Your AI is only as trustworthy as what it retrieves.

We audit, govern, and monitor your RAG retrieval layer so the wrong documents never reach your LLM.

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Who This Is For

Healthcare & Clinical AI Teams

Your RAG system pulls from clinical guidelines, research, and patient records. What gets retrieved and what gets blocked has real consequences. We build the governance layer that keeps it auditable and safe.

Legal, Finance & Compliance Teams

Regulated industries can't afford retrieval failures. If your AI surfaces outdated policy, wrong jurisdiction guidance, or unverified sources, that's a liability. We define trust tiers and monitoring so you can prove what your AI did and why.

Engineering Teams Going to Production

You built a RAG pilot that works. Now legal and security want answers before going live. We run the retrieval audit, identify what's flying under the radar, and wire in the governance controls your stakeholders need to sign off.

What We Do

RAG Retrieval Audit

  • •We test what your AI actually retrieves from your knowledge base — what surfaces, what gets blocked, and what shouldn't be there at all
  • •Plain-language report — not just logs, but what it means for your use case
  • •One-time engagement — good starting point before any governance work

Trust-Tier Architecture Design

  • •Define which sources are trusted, conditionally trusted, or blocked for your specific use case
  • •Policy multiplier strategy — what signals elevate or demote a source
  • •Governance deliverable — not code, a strategy document your compliance team can work with

RAG Pipeline Integration

  • •Wire governance controls into your existing RAG stack
  • •Configure retrieval tiers and validate ALLOW/REVIEW/BLOCK outputs before they reach the LLM
  • •React/Node/API implementation — your stack, not a new one

Governance Dashboard

  • •A readable interface showing policy decisions, rank movement, and blocked signals
  • •Built for the non-technical stakeholders who need to see what the AI is doing
  • •A deliverable owned by you, not a SaaS subscription

Retrieval Monitoring Retainer

Monthly audits against your live knowledge base. Catches drift, new untrusted documents creeping in, and tier anomalies over time — before they affect your AI outputs.

  • •Monthly retrieval audit against live knowledge base
  • •Drift detection — catches trust degradation before it causes problems
  • •Report delivered to your team each cycle
Recurring

How It Works

01

Scoping

We learn your RAG stack, your knowledge base, your use case, and what compliance or governance requirements apply.

02

Audit

We run your retrieval pipeline and surface what's happening — what ranks, what blocks, what gets through that shouldn't.

03

Architect

We define your trust tiers, policy rules, and governance structure. You get a deliverable your team can act on.

04

Build or Monitor

We wire in the controls, build the dashboard, or set up ongoing monitoring — depending on what you need.

Know What Your RAG Is Actually Retrieving

Most teams don't find out their retrieval layer has a problem until it causes one. Let's look at it before that happens.

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