LLM Integration Services
Calling an LLM API is easy. Running it in production — with cost under control, latency acceptable, and failures handled gracefully — is the actual engineering problem. That's what we integrate.
What's included
- Provider and model selection — OpenAI, Anthropic Claude, or open-source (LLaMA, Mistral)
- Prompt architecture, structured output, and function/tool calling
- Evaluation harnesses so quality regressions are caught before users see them
- Cost controls — caching, model routing, and token budgets
- Streaming, timeout, and fallback handling for production reliability
- Observability into every LLM call — latency, cost, and failure rate
Where this fits
LLM integration is usually one layer of a larger system. If the model needs to answer from your own data, that's a RAG pipeline problem. If it needs to take multi-step actions, that's an agent problem — part of our broader AI development service.
How we work
Weekly sprints with working software every week. Most LLM integrations reach a first production release in 4-8 weeks, instrumented so you can see cost and quality before you scale traffic to it.
Deciding between a hosted model, fine-tuning, or building in-house versus hiring a partner? Read AI Development Agency vs. In-House vs. AI Code Tools.