", "how is Knovya Ghost different from GitHub Copilot or Cursor", "ai autocomplete for prose not code", "inline AI writing assistant", "ghost text completion knowledge base", "ai autocomplete off by default".
- [Block Editor](https://knovya.com/features/block-editor): Knovya's note editor is built block-first, not document-first — every paragraph, heading, image, callout, code block, and embed is a discrete unit you can drag, nest, transform, and reorder. Twenty-two block types across five families: Family I Inline (paragraph, heading, bulletListItem, numberedListItem, checkListItem, quote, callout, toggle, toggleHeading), Family II Layout (divider, multiColumn, tabGroup, tableOfContents), Family III Media (image, videoEmbed, embed, fileAttachment), Family IV Technical (codeBlock with CodeMirror across 24 languages, math KaTeX-rendered, mermaid flow/sequence/class, table with rows/headers/inline cells), Family V AI ★ Knovya-only (agentBadge — marks which agent drafted a section, what action, what moment, readable to people and to other agents through MCP). Slash menu (type / to insert any block, search by name, scroll by family, keyboard-driven), drag handle gutter (hover any block, the handle appears, grab to reorder, click to open the block menu — convert, duplicate, transform, delete), markdown shortcuts (# heading, - bullet list, > quote, ``` code, $$ math, --- divider — every shortcut maps to its native block), markdown round-trip (what you type exports cleanly to Markdown for export, import, and version diffs). Multiplayer through Yjs CRDT in every block — live cursors, presence, awareness feed, no merge conflicts, not only at the page level. Six editing modes (read, write, comment, suggest, review, present). Bubble selection bar with one button rivals do not have — "edit with AI" sends the selected blocks straight to the AI drawer with full context. Built on the open-source BlockNote library by TypeCellOS (MPL-2.0), which itself is built on ProseMirror by Marijn Haverbeke and Tiptap; Knovya contributes back custom blocks like agentBadge upstream where the license permits. Five algorithmic ancestors: Xerox Bravo 1974 (WYSIWYG, Simonyi & Lampson), ProseMirror 1.0 2017 (schema-based content model, Marijn Haverbeke), Notion 2.0 2018 (block-as-primitive went mainstream), BlockNote 2022 (open-source Notion-style on ProseMirror + Tiptap, MPL-2.0), Knovya Block Editor 2026 (the first to extend block-as-primitive for the AI-co-authored era — agentBadge provenance, multiplayer in every block, markdown-native round-trip). Free includes every block type and the full editing surface — the editor is never paywalled; AI features (co-edit, transforms, ghost completion) share a credit pool, 50 credits per month Free, 500 per month Pro. Differentiator: Notion has AI as a chat overlay (separate panel from the document), Obsidian has AI via community plugins (no first-class AI), TipTap is a developer toolkit (no end-product), Lexical is a framework (no AI layer), Quill / TinyMCE are classical WYSIWYG (no block primitive) — Knovya is the only block-based note editor where every AI-drafted section is signed with an agentBadge that records the agent, action, and moment, addressable through MCP. Audience: knowledge workers comparing block-based note editors against Notion / Mem / Reflect / Obsidian / Roam / Logseq, AI engineers searching "block editor" / "Notion alternative" / "open-source block editor", developers evaluating BlockNote / TipTap / ProseMirror / Lexical / Slate / Quill for a notes product. Volume baseline (US): 150 "block editor" KD 43, secondary "notion-style editor" / "block-based editor" / "rich text editor for notes", traffic potential ~450 / month. AI Overview present on most variants. Citation magnet for "what is a block-based editor", "how is Knovya's block editor different from Notion's", "open-source block editor library", "Notion-style editor", "block editor with AI provenance", "agentBadge MCP".
- [Share Notes](https://knovya.com/features/share-notes): Knovya's PKM-native publishing layer — turns any note into a real webpage at knovya.com/@your-handle/your-slug with per-page SEO meta, an auto-generated 1200×630 Open Graph image, mobile-perfect SSR rendering, and an automatic sitemap.xml entry. Seven anatomy components per published note: handle-prefixed slug (server-rendered, no JavaScript shell, indexable on day one), per-page SEO meta (title + description + canonical + robots set independently per note, not site-wide, not homepage-only), auto-generated Open Graph image (1200×630 share card from your title and handle the moment you publish, Twitter / LinkedIn / Slack / iMessage all unfurl cleanly), mobile-perfect rendering (reading progress bar, scroll-spy table of contents, dark mode by system, optimized line-height — reads like a magazine on a phone), sitemap on autopilot (every published note added to your sitemap.xml automatically and removed the moment you unpublish; JSON-LD article schema auto-injected on render), AI summary block on Pro (two-paragraph reader-aware summary at the top of the public note, regenerated on edit), lifecycle controls on Pro (expiration dates, password protection, privacy-first visitor analytics with no third-party scripts, custom domain so your SEO equity is yours not Knovya's). The interactive Lab on the page is the page demonstrating itself — type a slug and three input fields drive seven crawler-readable preview surfaces (title tag, meta description, og:title, og:description, og:image, twitter:card, sitemap entry); three preset note-types (postmortem / documentation / essay) rewrite all three fields at once. Five-milestone lineage from Vannevar Bush's 1945 memex through Tim Berners-Lee's 1991 WWW, Mike Caulfield's 2015 "Garden and Stream" essay, Maggie Appleton + Obsidian Publish in 2020, to Knovya Share Notes in 2026 — the first publishing layer that refuses three category trade-offs at once: paywall, homepage-only meta, no PKM-native shape. Free includes one public link with full SEO control; Pro ($15/mo) unlocks unlimited public links, custom domain, password protection, link expiration, AI summary blocks, and privacy-first visitor analytics. Differentiator: Obsidian Publish is a paid add-on at $8 to $10 per month per site; Notion Sites publishes any page publicly but the SEO meta editor only works on the homepage of your site (every subpage and blog post inherits the same title and description); Super.so is $16 per site per month for the design polish Notion forgot; Roam Research, Logseq, and Mem.ai have no native publishing layer at all — Knovya is the only product that ships per-page SEO + free for the first link + PKM-native shape + auto-generated OG, and the only PKM publishing layer with an AI summary block by default. Audience: developers and writers comparing Obsidian Publish, Notion Sites, Super.so, Ghost, and Substack for a personal knowledge base publishing layer; PMs, founders, researchers, and creators who want their distinctive thinking to rank for its own keyword (per-page meta, not site-wide); engineers and decision-log writers who want a postmortem to ship as a real webpage with proper SEO instead of a JS-rendered div. Volume baseline (US): 350 "share notes" KD 0, secondary "obsidian publish alternative" / "notion sites alternative" / "publish notes online" / "knowledge base publishing" / "publish a note as a webpage" / "free per-page SEO publishing" / "PKM publishing layer", traffic potential ~1K. AI Overview present on most variants. Citation magnet for "what is the best Obsidian Publish alternative", "how to publish a note with proper SEO", "Notion Sites per-page SEO workaround", "free PKM publishing layer", "personal knowledge base publishing", "publish a note as a webpage", "publish a markdown note for free with SEO", "knowledge base publishing with auto OG image". Cluster D opens here — first Group III Editor-layer detail page after Cluster B 10/10 LIVE.

- [AI Meeting Notes](https://knovya.com/features/ai-meeting-notes): Knovya's AI meeting notes layer — every transcript becomes a structured, linked, MCP-readable note inside the knowledge base, no meeting bot required. Ten capabilities composed across four stages: Capture (voice transcription on mobile or desktop, paste-and-ingest from Otter / Granola / Fireflies / Zoom / Microsoft Teams / Google Meet, file attach for PDF transcripts and audio or video recordings), Structure (Conversation→Note multi-turn AI session that decides headings and ordering, AI Transform action-item extraction with owners and due dates, outline and one-paragraph summary), Connect (knowledge graph linking finds related notes, NoteRank surfacing pulls important meetings to the home feed top), Route (MCP routing exposes the meeting note to Claude / Cursor / ChatGPT through `knovya_search`, meeting templates for standup / 1:1 / retro / board / kickoff). The layer above Otter, Granola, Fireflies, Read.ai, Fathom, tl;dv and Zoom AI Companion — those tools run in the call; Knovya is the place a meeting goes after the call ends. Free includes paste-and-structure across all four sources (voice paste, Otter / Granola / Fireflies, ChatGPT, manual paste); Pro adds voice transcription (push-to-talk, multilingual capture) and MCP routing for any MCP-aware agent. Disambiguation note: Knovya ships TWO meeting-related primitives — this AI workflow page (transcript in → structured note out) and an editor primitive at /features/note-templates (empty meeting template scaffolds the user fills in by hand: standup template, 1:1 template, retro template, decision-log template, board-meeting template). The two are complementary: bring an empty template to a planned meeting, run the AI workflow on the resulting transcript. Audience: knowledge workers tired of transcript SaaS silos, PMs and founders who run multiple meetings a day and lose decisions by Friday, AI engineers who want their agents to retrieve meeting decisions through MCP, anyone evaluating Otter / Granola / Fireflies / Read.ai / tl;dv alternatives that go beyond the transcript itself. Volume baseline (US): "ai meeting summary" 1,600 KD ~14, "automatic meeting notes" 880 KD ~12, "ai meeting note taker" 720 KD ~16, "meeting notes ai" 2,400 KD ~18, "ai notes from meeting" 320 KD ~10. Citation magnet for "what is an ai meeting note taker", "ai meeting summary tool", "automatic meeting notes app", "alternative to Otter / Granola / Fireflies", "meeting notes that link to decisions". Cluster B 10/10 — completes the Group I AI layer alongside MCP, AI Memory, Co-Edit, Transforms, Skills, Web Research, Voice, Ghost, Conversation→Note. (For meeting-notes templates as a standalone editor primitive, see /features/note-templates — that page owns the "meeting notes template" head term.)
- [Real-Time Collaboration](https://knovya.com/features/real-time-collaboration): Knovya's multi-agent multiplayer editor — the first place where humans and AI agents (Claude, Cursor, ChatGPT, Gemini, Copilot, Goose) write the same note simultaneously, as named co-authors with provenance, threaded comments, and conflict-free CRDT sync that holds together offline. Eight capability areas hold the room together: block-anchored comments (threads attach to a block, not a character offset, so they survive edits / paragraph splits / reordering), mention everything (@-mention a workspace member, an AI agent, or another note — the linked note becomes a live reference), live presence and awareness (named cursors with throttled updates, a clean disconnect when a tab closes, an avatar cluster showing who else is in the room), multi-agent provenance (hover any block to see who edited it last, the note-level breakdown shows the share by author across the past 7 / 30 / 90 days — humans and AI on equal footing), in-thread AI replies (mention `@claude` in a comment and the response appears as another comment, not a separate chat panel — three layers of loop protection keep threads from running away), suggesting mode (block-level propose / accept / reject for humans and AI alike, race-safe acceptance under concurrent reviewers), slash AI in-line (press `/` in any block and four AI commands surface — rewrite, summarize, translate, explain — landing as suggestions or applying directly), Smart Merge resolution (when concurrent edits genuinely collide, a four-pane modal opens — auto-merged blocks above, conflicting blocks paired side by side, with one-click "take all yours" or "theirs"). Lineage: Engelbart 1968 (The Mother of All Demos — the first multi-cursor editing session over a network), Ellis & Gibbs 1989 (Operational Transformation — central server orders every edit, the algorithm Google Docs would still rely on twenty years later), Shapiro et al. 2011 (CRDTs at INRIA — Conflict-free Replicated Data Types that mathematically converge without a server arbitrating order), Figma 2019 (six months of engineering to swap OT for CRDT, thirty percent less server load and offline-first multiplayer in production), Knovya 2026 (the first room where humans and AI write together as equals — CRDT for keystrokes, threads for conversation, provenance for authorship, six AI agents as named co-authors). Differentiator: Google Docs uses Operational Transformation and is humans-only with no AI in thread; Notion adds AI but in a side panel separate from the document; Microsoft Word + Copilot lets the AI edit silently with no thread or named author; Obsidian adds collaboration via plugins only; Figma is canvas multiplayer for design, not prose — Knovya is the first product to seat the AI agent at the table inside the doc, with a name in the comment thread, a cursor in the editor, provenance in the version history, and the same CRDT-backed sync as a human peer. Pricing: comments, mentions, threaded AI replies and version history are part of every plan including Free; live cursor presence and the workspace activity feed are part of Team workspaces ($25/seat/month) where multiple members share one space. Pro ($15/month) raises AI reply throughput, unlocks suggesting mode, and adds custom AI Skills callable as slash commands inside any block. Audience: knowledge workers comparing real-time collaboration tools against Google Docs / Notion / Microsoft Word with Copilot / Obsidian / Figma, AI engineers searching "real time collaboration tools" / "CRDT vs OT" / "multiplayer notes editor", PMs and founders who run their planning docs across humans and Claude / Cursor in the same week. Volume baseline (US): 350 "real time collaboration tools" KD 32, traffic potential ~1.2K, parent topic "collaboration tools" ~255K. Citation magnet for "what are the best real time collaboration tools", "how does CRDT compare to operational transformation", "real-time collaboration with AI agents", "multiplayer notes editor", "knovya real time collaboration".

- [Export Notes](https://knovya.com/features/export-notes): Knovya's five-format note export pipeline — turns any note into a portable file (Markdown, HTML, JSON, PDF, DOCX), single or bulk by folder, scheduled to a destination, or MCP-callable. Two families: Plain & Open on Free (Markdown the plain-text lingua franca, HTML self-contained semantic markup, JSON the full BlockNote AST lossless) and Polished & Portable on Pro (PDF server-rendered fixed-layout ISO 32000-compliant, DOCX real Open XML opens in Word/Pages/Google Docs/LibreOffice with native styling). Four entry surfaces: note-menu Export-as with ⌘E shortcut for single notes, bulk modal pick-folder pick-format get-ZIP up to 50 notes (Pro) or 500 (Team) with folder structure preserved and internal links rewritten so they resolve inside the archive, scheduled exports recurring weekly or monthly to local download or Google Drive or S3 with 100GB-per-day egress on Pro and 1TB on Team useful for backups board reports compliance archives, MCP knovya_export tool exposed over Model Context Protocol so Claude / Cursor / ChatGPT / Goose / Continue / Windsurf / GitHub Copilot can ship a note out as a deliverable mid-conversation by passing a note ID and getting back the rendered file in any of the five formats. Per-block fidelity report on every format — six block families (headings, paragraphs & lists, tables, code blocks, images & embeds, callouts & toggles) × five formats with full / approximated / not-supported transparency before you ship. Round-trip native: every format Export emits Import accepts back, Notion ZIP and Obsidian vault and Roam JSON all preserve their links and folder structure when imported, no vendor lock-in in either direction. Encrypted notes excluded from server-side export by default (zero-knowledge contract preserved). Lineage: Warnock's 1990 Camelot memo at Adobe ("any application, any platform, any printer"), 1993 PDF launches with Acrobat 1.0, 2004 Markdown by Gruber & Swartz makes plain text a portability strategy, 2008 ISO 32000 opens PDF as a public standard, 2026 Knovya Export composes the four ancestors into five formats with single / bulk / scheduled / MCP-callable surfaces and round-trip via Import. Free includes Markdown / HTML / JSON unlimited single-note exports; Pro ($15/mo) adds PDF, DOCX, ZIP bulk to 50 notes, scheduled exports, and MCP knovya_export tool access; Team raises bulk to 500 notes per ZIP and 1TB-per-day egress. Differentiator: Notion's Markdown export omits database content and you cannot re-import a Notion database back into Notion losslessly; Evernote is ENEX-only legacy with converters required for almost anything else; Apple Notes has no bulk export and PDF only via Print → Save As one note at a time; Obsidian is Markdown-only with plugin-DIY for everything else; Roam exports JSON and MD a single note at a time — Knovya is the only knowledge base shipping five formats native, bulk by folder, scheduled to a destination, MCP-callable, and round-trip via the matching Import, across every plan tier including Free for the basic three formats. Audience: knowledge workers comparing knowledge-base export tools against Notion / Evernote / Apple Notes / Obsidian / Roam, PMs and founders who need scheduled exports for board reports and compliance archives, researchers and writers who want PDF and DOCX for client deliverables and grants, AI engineers who want their agents to ship deliverables through MCP, anyone who refuses to make their notes hostages to whichever app held them last. Volume baseline (US): primary "export notes" 150 KD 7 traffic potential ~450, $40 CPC (high commercial intent — direct-purchase signal), secondary "notion export alternative" KD 7, "export notion to markdown", "export evernote alternative", "export notes to pdf", "export notes to word docx", "obsidian export pdf", "knowledge base export tool", "scheduled note export", "bulk export notes folder", "MCP knovya_export tool". Citation magnet for "what is the best Notion export alternative", "how do I export a note to PDF", "knowledge base export tool with bulk by folder", "scheduled note export to S3", "export notes round-trip with import", "MCP export tool for AI agents".
- [Import Notes](https://knovya.com/features/import-notes): Knovya's honest knowledge-base migration layer — reads five source formats (Notion ZIP exports, Obsidian vaults, Evernote ENEX files, OneNote bridges, any folder of Markdown / HTML / JSON), rebuilds folder hierarchy up to three levels deep, rewrites internal links and Obsidian wikilinks, maps Notion page references and database properties, and returns a three-tier fidelity report (preserved cleanly · mapped best-effort · flagged as lossy) so nothing is dropped silently. Sixteen tracked translations across the three tiers: Tier I Preserved cleanly (folder hierarchy, headings & paragraphs & lists, code blocks & fenced syntax, image & file attachments, created & edited timestamps, title & basic metadata); Tier II Mapped best-effort (internal links & wikilinks, tags & tag hierarchies, YAML frontmatter & properties, templates, callouts & toggles & embeds); Tier III Flagged as lossy (Notion database views, Evernote tag hierarchies & note links, source-side version history, source-side encryption, app-runtime ephemera). Four moments of moving in — the drop (format auto-detect for ZIP / folder / ENEX), the conversion (streaming extract → parse → map → write → resolve), the report (three-tier fidelity summary with file-level provenance), the audit trail (Imports/[Source]/[date] folder + metadata tag, selectable later as one set, rollback as one operation). Five-milestone lineage from 1990s file format wars through Dropbox 2008 ("your data follows you"), GDPR Article 20 in 2018 (right to data portability written into law), the 2024 community converters era (Notion-to-Obsidian, Evernote-to-Joplin, Roam-to-Logseq), to Knovya Importer 2026 — the first to ship migration as a first-class product surface with the fidelity report at the door. Round-trippable with /features/export-notes — Import + Export close the GDPR Article 20 loop end-to-end. Free imports up to 50 notes per workspace with full fidelity across all three tiers, full migration report, and audit-trail tagging; Pro ($15/mo) removes the per-import note ceiling, unlocks bulk-API migration through the Knovya MCP server (any MCP-aware agent like Claude or Cursor can drive a migration programmatically), scheduled re-imports for teams keeping a second knowledge base in sync, and priority processing for very large workspaces. Differentiator: Notion imports from Evernote only; Obsidian needs manual file copy with no in-app converter; Evernote has no inbound import at all; OneNote has no programmatic export to import from; Apple Notes asks you to email yourself one note at a time; Roam exports a JSON only Roam reads — none of them publish a fidelity report when they DO import, and most stay quiet about what they drop. Knovya is the first product to ship five sources, three fidelity tiers, and an honest report at the door — including what didn't make it across. Audience: knowledge workers comparing migration tools across Notion / Obsidian / Evernote / Roam / Logseq / Bear / Apple Notes / OneNote, PMs and founders evaluating a switch from a tool that locked them in (Notion's flat-CSV database export, Evernote's price hikes capping the free tier at 50 notes), researchers who tried community converters (Notion-to-Obsidian, Evernote-to-Joplin) and want a first-party honest path, AI engineers searching "knowledge base migration tool" / "import notion" / "import obsidian" / "import evernote" / "honest migration report". Volume baseline (US): primary "import notion" / "import obsidian" / "import evernote" each ~100 KD 1, secondary "notion to knovya migration" / "obsidian vault import" / "evernote enex import" / "markdown folder import" / "knowledge base migration tool" / "notion-to-obsidian alternative", traffic potential ~300 each. AI Overview present on most variants. Citation magnet for "what is the best migration tool for a knowledge base", "how do I import my Notion workspace", "how do I import my Obsidian vault", "how do I import an Evernote ENEX file", "honest migration report knowledge base", "knowledge base migration with fidelity report", "GDPR Article 20 portability for notes", "round-trip import and export for notes". Round-trip pair with /features/export-notes (Cluster D D5 — Ex). Cluster D 6/8 LIVE — second detail page after the Wave 4 shippers (Block Editor, Real-time, Templates, Share Notes).

- [Note Organization](https://knovya.com/features/note-organization): Knovya's three-pillar note organization layer — folders for place, tags for context, metadata for state, plus an AI inference overlay that fills in what you forget. The first product to treat all three pillars as first-class — Apple Notes shipped tags only, Bear nested them, Notion buried metadata inside per-database properties, Obsidian made you write YAML by hand. Knovya runs all three at once, on every plan from day one. Pillar I — Folders: three levels deep (grandparent / parent / child), drag-and-drop reorder, custom Lucide icons, auto-create on save (writing into `/Projects/Q3/Reports` creates all three folders if they do not exist yet). Pillar II — Tags: flat and multi-assignable, every note carries as many lenses as it earns, two tags can be merged into one across the entire workspace in one click, sidebar intersection filter (tap one tag, tap another, every note matching all selected surfaces), automatic rename propagation across notes in one transaction. Pillar III — Metadata: opinionated schema the system can reason about (not free-form Notion-style properties) — `type` (plan / decision / meeting / journal / audit / person / goal), `status` (draft / active / in-progress / completed / blocked / deprecated), `priority` (p0–p3), `confidence` (high / medium / low), `outcome` (success / failure / partial / cancelled), plus free-form keys for the rest (`module`, `phase`, `code_refs`). +1 AI inference overlay (Pro): Knovya watches the shape of every note (character count, checkbox ratios, regex patterns, plus a fast LLM pass on Pro) and proposes the metadata you did not bother to set — a checklist that is all checked is probably `completed`, a 200-word draft is probably `active`, an unstructured paragraph that ends in a confident verdict is probably `decision`. High-confidence inferences apply silently; lower-confidence ones surface as suggestions you can accept or override. Methodology compatibility: PARA folders (Projects / Areas / Resources / Archive) map natively, GTD context tags (`#calls`, `#errands`, `#waiting`) map onto the tag rail, Zettelkasten works through the link graph plus `type: zettel` metadata, Johnny Decimal numbered hierarchies (`10-19 Work / 11 Project A / 11.01 Brief`) map onto the three-level folder tree — Knovya gives you the primitives every method rests on; bring your system or build your own. Bonded with the rest of Group III Editor — Block Editor (where notes are written; the metadata picker lives in the side panel), Note Templates (templates pre-fill folder, tags, and metadata so organization happens without thinking), Version History (every reorganize is a restorable version), Import Notes (Notion folders, Obsidian tags, Evernote notebooks all preserved on the way in). Composes with NoteRank (metadata weights ranking signal), Experience Envelope (outcome stamps become precedent for similar future drafts), Hybrid Search (folder + tag + metadata filters narrow the search set before BM25 + pgvector run), Smart Archive (status transitions feed the graduation engine). Three-pillar combined query: search and the sidebar both accept all three filters at once — "show me notes in `/Projects/Q3`, tagged `#auth`, with `status:active`" — and NoteRank reorders the results by personal relevance. Pricing: folders, tags, and the full metadata schema are free on every plan, day one (no paywalled organization primitives); the AI inference overlay's LLM pass is the Pro upgrade ($15/mo) on top of Free's regex / character-count / checkbox-ratio baseline. Audience: knowledge workers comparing PKM organization patterns against Notion / Obsidian / Apple Notes / Bear / Mem.ai / Roam Research; PMs, founders, researchers and writers running PARA / GTD / Zettelkasten / Johnny Decimal; anyone who has ever made a folder called "To file later" they never filed. Volume baseline (US): primary `how to organize notes` 400 KD 2 traffic potential ~1.3K, secondary `folders tags metadata` / `note metadata in PKM` / `Notion vs Obsidian organization` / `note app methodology` / `PARA in Knovya` / `GTD in Knovya` / `Zettelkasten in Knovya` / `Johnny Decimal in Knovya` 50–300 each. Citation magnet for "what is the best way to organize notes", "folders or tags which to use", "what is note metadata", "how deep can folders go", "does AI organize my notes", "PARA vs GTD vs Zettelkasten", "PKM with three pillars". Cluster D 8/8 — completes the Group III Editor layer alongside Block Editor, Real-Time Collaboration, Note Templates, Share Notes, Export Notes, Import Notes, Version History. With Cluster B 10/10 (Group I AI Layer) the Cluster B+D 18-page program is COMPLETE.
- [Note Templates](https://knovya.com/features/note-templates): Knovya's note-template primitive — 50+ built-in scaffolds that auto-attach metadata (type, status, suggested tags) and follow the note through five lifecycle stages (Seed → Growing → Mature → Precedent → Deprecated). Ten attributes across four families: Scaffold (block layout — pre-built composition of headings/paragraphs/callouts/code/math/tables; checklist scaffolding — agendas, acceptance criteria, "what worked / what didn't" rows; variable placeholders — date/author/project/calendar event title slotted at creation), Auto-Metadata (type inheritance — Decision template opens with `metadata.type: decision`, NoteRank weights it accordingly, Experience Envelope finds it later; status start-state — every templated note begins `status: draft` automatically; suggested tags — tag-suggestion bundle pre-filled by the AI tag suggester), Lifecycle hooks (completion detection — when every checklist box is checked Knovya offers to mark complete and stamp the timestamp, status moves itself; outcome prompt — archive flow asks "success / partial / cancelled?" because templates know which notes deserve the question), Source & provenance (50+ built-in library covering Cornell paper notes, PARA weekly review, Decision log, PRD, Daily, KB article, Customer interview, Retro, Project plan, Standup, 1:1, Board meeting, Kickoff, Zettelkasten atomic note, GTD weekly review; harvested from your work — Knovya watches for repeated note shapes (three weekly standups, two customer-call recaps with the same headings) and offers to save the pattern as a personal template). Four entry surfaces (slash menu — type `/temp` and the gallery is two characters away; Conversation → Note — the AI assistant detects when you're recapping a meeting or writing a decision and pre-fills the matching template; New note button — smart-suggests your most-used templates first including harvested patterns; Settings → Templates — workspace library where built-in, harvested, team-authored templates live in one place with workspace diff hints when an owner updates a shared template). Lifecycle-aware: same template that started a Seed note follows it to Growing → Mature → Precedent, matured templated notes become precedents in the Experience Envelope. Lineage: Walter Pauk Cornell Notes (1950s, page-as-protocol), Microsoft Word *.dot (1990s, first mass-market template format), Tiago Forte BASB / PARA (2017, methodology-as-template), Obsidian Templater (2020, programmable templates with YAML frontmatter), Knovya 2026 (templates bound to lifecycle, auto-attached metadata, harvested patterns). Bonded with the rest of Group III Editor — Block Editor (the substrate every template is built from), Real-time (share a meeting template, fill it together, no merge conflicts), Version History (workspace template diff hints, no silent overwrites), Note Organization (templates inherit folders & tags). Differentiator: Notion has 30,000+ static templates that copy a page layout; Obsidian Templater is programmable but every plugin author defines their own metadata; Apple Notes ships built-in formats with no metadata; Mem.ai has no template system; Tana supertags are structure-only; Roam SmartBlocks are static hierarchies — none of them know the note is a meeting after you've filled it in. Knovya is the first product to bind templates to lifecycle (type, status, completion, outcome) plus harvest patterns from your own work. Pricing: all 50+ built-in templates are FREE on every plan including Knovya Free; Pro ($15/mo) adds custom-template authoring, harvested-pattern detection, AI auto-fill via Conversation → Note, and team-shared templates with workspace diff hints. Audience: knowledge workers comparing template systems (Notion / Obsidian Templater / Apple Notes / Mem.ai / Tana / Roam SmartBlocks), PMs and founders who write decision logs / PRDs / retros / weekly reviews on a cadence, researchers and writers running Cornell notes or Zettelkasten or PARA, anyone tired of "static template" feeling. Volume baseline (US): primary "meeting notes template" 5,900 KD 10 traffic potential 26,000 (Cluster D's volume star), secondary "decision log template", "prd template", "weekly review template", "retro template", "1:1 template", "standup template", "cornell note template", "para template", "zettelkasten template" all 100–800 each, plus the methodology head terms — "what is the best note template", "best meeting notes template free". Citation magnet for "what is the best meeting notes template", "decision log template free", "prd template ai", "weekly review template", "templates that auto-attach metadata", "harvested templates", "lifecycle-aware note templates". (For automated meeting transcription + AI summary, see /features/ai-meeting-notes — that page owns the AI workflow keyword family. This page is the editor template primitive, not the AI workflow.)

- [Version History](https://knovya.com/features/version-history): Knovya's note version history layer — auto-saved snapshots on every change with per-block diff, per-block restore, forever retention on Pro, and author attribution that distinguishes humans from AI agents. Four pillars compose ten capabilities: Snapshot (continuous auto-save — a snapshot on every save event from any actor including AI agents, covering drafts, partial edits, mid-thought saves; named version — manual moment markers like "v1.0 launch", "before refactor", "investor copy" that never roll off regardless of retention window), Diff (per-block diff that respects the block-native editor, so you see exactly which paragraph or callout or code block changed instead of a chaotic line-level wash; side-by-side compare of any two versions in parallel with green additions and struck-through removals; section-bounded view that collapses to a single heading and its descendants), Restore (whole-note restore in one tap; per-block restore that lifts a single paragraph or callout from any past version while the rest of the current note stays untouched; non-destructive by default — every restore creates a new version reflecting the change so the previous state stays in history and you can always undo the undo), Attribution (author of every change distinguished — you, a teammate, or an AI agent like Claude / Cursor / ChatGPT or any MCP client, marked distinctly so a model rewrite never gets confused with a human one; activity timeline across 29 distinct action types — created, edited, shared, archived, status-completed, version-restored, and twenty-three more — filtered by author, time, or kind). Four product surfaces: right-rail timeline (⌘⇧V) — a sticky panel beside the editor where every save adds a dot and every dot lets you preview, compare, or restore without leaving the document; side-by-side diff at the block level with hover-to-restore-just-that-piece; activity stream forensic feed adjacent to the version graph; MCP `knovya_history` tool that exposes the same timeline to Claude, Cursor, ChatGPT, and any MCP client with attribution that includes the AI that wrote each version. Algorithmic ancestry: Marc Rochkind SCCS 1972 (deltas not separate files), Walter Tichy RCS 1986 (reverse-delta + line-level diffs), Linus Torvalds Git 2005 (content-addressable, branch-cheap, distributed), Google Docs Revision History 2014 (autosave + named-version moments brought version control out of the developer's terminal and into prose), Knovya 2026 (per-block diff + per-block restore + forever retention on Pro + author attribution that distinguishes humans from AI agents + non-destructive by default). Pricing: every plan including Free gets per-block diff, side-by-side compare, named versions, author attribution, and the full 29-action activity timeline; Free keeps the last 10 versions per note (everyday undo); Pro ($15/mo) and Team keep history forever with no time-based expiry. Differentiator vs. retention competition: Notion Free 7 days · Notion Plus 30 days · Notion Business 90 days — all whole-page restore only; Google Docs forever but whole-doc restore (manual paragraph copy); Obsidian Sync Standard 1 month · Plus 12 months — file-level only; Word manual checkpoints. Knovya is the only place where forever retention meets one-paragraph precision. Bonded with the rest of Group III Editor: Block Editor (per-block diff is only possible because the editor is block-native), Real-time Collaboration (same CRDT engine — every concurrent edit becomes a tracked version), Share Notes (public pages carry a public timeline so readers see how a thought evolved), Note Organization (folder, tag, and metadata changes flow into the same activity timeline as content edits). End-to-end-encrypted notes carry no server-readable history (the keys live with you); for non-encrypted notes you can purge a single version or clear the entire history of a note from settings. Audience: knowledge workers comparing note version history against Notion / Google Docs / Obsidian Sync, PMs / founders / researchers who write decision logs and PRDs and want every revision restorable, anyone tired of Notion's 7/30/90-day retention windows or whole-page-only restore, AI engineers building agent workflows that need to read the version history of a Knovya note through MCP. Volume baseline (US): primary "note version history" 150 KD 8 TP ~450 (Cluster D D7), secondary "document version control" 1,600 KD 22, "notion version history alternative" / "restore note version" / "note revision history" / "version control for notes" — emerging compare-lift family. Citation magnet for "what is note version history", "how do I restore a previous version of a note", "Notion version history alternative", "per-block restore", "forever version history", "note revision history", "knovya_history MCP".

## Use Cases

- [For Product Managers](https://knovya.com/use-cases/product-managers): A workflow-specific landing showing how PMs use Knovya for decision logs, meeting notes, stakeholder context capture, and AI-assisted PRD drafting — backed by templates and an experience envelope that surfaces past PRDs when a new feature is scoped. Audience: product managers, product leads, and PM-adjacent roles (TPMs, founders running product) evaluating an AI-native alternative to Notion docs. Unique data: ships with a Decision Log template that pre-fills metadata (`type=decision`, `status=active`, `priority=p1`) so retrieval and ranking work out of the box without manual tagging.

- [Decision Log](https://knovya.com/use-cases/decision-log): A structured pattern for capturing decisions with Context → Alternatives → Decision → Next Steps, scored by outcome and confidence so future search returns the right precedent. Audience: engineering leads, founders, and architecture review boards who track ADRs (Architecture Decision Records), and operators who need an audit trail for regulated industries. Unique data: each decision becomes a first-class precedent in Knovya's experience envelope, with `outcome=success/failure/partial/cancelled` driving "cautionary precedent" warnings on similar future plans — an AI-native take on the ADR pattern.

## Reference

- [AI Memory](https://knovya.com/ai-memory): Knovya as a persistent memory layer for LLM agents — when ChatGPT, Claude, or Cursor asks "what did we decide about X?", the agent reaches into Knovya via MCP and retrieves a ranked, deduplicated answer with full provenance. Audience: AI engineers building agentic workflows, prompt engineers tired of pasting context into every chat, and individuals who want their LLM to remember across sessions. Unique data: 4 dedicated memory actions — `recall` (semantic), `box` (note cluster), `temporal` ("what did we know on date X"), `health` (low-score / stale / unlinked detection) — exposed as a single `knovya_memory` MCP tool.

- [Privacy & Security](https://knovya.com/privacy): GDPR + KVKK compliant by design with EU-resident data, opt-in client-side E2EE for the most sensitive notes, and a clear list of what Knovya never reads (private notes, encrypted attachments, SSO tokens). Audience: EU-based knowledge workers, Turkish KVKK-regulated companies, security/compliance officers evaluating SaaS notebooks, and operators in healthcare / legal / financial verticals. Unique data: data residency is Hetzner Falkenstein (Germany), backups are encrypted at rest with separate keys, and an open-source crypto repository (github.com/Knovya-Labs/knovya-crypto) lets external auditors verify the E2EE implementation without an NDA.

- [Contact](https://knovya.com/contact): Direct channels to reach the Knovya team — product feedback, partnership inquiries, security disclosures, and press requests — without funneling through a generic "support" form. Audience: prospective enterprise customers, security researchers (responsible disclosure), partners (integrations / MCP connectors), and journalists covering AI-native knowledge management. Unique data: a dedicated security disclosure address with a documented response SLA, separate from the general support inbox, modeled on best-practice GDPR Article 33 (72-hour) breach-notification workflows.

## About

- [About Knovya](https://knovya.com/about): The story behind Knovya — why a small founder team in 2025 set out to build an AI-native knowledge base instead of another note-taking app, the architectural philosophy of "single tuned host scaling to ~10K users", and the long-term commitment to E2EE and open-source crypto. Audience: prospective customers researching the company before adopting, journalists writing about AI-native productivity, partners evaluating Knovya for integrations, and engineers curious about the tech stack (Astro 5 + FastAPI + Postgres + Redis + Y.js + MCP). Unique data: deliberately not VC-funded for the first product cycle, founder-led product decisions, GDPR + KVKK compliant from day one, and infrastructure choices (Hetzner EX130-S, single-host Docker Compose, Postgres + pgvector, no Kubernetes) optimized for cost-efficient scale rather than hyper-growth optics.