About

The missing piece of enterprise AI isn't retrieval. It's understanding.

How this started

From a debugging loop to a company

Our founder built his first agents at Atoms (formerly CloudKitchens), running the infrastructure platform team. The plan was simple: give on-call engineers an agent that could debug their own services. It stalled — not because the model was weak, but because the agent didn't know how anything at Atoms actually worked. Which namespaces mattered, how each database was configured, how a cost API call should even be structured. None of it was written anywhere. You learned it by asking someone who'd been there long enough.

The pattern repeated on a second, simpler project — and then the realization: the company had almost no agentic workflows in production, not for lack of trying, but because every useful workflow needed context from five different teams, and none of them had externalized what they knew in a form an agent could use. Humans work around that by asking the expert. Agents can't. They guess — and guessing in production looks like an agent you can't trust.

So he tested whether it was fixable: he built a context layer over Atoms' system of record — orders, menus, stores, brands — that captured not just the schema but the operational reality of how those entities worked. On top of it, a simple search: ask anything, debug a bad order, investigate a misconfigured store. No custom application code. The context layer did the work. That was the moment. BrainBox AI was incorporated in September 2025, and in April 2026 he left Atoms to build it full-time.

Today

What we build

The brain: a structural model of how a business works, kept current by interpreting the events your team sends it, and surfaced as living pages instead of stale docs. Agents query it. People read it. Nobody re-explains it.

Teams across RevOps, fintech, and developer tooling run BrainBox today — some as shared context for their own people, some embedded inside their own agent products with an isolated brain per customer.

Team

Gaurav Nolkha — Founder. Previously led the infrastructure platform team at Atoms (CloudKitchens).

Advised by senior engineers and leaders from Google DeepMind, LinkedIn, Wells Fargo, and Roku. The founding team is growing — see open roles.

How we work

Three commitments

Ground every claim in the model

If the Brain can’t attribute an answer to a source, it isn’t an answer. That bar applies to our product — and to this website.

Earn structure, don’t presume it

Knowledge starts in the wiki and gets promoted into structure when it proves out. Premature schemas are how systems calcify.

Build the interpretation layer

Plenty of companies make retrieval faster. We build the layer that understands — because that’s the part nobody else has.

Security & Trust

How we handle your data

Current state, plainly

  • BrainBox never reads your database rows. Database connectors ingest schema metadata and field properties only — tables, columns, types, relationships. Your records stay in your systems.
  • Nothing is captured passively: events become traces only when a person or an agent explicitly sends them, and traces store interpretations with source attribution — not copies of your raw systems of record.
  • All access is authenticated and org-scoped; brains are isolated per organization — and per tenant, for embedded deployments.
  • SOC 2 certification is in progress. We complete security questionnaires as part of procurement — ask us for current documentation.

Give your agents a brain.

Grounded answers, not guesses — from a living model of your business.

Book a Demo