CentralAgentGet a demo

The World’s First Central
Tribal Knowledge Extraction Engine

Make employees and agents know your company’s ins and outs like your most tenured engineer.

Run the demoInside your cloud

Built on 120+ deep dives with tech leads from

  • bBumble
  • Angular
  • JPJPMorgan
  • Notion
  • Datadog
  • RRamp
  • Stripe
  • Figma
  • Vercel
  • Shopify
  • Atlassian
  • Twilio
  • Snowflake
  • HubSpot
  • Airbnb
  • Uber
  • Spotify
  • Slack
  • Netflix
  • Salesforce
  • Adobe
  • Nvidia
  • Tesla
  • GitHub
  • Reddit
  • Dropbox
  • Zoom
  • PayPal
  • Coinbase
  • DoorDash
  • Discord
  • OpenAI
  • Cloudflare
  • MongoDB
  • Databricks
  • LinkedIn
  • Klarna
  • Revolut
  • Google
  • Amazon
  • Meta
  • Apple

Capture, at the physical layer

LIVE
mayalet’s ship the EU cluster on Friday
omaronly after the residency check, remember Berlin
CLAUDE CODE
you> add the residency check to the deploy runbook
claudedone, runbook updatedsaved to the brain
you> ship the EU cluster once it passes

Structured into agent memory

A knowledge graph with 10 dimensions of structure, designed from 120+ interviews with tech leads.

Core purposeCustomer data stays in region
North star / KPIsExport success rate
Strategy & goalsEU expansion, Q3
RoadmapEU cluster GA
DecisionsEU data residency gate
PeopleBerlin incident, Omar
Processes / SOPsDeploy runbook
ImplementationResidency check step
Projects & systemsEU cluster
ImpactQuarter-end exports

Answer, everywhere

Connect once. Each meeting and session it sits in makes the next answer sharper, with the source attached.

OVER MCP
Claude CodeCursorCodex
What breaks if we change the ingestion schema?
Search and analytics consume it directly; patch the digest lambda first
DESIGN MEETING · LIVE
lenawhy do exports fail every quarter-end?
omarcentralagent, thoughts?
centralagentAcme batch-uploads on the 30th; exports need the widened queue pool from the Q1 incident fix

Frontier agents are like smart consultants. With CentralAgent they work like your most tenured engineer.

Ask like you’d ask your most tenured engineer

What you have to write into each session

every new session starts from zero

ContextWe run gbrain, our internal graph store, as a custom Postgres extension on a self-managed EC2 instance (r6i.xlarge, gp3, nightly pg_dump to S3).

Backgroundgbrain is a custom C extension we wrote in-house. The decision to self-host predates most of the current team.

ConstraintsNo time to rewrite the extension this quarter; migration window is one weekend; budget roughly flat.

QuestionWhy don’t we just put gbrain on RDS or Aurora instead of babysitting our own Postgres on the box?

What you ask when CentralAgent is connected

❯ Why don’t we just put gbrain on RDS or Aurora instead of babysitting our own Postgres on the box?

Same task, two sessions

Without CentralAgentgranolaawsgithubnotionready
❯ Why don’t we just put gbrain on RDS or Aurora instead of babysitting our own Postgres on the box?

answer

With CentralAgentgranolaawsgithubnotionready
❯ Why don’t we just put gbrain on RDS or Aurora instead of babysitting our own Postgres on the box?

answer

you

Why don’t we just put gbrain on RDS or Aurora instead of babysitting our own Postgres on the box?

Without CentralAgentWith CentralAgent

All inside your cloud

CentralView ships with your CentralAgent: sources feed in on the left, three brains structure what they capture into one company brain, and the MCP endpoint serves every harness you connect. It runs in your private cloud, under your control.

CentralViewdrag sideways or with two fingers to move the mapTry it yourself
Starting the live demo…

AI runs on context

Models improve for everyone. Only yours gets smarter about your company.

Frontier models train on the public internet; your decisions, constraints, and hard-won fixes are not in it. CentralAgent briefs the agents you already run, over MCP, and every agent you connect starts smarter.

Intelligence plus tribal knowledge is what your most tenured engineer runs on.

Start collecting your tribal knowledge

We deploy inside your cloud at zero cost + 30-day free trial.

Deployed in your VPC. Even the CentralAgent team can’t access your data.
Book your setup call
  1. Day 1One setup call, then our engineer deploys the brain inside your VPC.
  2. Week 1Your meetings and agent sessions start flowing in.
  3. Week 2First answers that used to need a veteran: why a decision was made, which fix actually worked, who owns what. Each one cites the meeting or session it came from.
  4. Day 30Questions that used to wait on a Slack thread come back in seconds, with sources. Your coding agents pull the same answers over MCP.

Questions

How is this different from other company brains?
Tech leads in our 120+ interviews kept describing the same arc: a generic knowledge store works for a month or two, then retrieval degrades as content piles up. So we modeled CentralAgent’s memory on how biological memory consolidates, with per-role structure. In our testing it held up as content grew, where the generic store’s retrieval degraded. Comparisons: vs Glean, vs Notion AI.
Where does our data live?
In your private cloud, start to finish. It never leaves and nobody trains on it. Details on the security page.
Does it work with Claude Code, Cursor, and Codex?
Yes. The brain is one MCP endpoint, so any agent that speaks MCP can use it. Teammates query the same memory in CentralView.
How is this different from Granola?
Different jobs. Granola takes meeting notes, per person who runs it. CentralAgent sets up once for the whole company and captures continuously: meetings and agent sessions both, mapped into one memory the way a principal engineer thinks, so the note from a design review links to the session that fixed the deploy it discussed.
We already keep a CLAUDE.md. Why this?
Keep it. A rules file holds what someone remembered to write, per repo, and it goes stale. CentralAgent updates itself from every meeting and agent session, spans repos and teams, and answers questions no rules file anticipated.
Where in our development lifecycle does this plug in?
Wherever your agents work. The brain is one MCP endpoint, so any phase where an agent needs company context can draw on it: writing specs against past decisions, reviewing designs, planning tests around known failure modes.
What exactly is tribal knowledge?
The knowledge that lives only in people's heads: how things really get done, why decisions were made, what never to do. More on the tribal knowledge page.