LLM Integration Services
Production integration of OpenAI, Claude, and Gemini into your products and workflows, behind a provider-agnostic abstraction.
- 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
- OpenAI
- Claude
- Google Gemini
- LangChain
- TypeScript
- Python
- Multi
- Provider routing
- Gated
- Changes pass evaluation
- 3-8 wk
- Typical integration
What LLM integration covers
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 we deliver
Key features and capabilities of our LLM integration work.
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.
How the engagement runs
A clear sequence with a visible output at every step — so you always know where the project stands.
- 01
Discovery & feasibility
We assess the use case, data readiness, and whether AI is genuinely the right tool before proposing a build.
- 02
Prototype & evaluation
A working prototype measured against defined accuracy and cost criteria, so the decision to proceed is evidence-based.
- 03
Production build
Hardening, guardrails, monitoring, evaluation harness, and integration with your systems.
- 04
Monitor & improve
Ongoing quality monitoring, prompt and retrieval tuning, and model updates as the landscape changes.
Technologies & tools
Chosen per project for fit, maintainability, and your existing stack.
- OpenAI
- Claude
- Google Gemini
- LangChain
- TypeScript
- Python
Industry applications
Sectors where we most often apply this service.
- E-Commerce & Retail
- SaaS & Technology
- Healthcare
- Real Estate
- Finance & FinTech
- Education
- Travel & Hospitality
- Professional Services
What's included at each level
Every engagement is scoped to your needs; these tiers show how scope typically grows.
| 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 |
LLM Integration: common questions
Which model should we use?
It depends on the task, latency tolerance, and budget. Often the right answer is several — a capable model for complex reasoning and a smaller one for routine classification. Provider abstraction makes that routing practical.
How do you keep costs predictable?
Context budgeting, caching stable responses, routing simple requests to cheaper models, and per-interaction cost instrumentation with alerting. Cost surprises almost always come from unbounded context growth.
More from AI Development & Consulting
Ready to talk about LLM integration?
Tell us what you need. We reply within one business day with questions or a call slot — and a fixed, written proposal after.