AI & LLM Integration (RAG, Chatbots, Automation)

AI features fail when they are bolted on without solid backend engineering: bad retrieval, runaway costs, no evaluation. I build retrieval-augmented generation pipelines with embeddings and pgvector, integrate OpenAI/Anthropic APIs with proper streaming and rate limiting, and wrap it all in the same production-grade backend I use for everything else.

Who this is for

• SaaS products adding an AI assistant over customer data
• Businesses automating document processing or support
• Teams that tried a no-code AI tool and hit its limits

What you get

• RAG pipeline: chunking, embeddings, pgvector search, re-ranking
• Chat assistants with streaming responses and conversation memory
• LLM-powered classification, extraction and summarisation jobs
• Prompt management, evaluation and cost monitoring
• Secure API layer with usage limits per user or tenant

Outcomes

• AI answers grounded in your data, not hallucinations
• Predictable API costs
• A feature customers will actually pay for