AI Development & LLM Integration

LLM integration into existing products

Adding AI to an existing product is an integration problem before it's an AI problem: where the model sits in your architecture, what context it sees, how structured output flows back into typed code, and what happens on timeout, refusal, or garbage. We build on tool calling, streaming, and structured outputs with model routing that matches each task to the cheapest model that passes its eval — because paying frontier-model prices for classification is how AI budgets die.

Your existing backend and data stay where they are. We integrate around them.

RAG systems over your data

Retrieval-augmented generation is the difference between a model that improvises about your business and one that answers from your documents with citations. The engineering lives in the pipeline: ingestion and chunking tuned to your document shapes, embeddings and hybrid search (vector plus keyword), reranking, and context construction that respects token budgets. Access control is enforced at retrieval time — a RAG system that leaks across permission boundaries is a breach, not a feature.

We measure hallucination instead of promising its absence: groundedness checks and retrieval-quality evals run continuously, so answer quality is a number you watch, not a vibe you hope.

AI agents and agentic workflows

Agents are systems that decide and act — which makes them systems that fail in new ways. We build agentic workflows on LangGraph and the Model Context Protocol (MCP): explicit state machines rather than free-running loops, tools with typed contracts, human approval gates exactly where your process needs them, and full observability of every step, token, and decision. Recoverable, auditable, and bounded — because an agent you can't replay is an incident you can't explain.

Evaluation: how AI features earn production

Every AI feature we ship carries an evaluation suite: golden datasets built from your real cases, LLM-judge scoring with human calibration, and regression runs wired into CI so a prompt edit or model upgrade can't silently break behaviour that worked. Accuracy is measured before launch and monitored after — cost and latency tracked alongside, because a feature that's right but slow and expensive still fails.

This is the discipline that separates production AI from demos, and it's why we lead with it rather than hiding it in the fine print.

Self-hosted and open-weight models

When data cannot leave your infrastructure — banking, healthcare, government-adjacent work — we deploy open-weight models (Llama family) on your hardware or VPC via vLLM, with the same evaluation discipline as API models. You trade some capability for control; we quantify that trade on your actual tasks so the decision is made with numbers, not anxiety.