A company second brain is an AI knowledge base built over everything your business already knows — Slack, Drive, Notion, email, tickets, CRM — so anyone can ask a question in plain language and get a cited answer in seconds. Cognio Labs designs, builds and secures them: permission-aware retrieval, connectors for the systems no off-the-shelf tool supports, and organizational memory that survives employee turnover.
Currently building this for Coda and several other companies · 100+ agents shipped · fixed-fee phases
30 minutes. No pitch. You leave with a knowledge-system roadmap either way.
AI knowledge management is the practice of using AI — specifically retrieval-augmented generation (RAG) over your own content — to capture, organize and instantly retrieve what an organization knows. Instead of hunting through five tools and asking three colleagues, an employee asks a question and gets an answer assembled from your real documents, with links back to the sources. The old version of knowledge management asked people to maintain a wiki nobody updated. The AI version leaves knowledge where it is created and makes it answerable.
An AI second brain is what that looks like once it covers a whole company: a second brain for your company, not just for you. Personal second-brain systems organize one person's notes. A company second brain — our name for this service — indexes everything the business collectively knows, respects who is allowed to see what, and keeps working when the person who knew it leaves.
Cognio Labs builds these systems end to end: connectors into your stack, the ingestion and embedding pipeline, permission-aware retrieval, the assistant your team talks to, and the API your other AI agents query. Custom code, no-code tooling, or a managed platform — whichever gets the best outcome at the best price for your situation.
Cognio Labs is currently building a company second brain for Coda, alongside similar knowledge systems for several other companies. These are live engagements rather than finished case studies — we don't publish results before they exist. What we will tell you on a call is exactly how the architecture is put together, which trade-offs came up, and what we would do differently for your stack.
Led by Ashutosh Upadhyay, founder of Cognio Labs — an AI operator whose production systems have served 5,000+ users, and a Hugging Face compute grant recipient for work on Indic language models.
RAG and internal knowledge tooling is not a new line for us. We've shipped 100+ agents into production across 10+ countries, and we run four AI products of our own — including DruidX, our agent-building platform, which depends on exactly this retrieval stack. We build these for ourselves before we build them for you.
See our internal-knowledge build for a major universityCompany knowledge is never in one place. The second brain reads all of it — and keeps reading as it changes.
Slack, Microsoft Teams, and shared inboxes — where most decisions are actually made and then lost.
Google Drive, Notion, Confluence, SharePoint, file shares, and scanned PDFs (OCR'd on ingest).
HubSpot, Salesforce, Zendesk, Jira, Linear, GitHub, your product database — anything with an API.
Call transcripts and recorded sessions, transcribed and indexed alongside everything else.
The architecture, in plain terms — six stages from your raw tools to a cited answer inside Slack.
Read-only OAuth connections or service accounts to every system that holds company knowledge — Slack, Drive, Notion, Confluence, email, CRM, helpdesk, code, and internal databases. Nothing is copied out of a system you haven't approved.
An initial backfill, then incremental sync so the knowledge base stays current instead of going stale after month one. Scanned PDFs get OCR, recordings get transcribed, near-duplicates get collapsed, and every chunk keeps a pointer back to its source.
Semantic chunking, embeddings written to a vector store (pgvector, Qdrant, or Pinecone — your call or ours), plus a keyword index. Hybrid retrieval beats pure vector search on the acronym-and-product-name questions real employees actually ask.
Every chunk carries the access rules of the document it came from. Retrieval filters on the asking person's identity, so the AI knowledge base can never surface something that person couldn't already open. Permission-aware from day one, not bolted on later.
Retrieval, reranking, then a grounded answer with links to the source documents. If the answer isn't in your corpus, it says so — an internal assistant that guesses is worse than no assistant at all.
A Slack bot, a web chat, or an in-app assistant, plus an API and MCP server so your other AI agents query the same brain. Your sales agent, support agent, and ops agents stop each carrying their own half-right copy of company knowledge.
We're tool-agnostic. The build path is chosen per company, not per vendor relationship.
Our own ingestion services, hybrid retrieval, rerankers, and an eval set built from your team's real questions. The right call when accuracy, scale, permissions, or an unusual internal system matters.
n8n, Make, or Zapier moving documents into a hosted vector store with an off-the-shelf chat front end. Cheaper and faster when your sources are mainstream and volume is modest.
Where an existing platform already does 80% of the job, we deploy and configure it rather than rebuild it — then extend it with custom connectors for the systems it doesn't cover.
Builds are fixed-fee, typically starting around $8,000 for a first production scope and scaling with the number of systems connected, the permission model, and how much content gets indexed. After launch you pay infrastructure and model usage — not a per-seat licence that grows every time you hire. You get a fixed-fee proposal within 48 hours of the discovery call.
Off-the-shelf enterprise search (Glean, Notion AI, and similar) is the right answer when your knowledge lives entirely in tools they already support and per-seat pricing works at your headcount. It stops being the right answer the moment your most important knowledge sits in a system they'll never build a connector for.
| DIY internal build | Off-the-shelf AI knowledge tool | Cognio company second brain | |
|---|---|---|---|
| Time to first real answers | 3–6 months of engineering time | Days — but only for supported tools | ~4 weeks, across your actual stack |
| Coverage of your systems | Whatever you find time to build | Their connector list, and no further | Anything with an API, including internal databases |
| Permissions | Usually deferred — then painful to retrofit | The vendor's model, take it or leave it | Mirrors your source system ACLs at retrieval time |
| Answer quality | Depends who's still on the team | A black box you can't tune | Tuned against an eval set of your real questions |
| Agent integration | Another project entirely | Limited API, rarely agent-ready | API + MCP — every agent you run shares one brain |
| Cost shape | Engineer salaries plus the roadmap you didn't ship | Per-seat, per-month, forever | Fixed-fee build, then infrastructure you control |
| Who owns it | You | The vendor — your knowledge, their moat | You. The code and the accounts are yours |
AI knowledge management is the practice of using AI — specifically retrieval-augmented generation (RAG) over your own content — to capture, organize, and instantly retrieve what an organization knows. Instead of employees searching folder by folder and tool by tool, the system ingests documents, chat history, tickets, CRM records, and wikis, indexes them semantically, and answers plain-language questions with citations back to the source. The difference from traditional knowledge management is that nobody has to maintain a taxonomy or write the FAQ: the knowledge stays where people already create it, and the AI layer makes it answerable.
A chatbot answers from whatever the underlying model was trained on plus a short script, so it invents plausible-sounding answers about your business. An AI knowledge base retrieves the actual passages from your actual documents first, then writes an answer grounded in them and links to the sources. That grounding is the whole point: answers are checkable, they update the moment the underlying document changes, and when the answer genuinely isn't in your corpus the system says so instead of guessing. A chatbot is a conversation interface; an AI knowledge base is a retrieval system that happens to have one.
Cognio Labs scopes company second brain builds fixed-fee, with a first production build typically starting around $8,000 and scaling with the number of systems connected, the permission model, and the volume of content indexed. Running costs after launch are infrastructure and model usage — a vector store plus embedding and inference calls — not per-seat licensing, so cost doesn't climb every time you hire. You get a fixed-fee proposal within 48 hours of the discovery call, and there are no open-ended retainers.
Roughly four weeks to first answers in production for a focused first scope: two or three high-value sources connected, permission-aware retrieval working, and a chat interface where your team already works. Additional sources, deeper permission models, and agent integrations are added in later phases, each of which is scoped so it ships something usable rather than extending one long project. We deliberately start narrow — a second brain covering three sources that people trust beats one covering twenty that they don't.
No, your data is not used to train models. We build on enterprise API tiers where the provider contractually excludes your inputs and outputs from training, and where policy requires it we deploy models in your own cloud tenancy or self-host open-weight models so nothing leaves your infrastructure. Access is enforced at retrieval: every indexed chunk inherits its source document's permissions and is filtered against the asking user's identity, so the system can't surface anything that person couldn't already open. Connections are read-only by default, secrets are scoped, and every query and retrieval is logged and auditable.
The common set is Slack, Microsoft Teams, Google Drive, Notion, Confluence, SharePoint, Gmail and Outlook, HubSpot, Salesforce, Zendesk, Intercom, Jira, Linear, and GitHub. Beyond that, anything with an API or a database we can read — including internal admin tools and legacy systems that no off-the-shelf product will ever build a connector for. Custom connectors are usually the reason companies come to us rather than buying a seat-based tool: the knowledge that matters most tends to live in the system nobody else supports.
A company second brain is usually the memory layer underneath everything else we build. Browse all AI agent services.
Bring us your tool list and your three most-asked internal questions. We'll map what a company second brain would cover, what it would cost, and what to connect first — whether you hire us or not.
No lock-in, ever: you own the code, the index, and the accounts. Leave anytime and it keeps running.
30 minutes. No pitch. You leave with a knowledge-system roadmap either way.