Adding an LLM to your product isn't just an API call. It's prompt engineering, context management, token optimization, error handling, streaming, caching, and cost control. Get any of those wrong and you ship something that's slow, expensive, or unreliable.

I build the AI/LLM integration layer that sits between your application and the language models. Think of it as the middleware that makes AI actually work in production: structured inputs, validated outputs, graceful failures, and predictable costs.

LLM abstraction layer — unified interface across OpenAI, Anthropic, Gemini, or open-source models with automatic failover

Prompt management system — versioned prompts, A/B testing, and structured output parsing

Streaming & real-time responses — server-sent events or WebSocket streaming for chat-style interfaces

Context window management — smart truncation, summarization, and sliding window strategies for long conversations

Token & cost optimization — caching, batching, model routing (use GPT-4o for complex tasks, lighter models for simple ones)

Structured output parsing — JSON mode, function calling, and validation to get reliable structured data from LLMs

Evaluation pipeline — automated testing to measure output quality and catch regressions

OpenAI API, Anthropic API, Google Gemini, LangChain, LlamaIndex, Vercel AI SDK, Next.js, Node.js, Python, PostgreSQL, Redis.

Engineering teams adding LLM features who want the integration layer done right from the start

Products that have a basic ChatGPT integration and need it hardened for production scale

CTOs who want a clean AI abstraction layer their team can build on top of