LLM Integration
Production integration of OpenAI, Claude, and Gemini into your products and workflows, behind a provider-agnostic abstraction.
- Multi
- Provider routing
- Gated
- Changes pass evaluation
- 3-8 wk
- Typical integration
About llm integration
Calling a model API is straightforward. Running one in production is not. The difficulty is in the surrounding system: handling latency and failure, controlling cost, keeping quality stable as prompts evolve, and preventing misuse.
Provider abstraction from the start
Model capability and pricing change every few months. We put an abstraction layer between your application and the provider so you can switch or route between models without rewriting application logic.
Cost control as a design concern
Token cost scales with usage in ways that surprise teams at production volume. We control context size deliberately, cache aggressively where responses are stable, route simple requests to smaller models, and instrument cost per interaction so it is visible before it becomes a problem.
Quality that does not drift
Prompt changes have non-obvious side effects. Every change is evaluated against a maintained test set before release, which prevents the common pattern where fixing one behaviour silently breaks another.
What the engagement includes
Provider abstraction layer
One interface across OpenAI, Claude, and Gemini with routing.
Prompt engineering & versioning
Prompts treated as versioned artefacts, not scattered strings.
Caching & cost controls
Response caching, context budgeting, and model routing by complexity.
Evaluation & regression testing
Maintained test sets gating every prompt or model change.
Streaming & failure handling
Streamed responses with retries, timeouts, and fallbacks.
What you get
The measurable results this service is accountable for.
- Switch or route between providers without rewrites
- Cost per interaction visible and controlled
- Prompt changes gated by evaluation
- Graceful handling of latency, rate limits, and failures
- Guardrails against misuse and prompt injection
A process without surprises
Clear checkpoints at every stage, so you always know what is shipping and when.
- 1
Discovery & feasibility
We assess the use case, data readiness, and whether AI is genuinely the right tool before proposing a build.
- 2
Prototype & evaluation
A working prototype measured against defined accuracy and cost criteria, so the decision to proceed is evidence-based.
- 3
Production build
Hardening, guardrails, monitoring, evaluation harness, and integration with your systems.
- 4
Monitor & improve
Ongoing quality monitoring, prompt and retrieval tuning, and model updates as the landscape changes.
What is included at each tier
Engagements scale with your stage. Every tier includes everything below it.
| What's included | Starter | Growth | Enterprise |
|---|---|---|---|
| Integration architecture | Included | Included | Included |
| Provider abstraction & routing | Included | Included | Included |
| Prompt versioning | Not included | Included | Included |
| Evaluation harness | Not included | Included | Included |
| Cost & quality monitoring | Not included | Not included | Included |
Sectors we work in
We build behind a provider abstraction with cost instrumentation and evaluation gating, so the integration survives model and pricing changes.
Common questions
Which model should we use?
How do you keep costs predictable?
More ai development & consulting services
Ready to talk about llm integration?
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