Falconer
Falconer (founded by Dave Nunez and Maxi Benedetto) · Productivity · Leaning
Falconer is an AI knowledge base for fast-moving teams. It connects the tools you already use, keeps docs tied to the code they describe, and gives both people and AI agents cited answers from one place. As a company documentation tool, it also runs internal knowledge search across your stack. You can reach it through Slack, MCP, or its web app, and it flags stale or conflicting information before it slows anyone down. AI documentation updates happen on their own when the product changes. The pitch is simple: connect your sources, let Falconer build the knowledge base from them, and it keeps that base reconciled with your code, tickets, and conversations so nobody has to chase down an answer again.
About Falconer
What Is Falconer
Falconer is a knowledge management platform that treats your documentation as something that updates itself. Most teams lose time to docs that drift out of date, tribal knowledge that never gets written down, and repetitive questions that block engineers. Falconer connects your sources, builds a knowledge base from them, and keeps that base reconciled with your code, tickets, and conversations.
The gap is real. Docs lag. Nobody rewrites them. On most teams the documentation gets written once, celebrated for a week, and then quietly abandoned while the product it was supposed to describe keeps changing underneath it, release after release, until the people who rely on those pages stop trusting them at all.
The product is aimed at high-speed teams. It gives customer success, sales, and ops people cited answers without waiting on engineering, and it hands AI agents the same up-to-date context so they stop guessing. As a company documentation tool, it replaces the folder full of pages nobody trusts anymore. Everything is read-only by default, so Falconer only writes back to connected systems when you explicitly turn that on.
The main catch is who it's for. Falconer targets defense and deeptech companies that want tight control over where their data lives, and the heavier deployment options like single-tenant and on-prem sit on the Enterprise plan rather than the entry tier. Pricing isn't published, so smaller teams have to talk to sales before they know what they'll pay. It's a platform you adopt as a team, not a single-user tool.
Getting Started
- Go to falconer.com and sign up, or book a demo if you want a walkthrough first.
- Connect your sources, such as GitHub and the 15+ other supported integrations, so Falconer can read your docs, code, and threads.
- Let Falconer generate your first doc set and build the knowledge graph from what it reads.
- Ask questions in Slack or the web app, and check the cited answer against the source it points to.
- Turn on Knowledge Health to see stale, missing, duplicate, and conflicting knowledge, then hand the worst findings to Falconer to fix.
Product Information
A quick look at Falconer's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Engineering teams
- Customer success and support staff
- AI agent builders
Tasks
- Updating outdated runbooks and onboarding guides
- Answering internal questions fast
- Capturing knowledge as you work
Scenarios
- Cutting new-hire onboarding time
- Cleaning up a messy knowledge base
- Shipping fast without knowledge lag
Key features
Self-updating documentation
Falconer watches your code and connected sources, then finds docs that no longer match what your product actually does. Instead of waiting for someone to remember to edit a page, it proposes the exact updates. That keeps runbooks, onboarding guides, and support docs from quietly rotting. Old docs are a tax. This pays it down.
Cited answers across your stack
Ask a question in Slack or the web app and Falconer pulls from your docs, code, tasks, and conversations. Every answer cites the company knowledge behind it, so you can check it rather than trust it blindly. This is the feature that lets support and sales teams stop waiting on engineers.
Knowledge Health scoring
Knowledge Health grades every doc on coherence, coverage, freshness, and density, then rolls those into a single score out of 100 so you can watch the number move as your team fixes things. The breakdown matters more than the number: it tells you whether the problem is contradictions, missing topics, stale content, or redundancy. Findings are ordered by blast radius, so you fix the doc that breaks a build before the one with a typo. Simple as that.
Connect your sources
Falconer links GitHub and 15+ other integrations to build a knowledge graph from your stack. Code becomes context. Slack threads and meetings feed the same base. Read-only is the default, which means it won't touch connected systems until you enable writes. Fine by most security teams.
MCP and API access for agents
Falconer ships an MCP server and API with webhooks so AI agents can pull live company knowledge. That gives agents a current, cited source to work from, which cuts down on hallucinated answers. You can bring your own model provider key and keep knowledge portable across models.
Deployments built for control
You can run Falconer cloud-hosted, as a managed single tenant, or fully on-prem. It's built with defense and deeptech in mind, where data location is a hard requirement rather than a preference. Single-tenant and self-hosted options sit on the Enterprise plan.
Security and compliance
Falconer is SOC 2 Type I and HIPAA compliant, encrypts data in transit and at rest, and doesn't train on customer data. Permissioned workspaces give each team a focused view while gating sensitive knowledge.
Pros and cons
Pros
- Ties documentation to code so updates track product changes automatically.
- Cited answers let support, sales, and ops teams resolve questions without engineering escalations.
- Knowledge Health turns vague "our docs are a mess" complaints into a ranked, fixable list.
- MCP and API access give AI agents live, cited context instead of stale training data.
- Deployment options include managed single-tenant and on-prem for teams with strict data rules.
Cons
- No published pricing, so you can't budget for it without contacting sales.
- No free plan, which rules out trying it on a small project before committing.
- Heavier deployments like on-prem are locked to the Enterprise tier.
- Its value depends on connecting your sources, so it does little on its own before setup.
Frequently asked questions
It builds an AI knowledge base from your docs, code, tasks, and threads, then keeps it current and answers questions with citations. Think of it as a system of record for company knowledge that maintains itself as your product changes.
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