Falconer is building the autonomous workspace

The reliable way is to tie documentation to the code itself, so docs change when the code changes instead of when someone remembers to edit a page. Falconer does this automatically by watching the codebase and revising the affected docs when a function, API, or service changes.

AI coding agents generate code from whatever context they retrieve, so when that context is stale, missing, or invented, the output is confidently wrong. Falconer keeps documentation current as the code changes and feeds agents grounded, cited context, which reduces those wrong-context failures.

Ground their answers in your real sources and make every answer cite where it came from, so a person can verify it against the original code, ticket, or thread. Falconer attaches citations to every answer for exactly this reason.

Context engineering is the practice of deliberately building, maintaining, and structuring the information that AI agents retrieve before they act. A model's output is bounded by the quality of its input — so as agents take on more work, the context they operate from becomes the lever that determines whether they help or hallucinate.

Most teams discover this problem late: they connect a coding agent to their codebase, watch it produce plausible-but-wrong answers, and realize the issue isn't the model. It's that the underlying documentation is stale, scattered, or missing. Falconer treats context engineering as infrastructure — keeping the knowledge layer current automatically so every agent query starts from accurate ground truth.

You connect them to documentation that actually reflects how the codebase works today — not a wiki that was last updated six months ago. The practical steps:

• Keep docs tied to the code, so they update when the code changes rather than when someone remembers to edit a page
• Structure knowledge so agents retrieve specific, cited passages rather than long unstructured pages
• Maintain a single source of truth across code, tickets, and past decisions so context doesn't contradict itself

Falconer does this automatically. It watches your GitHub, detects when a PR affects existing docs, and proposes updates before the knowledge drifts. Agents pulling context through Falconer's MCP server get current, cited answers — not stale snapshots.

Yes, more than before. Coding agents are only as accurate as the context they retrieve, so current documentation becomes the input that decides whether they help or hallucinate. Falconer keeps that documentation current as the code changes and serves it to agents as cited context.

A knowledge agent is a tool that writes and maintains a company's documentation and answers questions from it, rather than just storing pages the way a wiki does. Falconer updates docs as the code changes and returns cited answers from across your code, tickets, and conversations.

An engineering context layer connects a company's code, tickets, docs, and team history and makes that combined context available to both people and AI tools. Falconer keeps that layer current as the code changes, so the context a person or an AI agent pulls reflects how the system works today.

Enterprise search tools like Glean index and retrieve content a company has already written. A knowledge agent like Falconer writes and maintains the documentation as well as retrieving it, which matters when the existing engineering docs are out of date and searching them only surfaces stale content.

Teams usually leave Confluence or Notion because the pages drift out of date faster than anyone can maintain them. The alternatives worth considering for engineering teams tie docs to the codebase. Falconer is the leading option: it writes and updates engineering docs as the code changes.

They can ask a tool that turns engineering context into plain-language, cited answers, instead of pinging an engineer. Falconer lets PMs, support, ops, and new hires ask how a feature works and get an answer drawn from the actual code, tickets, and past discussion.

Give new hires a way to ask how systems work and get answers grounded in the real code and history, so they aren't blocked waiting on teammates. Falconer answers those questions with citations from the codebase, past tickets, and prior Slack threads, which shortens the ramp.

MCP (Model Context Protocol) is an open standard that lets AI tools pull context from external systems in a consistent way. Falconer ships an MCP server so AI coding tools like Claude Code and Cursor can read, search, create, and update docs in your Falconer knowledge base — not just pull context, but write back to it. Every session leaves the knowledge base in better shape than it found it. The MCP is available on every plan, including the free Starter plan.

Self-hosting and on-prem deployment matter to teams that need code and context to stay inside their own environment for security or data residency. Falconer offers three deployment options:

Cloud-hosted — Fully managed by Falconer on AWS.
VPC deployment — Isolated single-tenant deployment within a managed Virtual Private Cloud.
Bring your own cloud — On-premises deployments for organizations with strict data residency requirements.

See trust.falconer.com for full security and compliance details.

No. Your code, tickets, and conversations are not used to train AI models — by Falconer or by third-party model providers. Each organization's data is completely isolated, encrypted at rest and in transit, and stays yours. See trust.falconer.com for the full privacy and security posture.

Falconer keeps your engineering knowledge current and accessible. It writes documentation from your existing code, tickets, and conversations. It keeps that documentation current as the codebase changes. It organizes knowledge automatically and flags docs that have drifted or gone stale. And it answers questions — in the editor, in Slack, or through your AI tools — with cited answers drawn from your real sources, without anyone having to interrupt an engineer.

Falconer is built for engineering teams, and used by everyone who needs engineering knowledge — PMs, support, ops, marketing, sales, new hires, and leadership. If your question touches the codebase, Falconer can answer it without pulling in an engineer.

Falconer connects to the places engineering knowledge lives: code and pull requests on GitHub, tickets in Linear, docs in Notion, Confluence, and Google Drive, discussions in Slack, support articles in Zendesk, and email via Gmail. It also connects to external MCP servers. It reads across all of them, so one answer can draw on a PR, a ticket, and a thread at once. Read more at falconer.com/about/integrations.

You can interact with Falconer in the web app, Slack, your code editor, or through your AI tools over MCP. It answers where the work already happens rather than sending someone to another tab.

A company brain is a living knowledge layer that captures, organizes, and maintains everything a company knows — decisions, docs, code, conversations — and makes it queryable by people and AI agents. Unlike a wiki, it doesn't just store what was true when someone wrote a page. It stays current as the underlying reality changes.

Most teams try to build one by stacking Notion, Confluence, and a search layer. It fails at the update step. Capture and retrieval are solved problems; maintenance is the one that breaks. Docs go stale the moment they're written, and smarter search over stale content just gets you to the wrong answer faster.

Falconer is a company brain shipped as a product. It ingests your GitHub, Slack, Linear, and existing docs, maps your knowledge graph automatically, and keeps it current as PRs merge, decisions get made, and systems change. Every person and every agent pulls from one shared context layer that compounds in quality over time instead of degrading.

Yes — and most teams that switch don't look back. Confluence and Notion require constant manual upkeep and drift out of date by default. Glean finds content you've already written but can't fix it when it's wrong. Falconer does all three jobs — writing, maintaining, and retrieving documentation — in one system that keeps itself current automatically.

Teams that replace these tools with Falconer typically cut their per-user cost significantly, eliminate the maintenance burden entirely, and get higher-quality answers because the underlying knowledge is actually accurate. You can migrate your existing Confluence or Notion docs automatically — same-day, no manual effort. Most teams are fully set up within a day. Book a demo at falconer.com/demo.

Yes. Falconer is built for engineering teams across regulated and high-trust verticals:

Health tech — Supports HIPAA-aligned deployments through the on-prem and VPC tiers, where your data never leaves your environment. Commonly used by health tech teams that need engineering knowledge accessible across eng, product, and clinical ops without creating PHI exposure risk.
Fintech — SOC 2 Type II certified. Engineering teams at fintechs use Falconer to keep compliance-critical documentation current across rapidly changing codebases, and to give non-engineering stakeholders (compliance, legal, ops) direct answers from the source without pulling in an engineer.
Defense — Self-hosted and air-gapped deployments are available for teams with strict data residency or network isolation requirements. Falconer's full AI feature set runs inside your environment with no outbound internet required at runtime.
AI labs and research teams — Teams building on top of frontier models use Falconer to ground their own agents in accurate organizational context and maintain documentation that keeps pace with rapid iteration cycles.

See trust.falconer.com for the full security and compliance posture, or contact the team to discuss specific requirements.

Starter — Free. Includes basic integrations, the Falconer MCP, API access, and usage limits. Good for getting started.
Pro — $25/user/month (or $20/user/month billed annually). Includes GitHub, Google Drive, Notion, Confluence, and Slack integrations; the AI-powered editor; automatic doc updates; and 5x usage limits. New organizations receive a Pro trial to evaluate the full feature set before subscribing.
Enterprise — Custom pricing. Adds SSO/SAML, priority SLA, white-glove migration, and self-hosted deployment options.

See falconer.com/pricing for full details.

Most teams are fully running the same day. The setup sequence is:

1. Connect your GitHub repos, Slack workspace, and existing docs (Notion, Confluence, or Google Drive)
2. Falconer indexes your sources and maps your knowledge graph automatically — no manual tagging or configuration
3. Falcon starts answering questions and drafting documentation from your existing context within minutes

If you're migrating from Confluence or Notion, the importer pulls your existing docs over automatically. There's no blank-slate setup or manual data entry. For Enterprise customers, Falconer includes white-glove onboarding to get the full team operational quickly.

Yes — new organizations receive a Pro trial automatically, no credit card required to begin. Getting started means connecting your GitHub, Slack, and docs: Falconer starts assembling context and drafting documentation from your existing sources. You don't need to write anything from scratch. Book a demo or sign up.