Deliverable 01
A regression-testable eval suite.
Fixtures, rubrics, scoring, and a CI job that blocks a merge when quality drops. Includes the queries you don't want to lose to.
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LLM Integration
Chat, RAG, and the wider integration stack — measured, regression-tested, and cost-aware from the first prompt.
The gap between a chat demo and a chat product is mostly measurement. Everything we do in this service — the eval spine, the regression bars, the provenance UX, the runbooks — is a version of “make the thing you can’t see visible”, so you can ship the next change without holding your breath.
We work in Ruby, Python, and TypeScript, on the provider your platform team has already picked. We don’t have a favourite framework. We do have a favourite pattern: measure before you tune, tune before you switch, switch before you rewrite.
When to bring us in
Signals we're the right fit.
- You've stood up a proof-of-concept RAG and quality has been drifting since.
- Your team is happy with the demo answers and nervous about the answers you haven't seen yet.
- Prompts live in five files across three services and nobody knows which one is live in prod.
- You know your hallucination rate anecdotally but not numerically, and finance has started asking.
- You want structured outputs from the model and get JSON that parses two times in three.
How we approach it
The moves that repeat.
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Start from the answers you want to see.
We draft a hundred representative queries and the answers they should produce before touching a prompt. That set becomes the eval spine; the rest of the engagement is measured against it.
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Retrieval before generation.
Chunk with structure in mind, embed with a model you can afford to re-run, layer a re-ranker where the quality lift pays for itself. Provenance in the answer surface — every citation clickable, every claim traceable.
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Prompt as code.
Prompts in the repo, versioned, tested against the eval set on every change. Structured outputs enforced with schemas and a repair loop that doesn't loop forever.
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Guardrails you can defend.
Input classification where the domain requires it, output policy checks, PII handling on the way in and the way out. Documented and testable, not a hopeful sentence in the README.
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Cost, latency, quality — all three.
A budget line for each. Dashboards that show where they're going. Alerts that fire before the CFO does.
What you get
Deliverables, in plain English.
Deliverable 02
A production-shape integration.
Retrieval, prompt, guardrails, structured output validation, observability, cost tracking. Vendor-abstracted where useful, vendor-native where cheaper.
Deliverable 03
A runbook.
What to do when quality drops. What to do when latency spikes. What to do when a provider takes down an endpoint at 03:00. Written for a person on call, not for the shelf.
Engagement models
Shapes the work comes in.
DEEP-DIVE
RAG deep-dive engagement.
Six to ten weeks. We come in on a live RAG that's underperforming, we leave with an eval suite, an upgraded stack, and a runbook.
PLATFORM
LLM platform build.
For teams standing up a shared integration layer. Prompt management, eval infrastructure, guardrail library, cost/quality dashboards.
Related services
Often composed with this one.
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Agentic Systems Engineering
Multi-step LLM workflows with clean topology, tool surfaces, memory, evaluation, and safety — built to land in production, not stall in a demo.
S5 · Advise
Technical & Solutions Architecture
A senior architect embedded with your programme — end-to-end design, build-vs-buy calls, vendor evaluation, technical risk review.
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Bespoke Software Development
Full production software delivery, usually with AI at the core — fixed team, named engagement lead, no staffing pyramid.
Ready to talk about a llm integration engagement?
A 30-minute discovery call. We'll get to the shape of the work and whether we're the right fit.