Eval first
Built the test harness on twelve years of anonymised case data, before any production code.
Public-sector SaaS platform. Shipped a RAG agent for case officers.
Shipped a RAG agent for case officers, built to cut case-prep time.
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This is a representative engagement pattern, not a specific named client. The metrics listed are what this kind of work is measured on, not results. We share named references on a private intro call, with the client’s explicit permission.
Engagement · 4 months to GAPublic sector SaaSAI in production
Illustrative
Hours
Officer hours saved
Prep
Case-prep time
Citations
Answers with sources
Challenge
A public-sector SaaS team wanted to ship an AI agent for case-officers, but couldn't afford the kind of incident a bad AI deployment would cause.
Approach
- Eval harness built before any production code, against twelve years of historical case data.
- RAG architecture with retrieval citations, refusal logic, and explicit policy guardrails.
- Agent embedded inside the existing case-management UI, not a separate tool.
Outcome
Designed to cut case-prep time for officers, with every answer cited and policy gates checked before anything reaches a user.
Brief us on your engagementHow we ran it
The shape of the engagement.
RAG + guardrails
Retrieval pipeline, citation requirements, refusal logic, explicit policy gates.
Embed & pilot
Agent embedded in the existing case UI, pilot with a controlled cohort of case officers.
GA rollout
Phased rollout to all officers with weekly eval and feedback review.
Services involved
Capabilities we deployed.
Stack we shipped with
The tools that did the work.
Anthropic Claude
OpenAI
Pinecone
LangGraph
PostgreSQL
Datadog
AWS
Vault
What we measure
Public-sector SaaS platform · the scorecard.
Metrics agreed in week one, reviewed weekly with the sponsor and reported quarterly to the exec team.
- Business metrics, not vanity metrics.
- Multiple capabilities, one accountable team.
- Stack chosen for fit, not familiarity.
Hours
Officer hours saved
Prep
Average case-prep time
Citations
Answers with sources
Policy
Policy gate results
"RAG-backed agent over years of case-law, trained against an evaluation harness and deployed inside the existing case-management product."
More work
Other engagements worth a look.
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