Integrating LLMs where they actually help.
Getting Claude or GPT calls to work in a demo is easy. Making them robust in production — with retries, fallbacks, evaluation, and cost control — is the actual work. We do that.
Model-agnostic Production-hardened Cost-controlled
What LLM integration actually involves
A production LLM integration is more than a prompt and an API key. It needs: structured output parsing, retry logic, cost monitoring, evaluation harnesses, fallback models, prompt versioning, and response validation.
Most of the nightmare stories about "AI that hallucinates in production" come from integrations that skipped these layers. We build them in from day one.
We work across Claude (default for reasoning-heavy tasks), GPT (default for when cost trumps quality), and open-source (Llama, Mistral) for on-prem or high-volume use cases.
Our integration approach
Model selection
Pick the right model per task. Not every call needs the frontier model.
Prompt engineering
Prompts versioned, tested against eval sets, kept short and deterministic.
Output validation
Structured outputs with schema validation; invalid outputs retried or flagged.
Cost + latency ops
Per-request cost tracking, latency budgets, caching where appropriate.
Ideal for
Good fit
- Products with a specific need for LLM features
- Teams with engineering capacity but no LLM ops expertise
- Wanting model independence (no lock-in to a single vendor)
Not a good fit
- Pure consumer chatbot projects
- Companies needing an internal data science function (different service)
Questions we hear all the time
Which model should we use?
Usually a mix. Claude Sonnet for reasoning, GPT-4-class for creative, open-source for privacy or volume. We pick per call, not per project.
Do you handle vector databases / RAG?
Yes. See our RAG systems page for the specific service.
Explore related topics
Let's see if we're the right fit.
30-minute call. We'll tell you honestly whether we can help — or if someone else is a better fit.
Or email us at contact@unlockmanagement.com