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AI Agents Internal Atlas

The retrieval and memory layer under everything else we build

The retrieval layer under Sentinel and Cartway. Typed, versioned sources with a freshness policy and a tenant boundary enforced at query time, and provenance on every passage returned.

Internal. Documented because it explains why the products above cite their sources.

38ms p95 retrieval latency
100% Passages carrying provenance

The problem

Retrieval quality collapses quietly as a corpus grows. Stale documents outrank current ones, access control leaks across tenants, and nobody notices until an answer is confidently wrong.

Our approach

We treat every source as typed and versioned, with an explicit freshness policy and tenant boundary enforced at query time rather than in application code. Every returned passage carries provenance, so any answer built on it can cite its way back to the origin document.

Sources are typed and versioned. Freshness policy and tenant boundary are enforced when the query runs, not at ingestion, and every passage returns carrying provenance back to its origin document and version.

This is internal infrastructure. We are documenting it here because it explains why our other products behave the way they do.

Why it is not a product

Retrieval is not a market we want to compete in. It exists because the alternatives made tenant isolation an application concern, and we were not willing to ship that.

The practiceAgent systemsFor teams that need software to act on real systems unattended, and to be able to prove what it did.AI AgentsThe note behind itAn agent is a workflow, not a chat loopMost agent reliability problems are not model problems. They are state management problems wearing a costume.AI AgentsNext productThe design system every GalvoLab product is built fromInternal. Handed over with the code on every engagement, so a client inherits the system rather than screenshots.Design

Working on something like this?

We take on a small number of engagements at a time. Bring the problem.

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