From AI Demos to Production Features
Everyone's seen ChatGPT demos. But turning LLM capabilities into reliable, production-grade features is an engineering challenge that requires experience with prompt engineering, retrieval-augmented generation, model selection, and robust error handling.
We've integrated LLMs into production applications serving thousands of users. We know what works and — more importantly — what doesn't.
Our LLM Integration Services
RAG System Development
Retrieval-Augmented Generation grounds LLM responses in your proprietary data. We build RAG pipelines that ingest your documents, chunk and embed them, and retrieve relevant context for accurate responses. No hallucinations about your products or services.
Prompt Engineering & Optimization
The difference between a good and great LLM feature is often in the prompts. We design, test, and optimize prompts for consistency, accuracy, and cost efficiency.
Model Selection & Fine-Tuning
GPT-4, Claude, Llama, Mistral — each model has strengths. We help you choose the right model for your use case and budget, and fine-tune when necessary for domain-specific accuracy.
Production Infrastructure
Streaming responses, fallback models, cost monitoring, response caching, content moderation, and observability — all the infrastructure needed to run LLMs reliably at scale.