LLM integration built for production, not for a demo
Most LLM work stalls in the same place. The prototype convinces everyone, then nobody can tell whether the production version is getting better or worse, what it costs per user, or what happens when the model provider changes something underneath it. Integration is the part that decides whether an AI feature becomes an asset or a support burden.
Book a strategy callWhat you get
- Model and provider selection based on your actual latency, cost and quality constraints — not on benchmarks
- Prompt and context design that survives real user input, including the inputs nobody planned for
- An eval suite, so a change to a prompt or a model version is something you can measure instead of guess at
- Streaming, retries, timeouts and graceful degradation, so a provider outage is a slow response rather than a broken page
- Cost and token instrumentation wired to whatever you already use for metrics
- A written handoff and a working session with your engineers, so the feature does not depend on me
How it runs
- 01
Scope
A call to establish what the feature has to do, what it must not do, and what 'good' looks like in numbers rather than adjectives.
- 02
Prototype
A narrow end-to-end slice against your real data, so the hard parts surface in week one rather than week six.
- 03
Harden
Evals, error handling, observability and cost controls. This is the part that separates a demo from a feature you can leave running.
- 04
Hand off
Documentation, a walkthrough with your team, and a clear line on what to watch after I am gone.
Questions
- What does LLM integration actually involve?
- Connecting a language model to your product in a way that holds up under real use: choosing a model, designing the prompts and the context you feed it, handling failures and latency, measuring output quality with evals, controlling cost per request, and instrumenting the whole thing so you can see what it is doing in production.
- Do I need a fine-tuned model?
- Usually not. Most teams who think they need fine-tuning need better retrieval and better context design first, both of which are faster to build and cheaper to change. Fine-tuning is worth revisiting once you have evals in place to prove it helps.
- How do you stop the model producing wrong answers?
- You cannot eliminate it, so the work is making wrongness visible and bounded: grounding answers in your own data, constraining output format, adding evals that catch regressions before users do, and designing the interface so the feature fails honestly rather than confidently.
- Can you work with my existing engineering team?
- That is the usual arrangement. I work inside your codebase and your conventions, and the handoff is part of the engagement rather than an afterthought.