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AI Agents & RAG

AI Agent & RAG Development

Retrieval and agents that ship to production — with output your compliance team can audit.

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Most AI demos fall apart the week they meet real users. The model invents a number nobody can trace, a citation points at a document that says something else, and the thing that impressed everyone in the pitch becomes a liability the moment it's answering customers.

We build the other kind. Our retrieval systems construct citations server-side from the chunks actually retrieved — never parsed from the model's prose — so a poisoned document cannot exfiltrate data across tenants. Our narration layers run behind a grounding guard that rejects any output citing a number the deterministic engine did not produce, falling back to deterministic copy instead of guessing.

That discipline is why we can move fast. Senior architects using AI leverage, with the guardrails written first: every deviation from spec recorded as a numbered ADR, isolation proven by positive-control tests rather than asserted in a README.

The problem we solve

Teams ship an LLM prototype that works in the demo and cannot survive an audit. There's no way to prove the model didn't invent a figure, no way to prove one customer's data can't reach another, and no record of why any of it was built the way it was.

What you get

Hybrid retrieval — dense vectors and full-text, fused with reciprocal rank fusion in a single query
Server-side citations built from retrieved chunk ids, never parsed from model output
Grounding guard that rejects ungrounded numbers and falls back to deterministic copy
Provider-agnostic model layer — swap providers with an env var, no code change
Tenant isolation enforced at the data layer with Postgres RLS, not UI checks
Agent orchestration with checkpointed state, so a failed turn resumes rather than restarts

What we deliver

  • Working RAG or agent system deployed to your infrastructure
  • Numbered ADRs recording every architectural decision and deviation
  • Isolation test suite with positive controls
  • Security review across every surface before handover
  • Runbook and 30-day warranty

Who it's for

  • Founders who need an AI MVP in front of users in weeks
  • Teams whose AI output will be read by regulators, auditors, or customers under contract
  • Companies who tried an LLM prototype and hit the grounding or isolation wall

Indicative investment

Transparent ranges so you can plan. Final scope and quote are confirmed on your scoping call.

PackageFromTimeline
AI Feature Add-on$5,0002–4 weeks
AI MVP Build$5,0004 weeks
Custom Platform$13,0006–10 weeks

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Frequently asked questions

How do you stop the model from making things up?

A grounding guard sits between the model and the user. Every number, percentage, and rule state in the output is checked against the facts the deterministic engine actually produced; anything ungrounded is rejected and replaced with deterministic copy. The CI check imports the same primitives as the runtime guard, so the two cannot drift apart.

Can you prove one tenant can't see another's data?

Yes, at the data layer. Isolation is enforced by Postgres row-level security using a per-transaction session variable with a bleed-guard: an unset variable reads back as NULL and matches no rows, so the failure mode is an empty result, never another tenant's data. The application role cannot bypass RLS and does not own the tables. Every isolation test carries a positive control, so an empty result can't be a broken query passing silently.

How fast can you actually ship?

Three to four weeks for a working system in front of users, four to ten for a multi-tenant platform. Two senior architects with AI leverage — our value is the architecture decisions, code review, integration, and deployment, which is also why the guardrails go in first rather than last.

Which model providers do you support?

Every model call goes through one provider-agnostic layer, so switching providers is an environment variable, not a refactor. That matters when a compliance requirement forces a specific region or vendor after the build has started.

Ready to scope your ai agents & rag project?

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