LLM APIs integrated as real software, not tutorial code.
Integrating an LLM API into production software requires proper error handling, cost controls, prompt management, token accounting and fallback logic. I build that infrastructure, not the happy-path demo.
LLM API integration covers connecting to OpenAI, Anthropic, Google Gemini, Mistral and other providers with proper production architecture: retries, error handling, streaming, token management, cost monitoring and prompt versioning.
What this engineering discipline covers
- OpenAI, Anthropic, Google Gemini, Mistral and other provider integration
- Prompt engineering and versioning
- Function calling and tool use
- Streaming responses and server-sent events
- Token accounting and cost monitoring
- Retry logic and rate limit handling
- Fallback and degraded-mode behaviour
How we engineer and deliver
Design the integration
Which provider, which model, what the prompt structure looks like, and how cost is controlled.
Build production-ready
Error handling, retries, streaming and monitoring — not the tutorial version.
Document and handover
Prompt versions, cost estimates and operational runbook.
Have something worth building?
Let's turn the idea into software. Scoped in writing, engineered to specification, and delivered with precision.