# Falconer

> Falconer is a self-updating knowledge platform that provides humans and agents with a long-term memory system. It connects to the tools your team already uses, builds a unified knowledge base, and gives you an AI agent to search, write, and keep documentation up to date.

## Docs

- [Ask questions in Slack](https://falconer.com/docs/ask/ask-questions-in-slack.md): Ask Falcon questions directly in Slack to get instant answers from your knowledge base -- without leaving your conversation.
- [Capture decisions in Slack](https://falconer.com/docs/ask/capture-decisions-in-slack.md): Important decisions happen in Slack, then vanish. Use @Falcon remember to save any fact, decision, or context the moment it happens, right from Slack.
- [Falconer vs AI chatbots](https://falconer.com/docs/ask/falconer-vs-ai-chatbots.md): Falconer and general-purpose AI assistants like Claude or ChatGPT are complementary tools. Here's how to think about when to use each.
- [Find answers with Falconer](https://falconer.com/docs/ask/find-answers-with-falconer.md): Use Falconer to instantly find answers across your codebase, docs, tasks, and conversations.
- [Personalize the agent](https://falconer.com/docs/ask/personalize-the-agent.md): Tailor every Falconer answer to your role, team, and preferences with Agent Personalization.
- [Use voice mode](https://falconer.com/docs/ask/use-voice-mode.md): Use voice mode to speak your prompts instead of typing them. Try it when you want to draft docs hands-free or describe something faster than you can type it.
- [Generate docs automatically](https://falconer.com/docs/get-started/generate-docs-automatically.md): Falconer Generate creates a complete set of documentation from your codebase -- so you're not starting from a blank page.
- [How Falconer works](https://falconer.com/docs/get-started/how-falconer-works.md): Falconer is a knowledge platform for engineering teams. It connects to the tools your team already uses, builds a unified knowledge base from them, and gives you an AI agent -- Falcon -- to search, write, and keep documentation up to date.
- [Falconer CLI](https://falconer.com/docs/mcp-and-cli/cli.md): Install the Falconer CLI for shell scripts and direct document operations.
- [Connect MCP](https://falconer.com/docs/mcp-and-cli/connect.md): Connect Falconer MCP to Claude.ai, Claude Code, Codex CLI, or Cursor.
- [Quickstart with MCP](https://falconer.com/docs/mcp-and-cli/quickstart.md): Connect Falconer MCP to your AI client, sign in with OAuth, and verify that your client can reach your Falconer documents.
- [MCP tools reference](https://falconer.com/docs/mcp-and-cli/tools-reference.md): Review the Falconer MCP tools for documents, comments, navigation, permissions, and media.
- [Organize docs](https://falconer.com/docs/organize/organize-docs.md): Falconer Organize analyzes your Company documents and proposes a reorganized structure with new folders, categories, and architecture.
- [Quickstart](https://falconer.com/docs/quickstart.md): Connect your first source to Falconer and ask questions or write a doc about your codebase.
- [Allowlist a domain](https://falconer.com/docs/set-up/allowlist-a-domain.md): Use the domain allowlist to allow teammates in your organization to log in with SSO.
- [Connect sources](https://falconer.com/docs/set-up/connect-sources.md): Connect your tools to Falconer to enable code-aware search, AI-powered documentation.
- [Invite teammates and manage permissions](https://falconer.com/docs/set-up/invite-teammates-and-manage-permissions.md): Get your team into Falconer and control what they can see and edit.
- [Automatically update documents](https://falconer.com/docs/update/auto-update-docs.md): Turn on auto-update for any document and Falconer will keep it current, automatically. When a pull request merges, Falconer reads the diff, finds which docs are affected, and proposes changes for your review.
- [Edit a doc with inline AI](https://falconer.com/docs/update/edit-a-doc-with-inline-ai.md): Use inline AI to make targeted edits to specific sections instead of regenerating an entire page.
- [Find and fix outdated docs](https://falconer.com/docs/update/find-and-fix-outdated-docs.md): Ask Falcon to audit your documentation for staleness -- from inside a doc or from the homepage -- and get a prioritized list of what needs updating.
- [Keep docs in sync with PRs](https://falconer.com/docs/update/keep-docs-in-sync-with-prs.md): Get notified when pull requests impact your docs. Falconer automatically detects when a merged pull request impacts your existing documentation and proposes targeted updates.
- [Update docs from Slack](https://falconer.com/docs/update/update-docs-from-slack.md): Update your Falconer docs directly from Slack conversations.
- [Create Linear issues from Falconer](https://falconer.com/docs/use-cases/automate-workflows/create-linear-issues-from-falconer.md): Create Linear issues from Slack conversation or Falconer docs.
- [Sync Granola meeting notes](https://falconer.com/docs/use-cases/automate-workflows/sync-granola-meeting-notes.md): Connecting Granola syncs your meeting notes into Falconer, making them searchable alongside your docs, code, and conversations.
- [Talk to the Falconer agent like a teammate](https://falconer.com/docs/use-cases/automate-workflows/talk-to-the-falconer-agent-like-a-teammate.md): Get answers and capture your knowledge in plain conversation -- no prompts required.
- [Update docs when code changes](https://falconer.com/docs/use-cases/automate-workflows/update-docs-when-code-changes.md): Automatically keep documentation in sync with your codebase as your team ships.
- [Create a skill doc](https://falconer.com/docs/use-cases/generate-documents/create-a-skill-doc.md): Turn a Falconer document into a reusable skill that gives Falcon instructions for repeatable work.
- [Create an onboarding guide](https://falconer.com/docs/use-cases/generate-documents/create-an-onboarding-guide.md): Create role-specific onboarding guides for new hires using existing context.
- [Create architecture overviews](https://falconer.com/docs/use-cases/generate-documents/create-architecture-overviews.md): Ask Falconer to explain how any part of your codebase works -- from high-level system architecture down to how a specific feature is implemented.
- [Generate a weekly team report](https://falconer.com/docs/use-cases/generate-documents/generate-a-weekly-team-report.md): Summarize what your team shipped, what's in progress, and what's blocked in minutes.
- [Generate an API reference from code](https://falconer.com/docs/use-cases/generate-documents/generate-an-api-reference-from-code.md): Turn your codebase into a structured API reference. Falconer reads your connected repositories and generates documentation for your endpoints, functions, parameters, and return types.
- [Write a changelog](https://falconer.com/docs/use-cases/generate-documents/write-a-changelog.md): Generate a changelog using context from your connected sources.
- [Write a runbook](https://falconer.com/docs/use-cases/generate-documents/write-a-runbook.md): Turn your codebase, Slack threads, and notes into step-by-step runbooks ready for the next incident or deployment.
- [Choose inherited or custom permissions](https://falconer.com/docs/use-cases/manage-permissions/choose-inherited-custom-permissions.md): Decide whether a doc or folder should follow its parent permissions or keep its own access settings.
- [Manage folder access](https://falconer.com/docs/use-cases/manage-permissions/manage-folder-access.md): Use folder permissions to manage access for groups of related docs.
- [Plan doc access](https://falconer.com/docs/use-cases/manage-permissions/plan-doc-access.md): Plan who should be able to view, comment on, edit, and share docs before you update permissions.
- [Share a doc](https://falconer.com/docs/use-cases/manage-permissions/share-a-doc.md): Give the right people access to one doc, manage authors, and control link access.
- [Write a doc from Slack](https://falconer.com/docs/write/write-a-doc-from-slack.md): Turn any Slack conversation into a structured Falconer document.
- [Write a doc](https://falconer.com/docs/write/write-a-doc.md): Write with AI, with your sources as context, or start from a blank slate.

## Notes

- [Agent Personalization: an agent that knows you](https://falconer.com/notes/agent-personalization.md): Falconer now tailors every response to who you are. Agent Personalization builds a lightweight, transparent profile of your role, team, and preferences — fully editable, with every attribute traced back to its source.
- [Your context is more than training data](https://falconer.com/notes/context-more-than-training-data.md): Everyone has access to the same frontier models. Your competitive advantage is institutional context — the decisions, tradeoffs, and battle scars inside your four walls. Here's why curating that context is now existential.
- [Falconer agent now speaks git](https://falconer.com/notes/falconer-agent-now-speaks-git.md): The Falconer agent can now answer questions about your code's history: who wrote what, when, and why. It calls git directly against your connected repos to do code archaeology, release diffs, regression hunts, and ownership lookups, all in a chat.
- [Falconer Generate: from repo to doc set in minutes](https://falconer.com/notes/falconer-generate-from-repo-to-doc-set-in-minutes.md): Falconer Generate turns a connected GitHub repo into a structured documentation set, helping teams get documentation started faster.
- [Falconer Update: Full self-driving docs](https://falconer.com/notes/falconer-update-self-driving-docs.md): Falconer Update keeps your documentation in sync with your codebase automatically. Toggle it on for any document and choose Review mode for human-in-the-loop edits, or Full Self-Driving mode to let Falconer handle it entirely.
- [How to generate a changelog in 10 seconds](https://falconer.com/notes/generate-changelog.md): Changelogs used to take hours each week to assemble across GitHub, Linear, Slack, and meeting notes. Falconer turns the same week of context into a changelog in seconds — already cited, audience-aware, and ready to send.
- [Ditching the agent sandbox for AWS S3 Files](https://falconer.com/notes/how-falconer-powers-agents-with-aws-s3-files.md): How we gave the Falconer agent git access (log, blame, diff between arbitrary refs) by mounting a shared NFS filesystem backed by S3 Files across our ingest and UI services. A walkthrough of the storage choice, the Pulumi quirks, and how we made the repo sync robust and reliable.
- [How others build agent memory, and what I took from each](https://falconer.com/notes/how-others-build-agent-memory.md): ChatGPT, Claude Code, and Letta have each built production memory systems for AI agents. Looking at the differences shaped how I built agent signals for Falconer.
- [Knowledge Health: observability for your knowledge base](https://falconer.com/notes/knowledge-health.md): Every company runs on written knowledge, and almost none of them know how healthy theirs is. Knowledge Health puts a single, live score on it and shows you exactly what's dragging it down: contradictions, stale docs, coverage gaps, and redundancies.
- [The source of truth for high-speed teams](https://falconer.com/notes/launch.md): Our mission is to capture all of your important context, keep it up to date, and make it easy for you to deploy it wherever you want: your teammates, your customers, your coding agents.
- [LLM-as-a-Courtroom](https://falconer.com/notes/llm-as-a-courtroom.md): How we built a multi-agent courtroom simulation to decide when code changes require documentation updates—and why the legal system is humanity's best framework for binary decisions under uncertainty.
- [Falconer May 2026 changelog](https://falconer.com/notes/may-2026-changelog.md): Falconer's May 2026 changelog covers six new editor block types, agent memory upgrades, expanded MCP/API write operations, Slack and Granola integration improvements, and dozens of fixes.
- [Rethinking data ingestion as a DAG](https://falconer.com/notes/rethinking-data-ingestion-dag.md): How we reduced data ingestion time from hours to minutes by reimagining our pipeline as a directed acyclic graph. This post covers the architectural shift from async workflows to job queues, the migration strategy we used to preserve behavior, and the observability patterns that helped us identify and isolate bottlenecks at scale.
- [Skill documents: shared team instructions for every AI tool](https://falconer.com/notes/skill-documents.md): Falconer Skill documents let teams create, share, manage, and reuse AI instructions across Falconer and MCP-connected tools like Claude, Cursor, and Codex.
- [Stop freeloading off open source](https://falconer.com/notes/stop-freeloading-open-source.md): Most teams quietly work around the rough edges of the open source libraries they depend on. We decided to fix them at the source instead, contributing patches upstream to Tiptap, the framework behind Falconer's editor.

## Guides

- [Why your AI agents burn tokens hunting for answers they should already have (May 2026)](https://falconer.com/guides/ai-agent-token-waste.md): An agent spent 80,000 tokens to answer a question that existed in one PR. Microsoft Research found agent costs vary by 30x on the same task — almost entirely determined by context quality. Learn the four mechanisms behind token waste and how a knowledge layer cuts retrieval costs by an order of magnitude.
- [Documentation tools that AI coding assistants can actually use (May 2026)](https://falconer.com/guides/ai-coding-assistant-documentation.md): AI coding assistants like Cursor and Claude Code are only as good as the documentation they can retrieve. This guide covers the four properties (atomic chunking, passage-level retrieval, freshness, and a programmatic interface like MCP) that determine which documentation platforms agents can actually use, with a comparison of Falconer, Mintlify, GitBook, Notion, Confluence, and SharePoint.
- [How to use AI for documentation: a practical guide for engineering leaders (May 2026)](https://falconer.com/guides/ai-documentation-generation-maintenance.md): AI doc tools split into two camps: generation (writes faster) and maintenance (keeps docs accurate over time). Most teams conflate them and end up disappointed. Learn the difference, why 61% of developers find AI output unreliable without org context, and how auto-updating systems solve the harder half.
- [Best AI documentation tools for engineering teams (April 2026)](https://falconer.com/guides/ai-documentation-tools.md): AI documentation tools that auto-update when code changes solve the real bottleneck — not search over stale content. We tested five platforms to find which ones genuinely keep docs current, integrate with your stack, and feed accurate context to coding agents.
- [AI FinOps for engineering teams: where token waste actually comes from (May 2026)](https://falconer.com/guides/ai-finops-engineering-teams.md): AI spend spans four sources of token waste: context quality, gateway gaps, observability gaps, and recursive workflows. Context quality is the one most teams miss — and it compounds with everything else. Learn the full framework and why a knowledge layer is the load-bearing fix.
- [Best AI-powered knowledge bases for engineering teams (April 2026)](https://falconer.com/guides/ai-knowledge-bases-engineering.md): Your team's knowledge lives in the codebase, Slack threads, and someone's head. We reviewed AI knowledge bases that connect those sources and keep everything current as code changes, ranking them on auto-updating, AI quality, integrations, and security.
- [Top automated documentation tools for engineering teams (May 2026)](https://falconer.com/guides/automated-documentation-tools.md): AI can generate docs in minutes — keeping them accurate after every merged PR is the harder problem. We scored automated documentation tools on both generation and maintenance, weighting maintenance more heavily, because that's what decides whether your docs still tell the truth six months in.
- [How to give Claude Code context: CLAUDE.md, skills, MCP, and keeping it current](https://falconer.com/guides/claude-code-context.md): Claude Code's official docs cover installation and configuration but leave the harder problem to you: feeding the agent accurate context about your architecture and decisions, and keeping that context current as your codebase changes. This guide covers the three native context mechanisms (CLAUDE.md, skills, and MCP) and how Falconer's MCP integration closes the maintenance gap with a knowledge base that auto-updates from merged PRs.
- [How to build a closed loop company](https://falconer.com/guides/closed-loop-company.md): Most companies lose institutional knowledge faster than they can write it down. Learn what a closed loop company is, why most companies aren't one, and how to build one that keeps context current automatically.
- [Coda reviews, pricing, and alternatives (April 2026)](https://falconer.com/guides/coda-alternatives.md): Read Coda reviews, compare pricing, and find better alternatives for engineering teams in April 2026. See how Falconer keeps docs synced with code changes.
- [What is a company brain? Why it's engineering's next competitive moat](https://falconer.com/guides/company-brain-competitive-moat.md): AI agents fail not because models are weak, but because they lack organizational context. A company brain is the living knowledge layer that fixes this — capturing architecture decisions, codebase context, and team conventions, then keeping it all current and queryable by humans and agents alike.
- [How to build a company brain that connects your entire engineering stack: GitHub, Slack, Linear, and Notion](https://falconer.com/guides/company-brain-engineering-stack.md): A company brain for engineering teams ingests GitHub, Slack, Linear, Notion, and the rest of your stack into a single knowledge graph that stays current as your codebase changes. Learn the four properties it needs and why most attempts fail at update.
- [What is a company knowledge base and why does it break at scale? (May 2026)](https://falconer.com/guides/company-knowledge-base.md): Company knowledge decays within 30-90 days of publication as code ships and systems evolve. Learn the five signals of knowledge health that traditional systems miss, why AI agents make stale docs dramatically more dangerous, and how self-updating systems close the maintenance loop automatically.
- [8 best Confluence alternatives for developer knowledge management](https://falconer.com/guides/confluence-alternatives-developers.md): Confluence wasn't built for code-first documentation, and the maintenance burden compounds as teams scale. This guide compares 8 alternatives for developer knowledge management — GitBook, Notion, Slab, BookStack, Wiki.js, DokuWiki, Document360, and Falconer — on Git integration, code-to-docs sync, version control, and total cost.
- [Confluence reviews, pricing, and alternatives (April 2026)](https://falconer.com/guides/confluence-alternatives.md): Confluence requires manual doc updates that go stale fast. Compare the best Confluence alternatives that auto-update documentation when code changes and provide accurate context to AI coding agents.
- [How to consolidate documentation into one source of truth for engineering teams](https://falconer.com/guides/consolidate-documentation.md): Most documentation consolidation projects stall because the new tool stops being accurate within a few months. This guide walks through why scattered docs are expensive, what a real single source of truth requires, and how to audit, migrate, and govern a consolidation without stalling — with Falconer's auto-updating knowledge base as the maintenance layer.
- [Context engineering vs prompt engineering: why your agents need company knowledge](https://falconer.com/guides/context-engineering-prompt-engineering.md): Prompt engineering controls how you ask the question; context engineering controls what your agent knows before the question gets asked. This guide explains why agents fail with good prompts but no company context, how MCP makes context reusable across models, why knowledge graphs beat vector search for multi-step reasoning, and how Falconer turns scattered company knowledge into agent infrastructure that compounds.
- [How to build a context graph for your organization: a guide for May 2026](https://falconer.com/guides/context-graph-engineering.md): Your team generates context every day through PRs, Slack decisions, and tickets — but nothing connects code to the decisions behind it. Learn how to build a context graph that maps your organization's knowledge and keeps relationships current as code changes.
- [Context retries: the silent token tax on agentic workflows (May 2026)](https://falconer.com/guides/context-retries-token-tax.md): Three compounding effects drive most agent input-token spend: conversation history bloat, tool definition overhead, and retrieval thrash. The third is the silent one — and usually the largest. Learn how each compounds, where the 30x cost variance comes from, and how a knowledge layer cuts the loop short.
- [Context sovereignty: why Atlassian's new data policy is a problem (April 2026)](https://falconer.com/guides/context-sovereignty-atlassian-data-policy.md): Atlassian will train AI models on your Confluence data starting August 17, 2026. Learn why organizational context is your most valuable non-commoditized asset and how to migrate to a platform that doesn't use your data for training.
- [Documentation platforms for AI coding assistants in defense tech startups (May 2026)](https://falconer.com/guides/defense-tech-documentation-platforms.md): Defense tech engineering teams need documentation platforms that match commercial-startup velocity while meeting data residency, access control, and on-prem deployment requirements. This guide compares Confluence, SharePoint, Notion, Outline, Mintlify, GitBook, and Falconer on what "AI coding assistant ready" actually means.
- [Best developer onboarding tools (June 2026)](https://falconer.com/guides/developer-onboarding-tools.md): A comparison of the best developer onboarding tools in June 2026, ranked on automated documentation maintenance, codebase integration, question deflection, and time to first meaningful contribution. Covers Falconer, Glean, Notion, Dosu (Swimm), and Guru.
- [Docs as code: how to keep documentation in sync with your codebase](https://falconer.com/guides/docs-as-code.md): Docs as code gives you version control for documentation, but most teams stop at tooling without solving drift. Learn why version control alone can't prevent stale docs, and how AI-powered change detection closes the loop by flagging affected documentation after every merged PR.
- [The enterprise LLM wiki: scaling Karpathy's pattern to your org (April 2026)](https://falconer.com/guides/enterprise-llm-wiki-karpathy.md): Andrej Karpathy's LLM Wiki pattern works brilliantly for personal knowledge. It breaks at company scale. Learn why the four properties (capture, link, compound, stay current) need a different implementation for engineering teams, and how an enterprise LLM wiki has to work.
- [Falconer MCP](https://falconer.com/guides/falconer-mcp.md): Give Claude Code, Cursor, and any MCP-compatible tool read and write access to your Falconer knowledge base. Search docs, create specs, update runbooks, and save postmortems — all from your coding agent, with every session leaving the knowledge layer better than it found it.
- [Falconer vs Confluence: which is better in April 2026?](https://falconer.com/guides/falconer-vs-confluence.md): Compare Falconer vs Confluence in April 2026. Auto-updating docs vs manual wiki maintenance, codebase integration, search, and migration support for engineering teams.
- [Falconer vs Glean: which is better in April 2026?](https://falconer.com/guides/falconer-vs-glean.md): Compare Falconer vs Glean in April 2026. See which tool handles documentation better for engineering teams: self-updating docs vs search-only retrieval.
- [Falconer vs Notion: which is better in April 2026?](https://falconer.com/guides/falconer-vs-notion.md): Compare Falconer vs Notion in April 2026. See how auto-updating docs, codebase-aware AI, and Total Search stack up against manual wiki maintenance.
- [Documentation tools for AI coding assistants at Fintech startups: SOC 2 and banking partner requirements (May 2026)](https://falconer.com/guides/fintech-documentation-platforms.md): Fintech engineering teams at Series A and B carry a compliance load that looks like a Series D company's. This guide covers what documentation platforms need to deliver to satisfy sponsor banks, SOC 2 Type II auditors, and AI coding assistants like Claude Code and Cursor at the same time.
- [How to generate release notes and changelogs in 30 seconds](https://falconer.com/guides/generate-release-notes.md): Falconer connects to your GitHub repos and generates changelogs and release notes in plain language.
- [Glean reviews, pricing, and alternatives (April 2026)](https://falconer.com/guides/glean-alternatives.md): Glean reviews, pricing details, and top alternatives compared. Find the best enterprise search solution for your team in April 2026 with features and costs.
- [Documentation tools for AI coding assistants in health tech startups: HIPAA-ready options (May 2026)](https://falconer.com/guides/healthtech-documentation-platforms.md): Health tech engineering teams need AI coding assistants and HIPAA compliance to work together. This guide covers what documentation platforms have to deliver under a BAA, with audit logs and a deployment model that keeps PHI inside the boundary, and compares Notion, Confluence, SharePoint, Mintlify, GitBook, and Falconer.
- [How to build a company brain](https://falconer.com/guides/how-to-build-company-brain.md): A company brain captures, updates, organizes, and monitors everything your organization knows. Learn why most company brains are broken, why the obvious fixes fail, and how to build one that keeps context current for humans and AI agents.
- [Best IDE-integrated knowledge bases for engineering teams June 2026](https://falconer.com/guides/ide-knowledge-bases.md): Engineers lose roughly 23 minutes of focus every time they break out of the editor to hunt for documentation. This guide covers how MCP, semantic search, and self-updating docs change the math, and compares IDE-integrated knowledge bases for engineering teams in June 2026 — including Falconer, Sourcegraph, Replit, Confluence, and Notion.
- [Internal knowledge base: complete guide and best practices for April 2026](https://falconer.com/guides/internal-knowledge-base.md): Complete guide to internal knowledge bases with best practices, implementation strategies, and solutions for April 2026. Build documentation teams use.
- [Best internal knowledge base software for engineering teams: April 2026](https://falconer.com/guides/knowledge-base-software-engineering.md): Engineering docs go stale the moment code ships. We compared Confluence, Notion, Glean, GitBook, Slab, and Falconer to find which knowledge base software actually keeps documentation accurate as your codebase evolves.
- [What are the best knowledge bases for developer workflows in May 2026?](https://falconer.com/guides/knowledge-bases-developer-workflows.md): Developer knowledge is scattered across GitHub, Slack, Linear, and docs nobody updates. We ranked knowledge bases on codebase intelligence, auto-updating docs, workflow integration, and AI agent context to find which ones actually keep pace with how engineering teams ship.
- [What is a knowledge layer for engineering teams? A clear guide for April 2026](https://falconer.com/guides/knowledge-layer-engineering-teams.md): Learn what a knowledge layer is for engineering teams in April 2026. Discover how it connects code, tools, and context to keep documentation current.
- [What is knowledge management and why does it matter in April 2026](https://falconer.com/guides/knowledge-management-engineering-teams.md): Learn what knowledge management is and why it matters for engineering teams in April 2026. Discover how to preserve context and keep documentation current.
- [Best knowledge management tools for startups (April 2026)](https://falconer.com/guides/knowledge-management-tools-startups.md): Startup knowledge decays fast as code evolves and Slack context disappears. We tested the best knowledge management tools for engineering teams in 2026, comparing auto-updating docs, codebase-aware AI, and MCP integration for coding agents.
- [How to build living documentation that actually stays updated](https://falconer.com/guides/living-documentation.md): Documentation dies when code changes and nothing detects the drift. Learn why living documentation requires three systems working together — staleness detection, smart routing, and one-click fixes — and how to build a feedback loop that catches stale docs at the point of change.
- [What docs platform works with MCP and coding agents (May 2026)](https://falconer.com/guides/mcp-documentation-platforms.md): The Model Context Protocol decides whether your documentation is reachable by AI coding agents or stuck behind a chat box. This guide covers which documentation platforms ship MCP servers today, what makes content actually agent-ready beyond the protocol layer, and how Falconer, Notion, Linear, Mintlify, Confluence, SharePoint, and Coda compare.
- [How to migrate from Confluence and Notion to a single source of truth (April 2026)](https://falconer.com/guides/migrate-notion-confluence.md): Split docs across Notion and Confluence destroy trust and waste hours daily. Learn how to audit, consolidate, and migrate into a single self-updating knowledge base that stays current as your codebase evolves.
- [Best Notion alternatives for engineering teams (April 2026)](https://falconer.com/guides/notion-alternatives.md): Notion requires manual maintenance that can't keep pace with fast-moving codebases. We reviewed six alternatives, comparing auto-updating docs, codebase-aware AI, coding agent support, and migration paths for engineering teams in 2026.
- [How to capture critical knowledge and build your company brain: 5 steps for May 2026](https://falconer.com/guides/preserve-institutional-knowledge.md): 42% of institutional knowledge exists only in individual heads, and departures are just when it becomes visible. Learn how to build shared memory into your daily workflow so context accumulates as a byproduct of work instead of disappearing with headcount.
- [How to reduce engineer onboarding time from weeks to days (April 2026)](https://falconer.com/guides/reduce-engineer-onboarding-time.md): New engineers shouldn't spend weeks hunting through Slack, Notion, and GitHub wikis for answers. Learn how to build a self-serve documentation hub, use AI to draft onboarding guides in minutes, and track time to first merged PR to cut onboarding from weeks to days.
- [How to replace Confluence without losing existing docs: a migration guide for April 2026](https://falconer.com/guides/replace-confluence-without-losing-docs.md): Migrate off Confluence in hours, not months. Learn how Falconer imports your spaces via OAuth, audits stale content automatically, and keeps docs current as your codebase evolves.
- [What is the best self-hosted Notion alternative for engineering teams?](https://falconer.com/guides/self-hosted-notion-alternatives.md): For engineering teams replacing Notion, this guide covers Falconer alongside the four legacy self-hosted alternatives — AFFiNE, AppFlowy, Outline, and Anytype. Where the others give you infrastructure control but treat docs as static pages, Falconer reads code, PRs, Slack, Linear, and Drive into one knowledge graph and keeps documentation current as you ship.
- [How to build a self-updating company brain (April 2026)](https://falconer.com/guides/self-updating-company-brain.md): A self-updating company brain captures what your organization knows and keeps it accurate as work changes. Learn the seven principles behind a knowledge system that maintains itself, from atomic chunking to decay detection to closing the update loop.
- [SharePoint reviews, pricing, and alternatives (April 2026)](https://falconer.com/guides/sharepoint-alternatives.md): SharePoint lacks codebase integration and forces engineers to context-switch away from their IDE. Compare the best SharePoint alternatives that auto-update documentation when code changes and feed context to AI coding agents.
- [Best knowledge management tools with Slack integration for engineers](https://falconer.com/guides/slack-knowledge-management.md): Engineering teams live in Slack — but most knowledge tools either search outdated docs or push notifications without keeping content current. This guide compares Slack-integrated knowledge management tools (Falconer, Glean, Guru, Swimm, and GitBook) on bidirectional integration, auto-updates from code, codebase awareness, and how well they deflect repeat questions before they reach senior engineers.
- [Tools that answer questions from Slack using internal docs: May 2026 guide](https://falconer.com/guides/slack-question-answering-tools.md): Engineers spend 30% of their time searching for information, often interrupting teammates in Slack. We compared static search bots, RAG answer engines, and live knowledge intelligence systems to find which ones give answers you can actually trust as your codebase evolves.
- [What tool keeps documentation in sync with code changes: April 2026 guide](https://falconer.com/guides/sync-documentation-code-changes.md): Learn what tool keeps documentation in sync with code changes in April 2026. Stop losing 10 hours weekly to stale docs with AI-powered automation.
- [Best technical documentation platforms with codebase integration (April 2026)](https://falconer.com/guides/technical-documentation-codebase-integration.md): Technical documentation platforms with codebase integration auto-sync docs when code changes. We compared Falconer, Notion, GitBook, Mintlify, Dosu, and others to find which ones actually reduce documentation maintenance.
- [Technical documentation: A complete guide for engineering teams](https://falconer.com/guides/technical-documentation.md): A complete guide to technical documentation for engineering teams — what it is, the categories every team needs (product, process, user docs), how to write docs people actually use, and how to keep them current as code moves. Covers API docs, tool choices, AI-assisted maintenance, and how Falconer auto-updates docs from merged pull requests.
- [Tokenmaxxing vs. token efficiency: what changes when agents share a knowledge layer (May 2026)](https://falconer.com/guides/tokenmaxxing-token-efficiency.md): Tokenmaxxing — treating raw AI token consumption as a productivity metric — collapsed at Meta in April 2026 after engineers gamed the internal leaderboard. Token efficiency is the better unit, but only at the system level: this guide explains why per-engineer efficiency is unmeasurable, why a shared knowledge layer is the biggest lever, and what to measure instead.
- [What is a company brain, and why you need one](https://falconer.com/guides/what-is-company-brain.md): Your company knows more than you think — the problem is nobody can find it. Learn what a company brain is, why most attempts fail, and how to build a self-updating knowledge layer that keeps context accurate for humans and AI agents.

## Customer stories

- [How Payabli replaced Confluence with Falconer, an AI-native knowledge base, in 2 days](https://falconer.com/customers/payabli.md): Payabli's knowledge was scattered across Confluence, Google Drive, and Slack. With Falconer, teams from engineering to HR now write, find, and trust their docs, and the move off Confluence took two days.
- [How Vori uses Falconer as a librarian to automate engineering docs](https://falconer.com/customers/vori.md): Vori turned scattered engineering knowledge across Notion, Slack, Linear, and GitHub into high-quality, self-updating docs with Falconer, cutting repeat questions for engineers.

## Documentation Sets

- [Abridged documentation](https://falconer.com/llms-small.txt): a compact version of the documentation for Falconer, with non-essential content removed
- [Complete documentation](https://falconer.com/llms-full.txt): the full documentation for Falconer

## Notes

- The complete documentation includes all content from the official documentation
- The content is automatically generated from the same source as the official documentation