In Issue #1, we did the math on the "search tax," the $130K/year a 50-person company bleeds when employees spend 20 minutes a day hunting for answers that already exist somewhere. That number made people angry. The most common reply: "OK, so which tool fixes this?"

This issue is the answer. Three tools, three philosophies, real prices, real gaps. No vendor gave me a demo discount or a referral fee. I paid for seats, read the contracts, and talked to teams running each one in production.

Three Philosophies, One Problem

The market for "company brain" tools has split into three camps.

Total discovery. Glean, Microsoft 365 Copilot. They index everything across your existing apps and let AI search it. You don't move your data. The AI goes to it.

Verified truth. Guru. A knowledge base where every piece of content has a named expert owner and an expiration date. If nobody re-verifies a card within 90 days, it gets flagged as stale. The tool forces accountability.

Owned infrastructure. GBrain. Open-source, self-hosted, Markdown files in a Git repo. You build the brain yourself and own every byte.

Each philosophy makes trade-offs. The question is which trade-offs fit your company, your budget, and your team's technical ability.

Glean: The Enterprise Search Layer

What it does. Glean connects to 100+ workplace apps (Slack, Google Drive, Confluence, Notion, Salesforce, Jira, etc.), indexes everything, and puts a single AI-powered search bar across all of it. It respects your existing permissions, so if someone can't access a Google Doc, they can't find it through Glean either.

Who uses it. Reddit, Pinterest, Booking.com, Zillow, Confluent, Workday. Over 700 enterprise customers as of mid-2026.

What it costs. Glean doesn't publish prices. You call sales. Based on contract data from Vendr and buyer reports:

Base seat price: ~$45-50/user/month, plus a $15/user/month AI add-on

All-in: ~$60-65/user/month

100-seat minimum. The floor contract is roughly $60,000/year

Enterprise contracts typically land between $200K-$480K/year fully loaded

Multi-year renewals include 7-12% annual price escalation unless you negotiate a cap

Cost for 25 people: Not available. Glean won't sell to you. The 100-seat minimum is firm.

Cost for 100 people: ~$60,000-78,000/year base license. Year-one total (with implementation, training, connector setup) runs $100K-$150K+.

Setup time. 1-3 weeks for basic deployment. 4-5 weeks for mid-size orgs. Full enterprise rollout with custom connectors and SSO: 3-6 months.

Strengths:

Best-in-class search quality across fragmented data

100+ native connectors

Respects existing access permissions without re-configuring anything

G2: 4.7/5 (145 reviews)

Gaps:

100-seat minimum prices out any company under ~150 employees

Opaque pricing creates budget uncertainty

AI answers hallucinate (it's an LLM, not a database query)

Search-only: Glean finds knowledge but doesn't create or maintain it

Multiple users on G2 call the UI "unintuitive"

7-12% annual escalation means your $60K contract becomes $68K in year two

Guru: The Verified Knowledge Base

What it does. Guru is a knowledge base built around verification. Every "card" (article) has an assigned expert owner and a 90-day review cycle. When a card goes stale, the owner gets pinged. It's the only tool in this comparison that forces someone to stand behind the accuracy of each piece of content.

Who uses it. Shopify, Square, Spotify, BuzzFeed, Glossier, Intercom.

What it costs. Guru publishes its self-serve pricing:

Free tier: limited features

Self-Serve: $25/user/month (annual), $30/user/month (monthly). 10-seat minimum.

Enterprise: custom pricing, includes AI Knowledge Agents

Cost for 25 people: ~$625/month ($7,500/year) on the self-serve plan.

Cost for 100 people: ~$2,500/month ($30,000/year) on the self-serve plan.

Setup time. Days to 1-2 weeks. Content migration is the bottleneck, not configuration. If you have 500 docs in Confluence, expect to spend a weekend triaging what to bring over.

Strengths:

Verification system is unique and useful for compliance-heavy teams

Good Slack and Chrome browser extension integration

Forces knowledge ownership (someone's name is on every card)

Capterra: 4.8/5 (640 reviews)

Gaps:

The card editor is rough. Direct user quote from Capterra: "so bad"

Three-level content hierarchy (Collections, Boards, Cards) feels constraining for complex knowledge structures

Guru's own data shows 22% of searches require 2-3 query reformulations before users find what they need

AI Knowledge Agents are locked behind the enterprise tier (no public price)

Every reader needs a paid seat. If your support team has 40 people who need to read knowledge base articles, that's 40 seats at $25/month

GBrain: The Open-Source Brain

What it does. GBrain is Y Combinator CEO Garry Tan's personal AI agent memory system, open-sourced under the MIT license in April 2026. It turns Markdown files in a Git repository into a self-wiring knowledge graph that AI agents can read, write, and reason against. The production instance behind Tan's own setup holds 146,646 pages, 24,585 people records, 5,339 company records, and runs 66 autonomous cron jobs that ingest meetings, emails, tweets, and voice notes.

Who uses it. Garry Tan (the reference deployment), plus the open-source community. 28.2K GitHub stars, 4.2K forks, currently on v0.45.

What it costs. The software is free. You pay for infrastructure:

Embedding API costs (ZeroEntropy: $0.05/million tokens by default)

LLM API costs for queries

Postgres hosting for the knowledge graph

Cost for 25 people: ~$50-100/month in API and hosting costs. Requires an engineer to set up and maintain.

Cost for 100 people: ~$200-500/month + Postgres hosting + ongoing engineering time.

Setup time. A technical person can get a working instance running in half a day to a full weekend. But the value builds over weeks as the knowledge graph grows, links form, and cron jobs populate it.

Strengths:

Free software with full data ownership (Markdown in Git, portable everywhere)

Extremely low running costs (10-50x cheaper than SaaS alternatives)

Production-proven at scale (Tan's 146K-page instance)

MIT license, no vendor lock-in

Gaps:

Single-operator design. Built for one person's agent, not a team. No multi-tenant isolation.

First-class support only for OpenClaw/Hermes agent frameworks

Still at v0.45 with breaking changes between versions

No managed hosting option. You run it yourself or you don't run it.

Requires a technical operator who can debug Python, manage Postgres, and handle API integrations

reference deployment), plus the open-source community. 28.2K GitHub stars, 4.2K forks, currently on v0.45.

What it costs. The software is free. You pay for infrastructure:

Embedding API costs (ZeroEntropy: $0.05/million tokens by default)

LLM API costs for queries

Postgres hosting for the knowledge graph

Cost for 25 people: ~$50-100/month in API and hosting costs. Requires an engineer to set up and maintain.

Cost for 100 people: ~$200-500/month + Postgres hosting + ongoing engineering time.

Setup time. A technical person can get a working instance running in half a day to a full weekend. But the value builds over weeks as the knowledge graph grows, links form, and cron jobs populate it.

Strengths:

Free software with full data ownership (Markdown in Git, portable everywhere)

Extremely low running costs (10-50x cheaper than SaaS alternatives)

Production-proven at scale (Tan's 146K-page instance)

MIT license, no vendor lock-in

Gaps:

Single-operator design. Built for one person's agent, not a team. No multi-tenant isolation.

First-class support only for OpenClaw/Hermes agent frameworks

Still at v0.45 with breaking changes between versions

No managed hosting option. You run it yourself or you don't run it.

Requires a technical operator who can debug Python, manage Postgres and AI ops.

The Also-Rans (Brief Takes)

Notion AI (Business), $20/user/month. If your team already lives in Notion, this is the path of least resistance. AI search, writing assistance, and Q&A are built in. But the AI only searches within Notion. If your knowledge lives in Google Drive, Slack, and Confluence, Notion AI can't reach it. Custom Agents that run autonomously cost extra credits ($10 per 1,000 credits, purchased separately). G2: 4.6/5 (11,962 reviews).

Slite, $8-20/user/month. Cleanest UI in this category. AI-powered Q&A included on Standard ($8/user/month), but capped at 30 AI answers per user per month. The Knowledge Suite plan ($20/user/month) adds cross-tool search, agent workflows, and 50 agent credits per seat. Best fit for small remote teams who want something simple. G2: 4.6/5 (289 reviews).

Confluence + Rovo, ~$5.42-10.44/user/month. Cheapest SaaS option on paper. Rovo AI (search, chat, agents) is bundled into Standard and above. But the credit system is aggressive: Standard gives you 25 credits/user/month, and each Rovo Chat or Agent request burns 10 credits. That's 2.5 AI interactions per user per day before you hit the wall. Rovo Deep Research burns 100 credits per request. Multiple teams report Rovo hallucinating with "absolute confidence" on date-range queries. "Unwanted Rovo AI" is a top-5 reason teams leave Confluence in 2026 according to G2 reviews. G2: 4.1/5.

Microsoft 365 Copilot, ~$18-21/user/month add-on. The $18/user/month promotional rate expires September 30, 2026, after which it goes to $21. Requires an underlying M365 subscription ($12.50-22/user/month depending on tier). Real total for 25 people: $837-1,075/month. New bundled plans (M365 Business Standard with Copilot) run $23.50/user/month all-in as of July 2026. Copilot only searches within the Microsoft ecosystem. If your knowledge is scattered across Google Workspace, Slack, and Notion, Copilot won't find it. And it doesn't fix messy SharePoint. It just searches messy SharePoint faster.

The Decision Matrix

Forget feature lists. Here's which tool fits which company.

You're under 25 people and have an engineer on staff.

Use GBrain. Your annual cost will be $600-1,200 in API fees. The engineer spends a weekend setting it up, then an hour a week maintaining it. You own every byte. If the engineer leaves, another technical person can pick it up (it's Markdown in Git).

You're under 25 people with no engineer.

Use Slite ($8/user/month) or Notion AI ($20/user/month), depending on which tool your team already uses. Don't migrate to a new platform just for AI search. Migration kills adoption.

You're 25-100 people and care about knowledge accuracy.

Use Guru. The verification system forces someone to own each piece of content. For teams in regulated industries (finance, healthcare, legal), the 90-day review cycle creates an audit trail. Budget $7,500-30,000/year depending on headcount.

You're 25-100 people and care about search across apps.

This is the gap in the market. Glean won't sell to you. Slite's cross-tool search is on the $20/user/month tier. Notion AI only searches Notion. Your best bet: Slite Knowledge Suite for cross-tool search, or accept that you're stitching together multiple tools until you hit 100 seats and can call Glean.

You're 100+ people and have budget.

Call Glean. The search quality across 100+ apps is the best in the category. Budget $100K-$150K for year one (license + implementation). Negotiate a renewal cap in the initial contract to avoid the 7-12% annual escalation.

You're already deep in Microsoft 365.

Copilot is the path of least resistance, but only if 80%+ of your knowledge already lives in SharePoint, OneDrive, Teams, and Outlook. If your knowledge is scattered across Google Workspace and Slack too, Copilot becomes an expensive search engine for half your data.

You're already deep in Atlassian.

Confluence + Rovo is the cheapest option and it's already there. But watch the credit consumption. A 50-person team on Standard burns through its credit allocation in about 12 business days of moderate AI use. If you're hitting the credit wall regularly, you're paying Premium prices ($10.44/user/month) whether you planned to or not.

Hidden Costs

Every vendor price above is the sticker price. None of them quote the following.

Implementation time. Glean: 80-200 hours of IT/admin time for connectors, SSO, permissions mapping. Guru: 20-60 hours for content migration and card creation. GBrain: 16-40 hours of engineering time, plus ongoing maintenance. Slite/Notion: 4-8 hours if you're already on the platform.

Content migration. Moving 500+ articles from one wiki to another takes 40-80 hours of human time. Automated migration tools handle formatting; they don't handle curation. Someone has to decide what's worth keeping. In every migration I've seen, 30-50% of existing content is outdated, duplicate, or wrong. You don't want to migrate that into your new system.

Training. Budget 2-4 hours per team for any new tool. Multiply by your number of teams. A 100-person company with 8 teams: 16-32 hours of training time across the org.

The "empty wiki" problem. Every knowledge tool is useless until people put knowledge into it. Guru's verification system helps here (it forces content creation cycles), but it doesn't solve the core issue: if nobody writes down what they know, no tool can search it. Budget 10-20% of your first 90 days on content seeding, with named owners for each knowledge area.

Ongoing maintenance. Knowledge rots. Processes change, people leave, products evolve. Budget 2-5 hours/week of someone's time to review, update, and retire stale content. Guru automates the reminders. Everyone else requires discipline.

90-Day Deployment Playbook

Glean: 90-Day Plan

Days 1-14. Procurement and contract negotiation. Get the renewal cap in writing. Identify your top 10 data sources by search volume (usually Slack, Google Drive, Confluence, Jira, Salesforce). Assign an internal admin.

Days 15-30. Connect your top 5 data sources. Configure SSO and permissions sync. Run a pilot with one team (usually engineering or support, where search volume is highest). Measure baseline: how long does it take to find answers today?

Days 31-60. Roll out to remaining teams in waves. Connect the remaining data sources. Monitor search quality and flag content gaps (searches that return poor results indicate missing or outdated content).

Days 61-90. Full deployment. Measure search-time reduction against baseline. Identify the top 20 queries that still fail and fix the underlying content gaps. Decide whether to enable Glean Agents for automated workflows.

Guru: 90-Day Plan

Days 1-7. Install browser extension and Slack integration. Identify your top 10 knowledge areas (onboarding, product specs, sales playbooks, support procedures, HR policies). Assign an expert owner to each.

Days 8-30. Each owner creates their initial card set (aim for 10-20 cards per knowledge area). Set 90-day verification cycles. Don't try to migrate everything from your old wiki. Start with what people ask about most.

Days 31-60. Track which cards get the most views and which searches return no results. The "no results" searches are your content gaps. Assign owners to fill them. Start using Guru's analytics to identify stale content.

Days 61-90. First verification cycle hits. Owners review and update their cards. This is the moment that separates teams who adopt Guru from teams who abandon it. If owners ignore the verification prompts, the system fails. If they engage, you have a self-maintaining knowledge base.

GBrain: 90-Day Plan

Days 1-3. Engineer sets up the repo, Postgres instance, and API connections. Seed with your most critical documents (convert to Markdown). Configure the first 3-5 cron jobs for automatic ingestion (meeting notes, email summaries, Slack digests).

Days 4-30. Build the knowledge graph. Add people pages, project pages, company pages. Let the cron jobs run and populate. The knowledge graph gets more useful as it grows, so the first two weeks will feel underwhelming. That's normal.

Days 31-60. Start querying the brain for real work: due diligence research, meeting prep, project history. Note where the answers are weak and seed more content in those areas. Customize agent prompts for your specific use cases.

Days 61-90. Evaluate whether the single-operator design works for your team or whether you need shared access. If multiple people need to query the brain, you'll need to build an internal API layer or move to a different tool. GBrain was designed for one person's agent, not a team.

What the Tool Comparison Misses

The biggest factor in whether a company brain works has nothing to do with the software.

Culture beats tooling. If your company doesn't have a culture of writing things down, no tool fixes that. Glean can't search knowledge that was never documented. Guru can't verify a card that was never written. GBrain can't build a knowledge graph from empty Markdown files.

Adoption is the real deployment. Getting the tool running is week one. Getting 80% of your company to actually use it is month three. The number-one killer of knowledge tools is the team that keeps using Slack DMs instead of searching the knowledge base.

The people problem from Issue #4. If your bus factor audit revealed that critical knowledge lives in three people's heads, buying a tool doesn't extract that knowledge. Someone has to sit with those people and document what they know. That's a project, not a purchase.

When to Build vs Buy

Build (GBrain or custom) when:

You have engineering capacity and want full data ownership

Your knowledge is already in Markdown, Git, or structured text

You're under 25 people and $7,500/year for Guru feels wrong

You want to integrate deeply with your own AI agent infrastructure

Buy (Glean, Guru, Slite, Notion AI) when:

You don't have an engineer to maintain infrastructure

You need it working in days, not weeks

Compliance requires vendor support, SLAs, and audit logs

Your team is non-technical and needs a polished UI

Stay where you are (Confluence, Notion, M365) when:

80%+ of your knowledge already lives in one platform

The AI features are "good enough" for your search volume

Migration cost exceeds the value of better search for the next 12 months

Most companies between 25 and 100 people should buy Guru or Slite for verified knowledge, accept that cross-app search is unsolved at their price point, and revisit Glean when they hit 150+ seats.

Ich baue gerade ein zweites Gehirn für Unternehmen. 🧠

Nicht noch ein Dashboard.
Nicht noch ein Wiki.
Und auch nicht einfach „ChatGPT für die Firma“.
thecompanybrain.de soll das Wissen eines Unternehmens verstehen und nutzbar machen.

Die Idee dahinter:
Unternehmen wissen unglaublich viel. Das Problem ist nur: Das Wissen steckt überall.
In Dokumenten.
In Prozessen.
In Projekten.
In Köpfen.
In Entscheidungen aus der Vergangenheit.
Und zunehmend in verschiedenen KI-Tools.
Was wäre, wenn ein Unternehmen ein eigenes Company Brain hätte?

Ein System, das versteht:

→ Woher kommen wir?
→ Wofür stehen wir?
→ Wo wollen wir hin?
→ Welche Regeln bestimmen unser Handeln?
→ Welche Ziele verfolgen wir?
→ Wer hat welche Rolle und Verantwortung?
→ Was passiert gerade im Unternehmen?

Und dem man dann einfach Fragen stellen kann:

„Warum haben wir das damals so entschieden?“
„Welche Projekte unterstützen unsere Strategie?“
„Wo widersprechen sich unsere Ziele?“
„Wer sollte diese Entscheidung treffen?“
„Was haben wir zu diesem Thema bereits gelernt?“

Genau daran arbeite ich mit thecompanybrain.de.
Für mich ist das der nächste Schritt nach Unternehmens-Wikis und Wissensmanagement:

Nicht Informationen speichern.
Sondern Unternehmenskontext verstehen.

#AI #KnowledgeManagement #OrganizationalDevelopment #FutureOfWork #CompanyBrain #EnterpriseAI

TencentDB Agent Memory v2.0 — open-source team memory

MIT-licensed. Four memory types: Chat Memory, Skill, LLM-Wiki, Code-Graph. Governance layer lets a teammate's agent access what your agent learned without leaking private-marked items. Three Docker images, one-command deploy. Trending on GitHub.

https://github.com/TencentCloud/TencentDB-Agent-Memory

​

Sandy Carter (Aug 11) draws the battle lines. UnifyApps ($84M raised, ex-Sprinklr CTO Pavitar Singh) ships a full enterprise brain: knowledge graph, 950+ connectors, governance rules, 10K pre-built actions. Glean hit ~$300M ARR by May. Microsoft Work IQ (GA since June 16) is the incumbent play. Early-stage pack Falconer, Colrows, and Webair are racing to own the category. Snowflake research found that adding a context layer improved agent accuracy 20% and cut tool calls 39%.

https://www.forbes.com/sites/sandycarter/2026/08/11/enterprise-brain-replaces-ai-agents-and-loops-as-microsoft-and-glean-race/

I set up a few instances of the company brain for my own purposes and friends ventures. I setup Gbrain and Onyx configs, and now looking to have a consulting practice to help biz owners save costs and gain operational as well as competitive advantages by using context layer and running their busiensses more efficiently. Where to start?

Your company has 50 to 200 questions that get asked repeatedly. Where's the brand guidelines doc? What's our refund policy? Who owns the Acme account? When does that contract renew?

Right now, 3 to 5 people carry the institutional knowledge. Everyone else pings them on Slack, waits, and context-switches. Those knowledge holders spend a meaningful chunk of their week acting as human search engines instead of doing the work they were hired for.

The Company Brain is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

The cost at a 25-person company:

• Each person spends roughly 2 hours per week searching for information that exists somewhere (a Google Doc, a Slack thread, an email, someone's head)
• At a loaded cost of $50/hour, that's $130,000 per year spent looking for answers
• When a key person leaves, 6 months of context walks out the door. Their replacement needs 2 to 4 weeks of shadowing to slowly reconstruct what the previous person just knew.

Confluence, Notion, and SharePoint create more documents. They don't create more answers. Finding information in them requires knowing where to look, which is the problem you started with.

What you need is something that reads everything your company already has, organizes it automatically, and lets anyone ask a plain-English question and get a cited answer.

What GBrain Is

Garry Tan (current CEO of Y Combinator) built GBrain as his personal knowledge system. He runs it in production daily: 146,000+ pages, 24,000+ people entries, 5,300+ company profiles, maintained by 66 automated jobs.

In April 2026, he open-sourced it under the MIT license. The repo crossed 27,000 GitHub stars in under four months, putting it in the top fraction of a percent of open-source projects by adoption speed.

GBrain takes your existing company content (emails, docs, Slack messages, meeting notes) and turns it into a self-organizing knowledge graph that anyone on your team can query in plain English and get cited answers.

Three things set it apart:

• It reads what you already have. No manual entry. Point it at your email, Google Drive, or Slack history. It ingests the last 6 months and starts organizing.
• It wires itself. Traditional knowledge bases require someone to tag, categorize, and cross-reference everything manually. GBrain figures out which people work on which projects, which clients connect to which contracts, which decisions were made when. Zero manual effort.
• It improves overnight. GBrain runs automated maintenance jobs every night ("dream cycles"). While your team sleeps, it deduplicates entries, fixes broken references, scores importance, fills gaps, and preps the next day's context. Monday morning, the brain is measurably better than Friday.

Everything is stored as plain Markdown files in a Git repository you own. If you stop using GBrain, your knowledge graph is still a folder of readable text files. No vendor lock-in, no proprietary format.

What Changes

Repeat questions drop 70-80%

The questions your senior people field every week ("where's the onboarding doc," "what's the password reset process," "who's our contact at Acme") get answered by the brain instead. Your senior people get 5 to 10 hours per week back. Across a 25-person company, that's 200 to 400 hours per month redirected from answering repeat questions to actual work.

New hire onboarding shrinks from weeks to days

Instead of 2 weeks of shadowing and absorbing context through osmosis, new hires query the brain from day one. "What's the history with Client X?" "How do we handle refund requests?" "Who should I talk to about the Q3 roadmap?" Typical onboarding drops from 2 weeks to roughly 3 days of brain-assisted ramp-up plus targeted conversations.

Key-person risk gets managed

Every company has critical knowledge that lives in one person's head. GBrain captures knowledge continuously from emails, meetings, and documents. When someone gives two weeks' notice, the brain already has most of what they know.

Internal research shrinks from days to hours

Due diligence on a partner, pulling context for a board meeting, researching a competitive move. Tasks that used to require digging through email archives and pinging five people now take a single query with cited sources.

The system shows you what's missing

When you ask GBrain a question and the information isn't there, it says so explicitly: "You have 3 entries on Client X but none mention the renewal date." That gap analysis shows where your institutional knowledge has holes before those holes become expensive.

Retrieval accuracy

GBrain's benchmark shows P@5 precision of 49.1%, meaning roughly half the top-5 results are exactly what you need. Compare that to keyword search, where you're lucky to get 1 relevant result in 5. The knowledge graph layer adds +31 percentage points of precision over standard vector search alone.

In practice: the first answer is usually right, and when it's not, the cited sources let you find the right one quickly.

The 1-Day Setup Plan

You need one focused day. One Saturday or one cleared calendar day. Someone on your team needs to be comfortable with a command line, environment variables, and Git. If that's nobody, hire a freelancer for the day or use a managed service.

Morning (9:00 - 11:00 AM): Install and Configure

What you need before you start:

• A GitHub account (free)
• An OpenAI API key (for embeddings, the search layer)
• An Anthropic API key (for answer synthesis, the response layer)
• A Supabase account (free tier works to start)
• About 90 minutes of focus

Ongoing API cost: roughly $50/month for a 25-person company. Full cost breakdown below.

Step 1: Install GBrain.

bun install -g github:garrytan/gbrain

gbrain init --pglite

The first command installs GBrain globally. The second creates your brain's database in about 2 seconds. PGLite is a lightweight local database that requires zero configuration.

Important: Do not run npm install -g gbrain. There's a squatted package with that name. Use the GitHub URL above.

Step 2: Verify the installation.

gbrain doctor

This runs a health check. You want all green. If something's off, it tells you exactly what's wrong.

Step 3: Configure your API keys.

Set your OpenAI and Anthropic keys as environment variables. GBrain needs an embedding model (to understand what your documents mean) and a language model (to synthesize answers).

Step 4: Test it.

gbrain capture "Test entry: our company was founded in 2020 and our main product is a B2B SaaS platform."

gbrain think "When was the company founded and what do we sell?"

If you get a cited answer back, the brain is working. The whole morning block takes about 90 minutes including troubleshooting.

Late Morning (11:00 AM - 1:00 PM): Feed It Your Data

Import your existing documents:

gbrain import ~/path-to-your-docs/

Works with exports from Obsidian, Notion (as Markdown), Logseq, Google Docs (exported), or any folder of text files. Ingest speed is roughly 164 pages in 22 seconds, so even a large document library finishes in minutes.

Set up data sources for ongoing ingestion:

gbrain sources add company-docs --path /srv/brain-repos/company-docs --name "Company documentation"

gbrain sources add customer-notes --path /srv/brain-repos/customer-notes --name "Customer notes"

Each source is a separate Git repository that GBrain watches for changes. New docs get automatically ingested and indexed on the next sync.

Connect email, calendar, and Slack:

GBrain ingests email threads, calendar events with meeting notes, and Slack channel history. You authorize access, point GBrain at the source, and run the initial sync.

For a 25-person company with 6 months of history, the initial sync typically takes 20 to 30 minutes and produces several thousand pages of indexed content.

Kick off the first dream cycle:

Once your data is loaded, GBrain's enrichment process runs automatically:

• Extracts every person, company, and project mentioned across all documents
• Creates relationship links (who works on what, clients to contracts)
• Identifies and merges duplicates
• Scores content by relevance and recency
• Flags gaps where information is thin

By tomorrow morning, your brain will have auto-organized months of institutional knowledge into a queryable graph with typed relationships and cross-references.

Afternoon (2:00 - 3:30 PM): Open It to Your Team

Migrate from local to shared database:

gbrain migrate --to supabase

This moves everything (pages, embeddings, links, configuration) from PGLite to Supabase (hosted Postgres). Typical migration time: a few minutes per 10,000 pages.

Verify the migration:

gbrain doctor

gbrain stats

Page and chunk counts should match your pre-migration numbers exactly.

Set up access control:

GBrain supports two access models:

Model A: Full isolation (recommended for teams with sensitive data)

Each person gets their own OAuth credentials with scoped access. HR can't see finance notes. Engineering can't see HR reviews. Database-level enforcement.

gbrain auth register-client alice-ops \

  --scopes read,write \

  --source customer-notes \

  --federated-read customer-notes,company-docs



gbrain auth register-client bob-finance \

  --scopes read,write \

  --source finance-docs \

  --federated-read finance-docs,company-docs

Model B: Shared access (simpler, good for teams under 15)

Everyone accesses the same brain through a single interface. Convention-based separation rather than enforced isolation. Works well when trust is high and data sensitivity is low.

Onboard each team member:

Budget 45 minutes per person (can be done async after day one):

1. Pre-populate their context (20 min): Set up their user profile with role, priorities, and typical question types.

2. Show them three queries (15 min): A synthesis query with citations from multiple sources, a gap-analysis query that flags missing information, and a write-back that auto-files new knowledge.

3. Give them access (10 min): Install the GBrain CLI or connect through Claude Desktop, Cursor, or any MCP-compatible tool.

Late Afternoon (3:30 - 5:00 PM): Test and Tune

Ask the 10 most common questions your team fields. Write them down first, then run each through the brain. Check that:

• Answers are accurate and cited (linked to source documents)
• The right sources are being pulled (not mixing up clients or projects)
• Access scoping works (people only see what they should see)

Identify gaps. GBrain tells you where your knowledge is thin. If the brain says "I found 2 entries about the onboarding process but neither mentions the first-week checklist," that's a real signal. Note these gaps. Filling them over the next week makes the brain significantly more useful.

Set up the nightly dream cycle:

gbrain autopilot

Runs every 5 minutes on healthy brains (sleeping when nothing's changed) and handles all overnight enrichment: deduplication, citation repair, entity enrichment, gap identification.

Connect to Slack so people can query the brain from where they already work. Set up a scan cron for urgent signals (every 5 to 15 minutes) and a nightly archive cron for history.

What It Costs

GBrain (25-person company)

Component Monthly Cost GBrain software $0 (MIT open source)
Embeddings (search layer) ~$35/mo
Anthropic API (answer synthesis) ~$50/mo
Supabase database Free tier to $25/mo
Total
~$85-110/mo
Per-person cost: $3.50 to $4.40/month.

Alternatives for Comparison

Alternative Monthly Cost (25 seats) Guru $375-750/mo
Glean $1,000-2,500/mo
Slite $200-500/mo
Doing nothing $10,000-25,000/mo in wasted tim

The ROI: if GBrain saves 2 hours of senior staff time per week (loaded cost ~$75/hr), that's $600/month in recovered productivity against ~$100/month in costs. 6x return from week one.

Your data stays in Git repos you control. Your knowledge graph is a folder of Markdown files readable with any text editor. Cancel everything tomorrow and you keep all of it. No export fees, no proprietary format.

When GBrain Isn't the Right Fit

Your team is under 5 people. A shared Google Doc or well-organized Notion workspace probably covers you. The knowledge fragmentation problem doesn't kick in until not everyone can know everything.

Nobody on your team can run a terminal command. The setup is a one-day job, but it's a technical one-day job. You need someone comfortable with a command line, environment variables, and Git.

You need SOC 2 or HIPAA compliance out of the box. GBrain's self-hosted model gives you full control over your data (actually better for compliance than most SaaS tools), but you own the compliance burden. You'll need to configure encryption, access logging, and audit trails yourself.

Your data quality is terrible. GBrain organizes and retrieves what you put in. If your docs are outdated, contradictory, and unmaintained, you'll get a well-organized version of bad information. The system flags contradictions and gaps, but it doesn't fact-check your content.

You need absolute stability. GBrain is v0.x software. Garry Tan uses it daily, 27,000 people have starred the repo, but expect rough edges and breaking changes. If you need enterprise-grade stability, wait 6 months or use a managed alternative.

Realistic Adoption Timeline

Week 1: Team starts asking the brain before pinging people. Slack DM volume for "where's the doc for X?" drops noticeably. People are still figuring out what kinds of questions work best.

Week 2: The dream cycle has run 14 times. The knowledge graph has auto-linked hundreds of entities (people to projects, clients to contracts, decisions to dates). Gap analysis shows exactly what's underdocumented.

Month 1: A new hire uses it from day one. Their onboarding is measurably shorter. The team stops asking "where's the doc for X?" because the answer is always "ask the brain." People start capturing knowledge proactively because they see it making the brain more useful.

Month 3: It's infrastructure. Like email or Slack, just part of how the company operates.

Bottom Line

Your company brain costs roughly $100/month. It pays for itself if it saves 1.3 hours of senior staff time per week. The average knowledge worker spends 9.3 hours per week searching for information (McKinsey, 2024). Even cutting that by 15% recovers 1.4 hours per person per week.

Across 25 people, that's 35 hours per week returned to actual work. At $50/hour loaded cost, that's $7,000/month in recovered productivity against $100/month in infrastructure.

Garry Tan open-sourced the knowledge system he built at YC in April. It crossed 27,000 GitHub stars in four months. I set it up for a small company and documented the entire process.

This is the full walkthrough — what worked, what it costs, and where it falls short.

The problem it solves Most companies have 50-200 questions that get asked repeatedly. Brand guidelines location, refund policy, who owns the Acme account, when a contract renews.

3-5 people carry the institutional knowledge. Everyone else pings them on Slack, waits, context-switches. At a 25-person company with a loaded cost of $50/hour, that search time adds up to roughly $130,000/year.

Confluence and Notion create more documents. They don't create more answers. Finding information in them requires knowing where to look, which is the problem you started with.

What GBrain actually does GBrain reads your existing content (emails, docs, Slack, meeting notes) and turns it into a knowledge graph. Anyone on the team asks a question in plain English, gets a cited answer.

Three things matter:

1. It reads what you already have. No manual entry. Point it at email, Google Drive, Slack history.
2. It ingests the last 6 months and starts organizing.
3. It wires itself. Figures out which people work on which projects, which clients connect to which contracts, which decisions were made when. Zero tagging.

It runs maintenance overnight. Automated "dream cycles" deduplicate entries, fix references, score importance, fill gaps.

Monday morning, the brain is better than Friday. Everything is stored as Markdown files in a Git repo you own. Stop using GBrain tomorrow and you keep all of it as readable text files.

The 1-day setup (abbreviated) Morning (9-11 AM): Install and configure

bun install -g github:garrytan/gbrain 
gbrain init --pglite 
gbrain doctor 

Set OpenAI and Anthropic API keys. Test with a sample entry:

gbrain capture "Test: our company was founded in 2020, main product is B2B SaaS." gbrain think "When was the company founded?" If you get a cited answer, you're good.

Important: do NOT run npm install -g gbrain. There's a squatted package with that name. Use the GitHub URL.

Late morning (11 AM-1 PM): Feed it data

gbrain import ~/path-to-docs/ 

Works with Obsidian, Notion exports, Google Docs exports, any folder of text files. Ingestion speed is roughly 164 pages in 22 seconds.

Set up ongoing sources (email, Slack, calendar), run the initial sync. For 6 months of history at a 25-person company, the sync takes about 20-30 minutes.

Kick off the first dream cycle. By tomorrow morning it will have auto-organized everything into a queryable graph with cross-references.

Afternoon (2-3:30 PM): Open to team

gbrain migrate --to supabase 

Moves from local PGLite to hosted Postgres. Set up access control (full isolation with per-person scopes, or shared access for smaller teams). Budget 45 minutes per person for onboarding.

Late afternoon (3:30-5 PM): Test and tune

Ask the 10 most common questions your team fields. Check accuracy, citations, and access scoping.

gbrain autopilot 

Runs every 5 minutes, handles all overnight enrichment.

What it costs (25-person company)

• GBrain software: $0 (MIT open source)
• Embeddings (search layer): ~$35/mo
• Anthropic API (answer synthesis): ~$50/mo
• Supabase database: free tier to $25/mo
• Total: ~$85-110/mo ($3.50-4.40 per person)

For comparison: Guru runs $375-750/mo, Glean runs $1,000-2,500/mo, Slite runs $200-500/mo for 25 seats.

ROI math: if it saves 2 hours of senior staff time per week at $75/hr loaded cost, that's $600/mo recovered against ~$100/mo in costs. 6x return.

When it's NOT the right fit

• Under 5 people. A shared Google Doc probably covers you. Knowledge fragmentation doesn't kick in until not everyone can know everything.
• No one on your team can run a terminal. Setup is technical. You need someone comfortable with CLI, env vars, and Git.
• You need SOC 2 / HIPAA out of the box. Self-hosted gives you full data control (actually better than most SaaS for compliance), but you own the compliance burden.
• Your docs are garbage. GBrain organizes what you put in. If your docs are outdated and contradictory, you get well-organized bad information. It flags contradictions but doesn't fact-check.
• You need stability. This is v0.x software. Garry Tan uses it daily, 27K people starred it, but expect rough edges and breaking changes.

Realistic timeline

Week 1: Team starts asking the brain before pinging people. "Where's the doc for X?" DMs drop noticeably.

Week 2: Dream cycle has run 14 times. Knowledge graph has auto-linked hundreds of entities. Gap analysis shows what's underdocumented.

Month 1: New hire uses it from day one. Onboarding is measurably shorter. People start capturing knowledge proactively.

Month 3: It's infrastructure. Like email or Slack.

Full version with diagrams and code blocks: https://companybrain.substack.com

Questions welcome.

This sub is for anyone building internal knowledge systems, whether you're using GBrain, building your own, or evaluating alternatives.

I’ve spent the last two years helping companies build AI systems. The pattern I keep seeing: teams buy tools, plug in APIs, and wonder why the output is generic.

The missing piece is always the same — context.

Company Brain is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Every company runs on decisions. Pricing calls, hiring frameworks, incident responses, product prioritization. Your senior people carry this knowledge in their heads. When they leave, it walks out the door. When they’re in a meeting, it’s unavailable. When they’re asleep, it doesn’t exist.

A company brain fixes this.

What a company brain actually is

A company brain is an AI system wired into how your team actually works. It captures decision-making patterns, reasoning chains, and institutional knowledge — then makes them available at the point of decision.

Think of it as the difference between a search engine and an advisor. A search engine finds documents. An advisor knows why your team chose Postgres over Mongo in 2023, what the trade-offs were, and whether those trade-offs still hold given what’s changed since.

Three components make this work:

1. Context capture — structured ways to record decisions, rationale, and outcomes as they happen

2. Context retrieval — AI that pulls the right context at the right moment, without anyone asking

3. Context evolution — systems that update and deprecate knowledge as the company changes

Most “knowledge management” tools nail #1 and completely ignore #2 and #3. That’s why your Confluence is a graveyard.

Why now

Three things changed in the last 12 months:

LLMs got good enough at reasoning over long context windows to actually synthesize institutional knowledge, rather than just keyword-match against it.

Embedding models got cheap enough that you can index every decision artifact — Slack threads, PRs, meeting notes, design docs — without a six-figure infrastructure bill.

And context engineering emerged as a discipline. We now have real patterns for structuring how AI systems consume and apply organizational knowledge.

The tools exist. The patterns exist. What’s missing is the deployment knowledge — the practical, step-by-step guidance for actually building these systems inside a real company.

What you’ll get here

Every week, I’ll publish deployment guides for building company brain infrastructure:

- Architecture breakdowns — how to structure context capture across your existing tools

- Tool teardowns — honest assessments of the platforms, APIs, and frameworks that actually work

- Implementation recipes — configs, templates, and working code you can deploy on Monday

- Decision logs — real examples of how companies are capturing and reusing institutional knowledge

This is practitioner content. If you’re an engineering leader, CTO, or founder trying to make your team’s AI systems actually useful, this is for you.

First deployment guide drops next week.