Customer Use Cases

How teams deploy a brain

Two kinds of buyer run BrainBox: teams that use it directly as shared context, and engineering teams that embed it inside their own agent products. Customer names and measured results are published as customers sign off.

Shared GTM Context
Solution details →

One brain across the revenue org

The problem it solves
Sales, CS, and Support each hold a partially-current view of the same accounts, assembled from calls, tickets, and email — and reconciled by hand in pipeline reviews.
How it's deployed
Every customer touchpoint is interpreted once against a shared brain and written to a single living account brief. Each team reads the same page; the page rewrites itself as new events are sent.
In the field
A RevOps platform runs this pattern across its internal GTM teams, with the Brain built from their CRM schema and account docs.
Embedded · Multi-Tenant
Solution details →

A brain per customer, inside your own product

The problem it solves
Agent products serve many end customers, each with different data, vocabulary, and rules. One shared model risks cross-contamination; no model means the agent guesses.
How it's deployed
Each end customer gets an isolated brain, provisioned through the SDK. At query time the agent specifies which tenant to ground against; isolation is enforced by the platform, and every customer still gets a real model of their own business.
In the field
A fintech company runs this pattern inside its financial reconciliation agent. A full case study with this customer is in preparation.
Text-to-SQL Grounding
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SQL generation that knows the business

The problem it solves
Text-to-SQL failures are rarely syntax — they’re semantics. The agent picks a plausible column and answers a different question than the one asked, because the meaning behind the schema was never available.
How it's deployed
Schema metadata and supporting docs are connected into one brain. Queries resolve business vocabulary and rules — what "active" means, which table backs which entity — before touching the schema.
In the field
A text-to-SQL platform runs this pattern on its query-generation path, grounding generation in customer-specific definitions.

Which pattern fits your stack?

Walk us through it and we'll tell you honestly.

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