BrainBox gives your company a brain — a living model of the entities, processes, rules, and people that make your business run. Built from your schemas and docs, kept current by the notes, emails, and threads your team sends it — so any agent you point at it answers from how things actually work.
RAG returns the closest-matching passage — and stops there. It doesn't know that “grace period” has a precise meaning in your billing policy, that a renewal hinges on a budget cycle nobody wrote down, or that one churn signal should pull in three different teams. Today that understanding lives in a few experts' heads and gets re-explained in every onboarding, every handoff, every prompt. BrainBox gives it a permanent home.
And the Brain sharpens with use — corrections are reviewed and folded back in, so this month's tenth answer is better-grounded than its first.
Tag @brainbox in Slack to send a trace directly — no pipeline to build. See all integrations
Every customer touchpoint is interpreted once and lands in one live account brief — so a rep, a CSM, and a support agent stop reconciling notes and start reading the same page.
Agent products serve many customers with hard tenant boundaries. Each customer gets an isolated brain; the agent selects the tenant at query time through the SDK.
A schema says what columns exist — not that "active" means status = 2 with no churn date. SQL generation grounds against business vocabulary and rules before it touches a table.
BrainBox is infrastructure, not only a dashboard. If you're shipping an agent product of your own, give each of your customers their own brain — isolated, queryable, and yours to embed.
The same primitives our hosted MCP tools use, callable from your backend.
Scaffold a project, push a schema, inspect the Brain — without leaving the terminal.
A separate, isolated brain for each of your customers, selected at query time.
The hardest part of shipping a reliable agent isn't building it — it's knowing whether it's right. Because the Brain holds expert-validated structure, it's the natural source for the golden datasets you evaluate against. Evals grounded in the Brain are on our roadmap — it's the problem that started this company.
Context windows are big enough to hold your docs — but a pile of documents isn’t understanding. The model re-reads everything on every call (slow, expensive), has no way to resolve contradictions between a stale doc and a current one, and produces answers nobody can attribute to a source. The Brain does the interpretation once, gets it reviewed by your experts, and serves the resolved answer — with provenance — to every call after that.
RAG retrieves passages; it doesn’t resolve meaning. It can’t tell your agent that "active" means status = 2 with no churn date, or that two systems call the same customer by different names. The Brain is a structured, curated model — entities, relationships, rules, vocabulary — with source attribution on every fact. Many teams run both: RAG for documents, BrainBox for understanding.
Connectors give an agent access to raw files at question time — every conversation re-reads, re-derives, and re-guesses, and nothing it figures out survives to the next session. Access isn’t understanding. BrainBox holds the already-interpreted model of your business, curated by your team, and serves it to Claude (and everything else) as one MCP tool call instead of a folder crawl.
Agent memory is per-user and per-tool: what your Claude learned, your teammate’s Cursor never sees, and none of it is reviewed by anyone. The Brain is the opposite — one shared, structured model of the business, curated by your experts, versioned, and attributed. Memory is what one agent picked up along the way; the Brain is what your company actually knows.
No. A vector database stores embeddings and answers "what text is similar to this?" — it’s retrieval infrastructure. The Brain is a typed model: entities with properties and join paths, governing rules, vocabulary mapped to real fields, every fact attributed to its source. There’s no similarity search standing in for meaning.
Enterprise search tools index what your company wrote down and help people find it. BrainBox models how your business works — including the rules and definitions no document states — and serves that model to agents over MCP. The output isn’t a ranked list of documents; it’s a grounded answer with the source that backs it.
Teams that try usually get a first version working: a context doc, some prompt templates, a lookup tool. The hard part is everything after — keeping it current as the business changes, resolving conflicts between sources, attributing facts, isolating tenants, and serving it to every agent consistently. That’s a product, not a sprint. BrainBox is that product, and your team’s effort goes into curating the model, not maintaining the plumbing.
No — that’s much of the point. The Brain is served as one MCP server, so Claude, Cursor, Codex, and your own orchestrator all query the same model and get the same grounded answer with the same attribution. When an expert corrects a definition, the correction lands everywhere at once.
Re-run the connector, or send the update as an event — a schema migration, a revised policy doc, a decision made in a Slack thread. The change enriches the existing brain rather than rebuilding it: affected entities and rules update, conflicts resolve by source authority, and Wiki pages that depend on them rewrite with the change recorded in their history.
Your systems of record stay yours. For databases, BrainBox ingests schema metadata and field properties only — tables, columns, types, relationships — never the rows. For docs and events you explicitly send, it stores the interpretation with a pointer back to the source, not a copy of the raw content. Brains are isolated per organization, and per tenant for embedded deployments.
See your own business modeled in a live demo — or connect a source yourself and watch the Brain build.