LLM Integration Services

Adding an LLM to an existing product is a smaller project than building an AI agent from scratch, and a much easier way to ship real AI value this quarter instead of next year. Most of our LLM integration work is exactly that: a defined feature — summarisation, a chatbot widget, semantic search — bolted onto a product that already has users, without a rewrite.

Model-agnostic by design

We don't default to one vendor. OpenAI's GPT-5 is often the right call for general-purpose reasoning; Anthropic's Claude tends to win on long-context document work; Llama 4 or Mistral self-hosted are the right answer the moment data residency becomes non-negotiable — HIPAA, GDPR, or a client contract that prohibits sending data to a third-party API. We pick the model based on your latency budget, data residency constraints, and cost per query, not based on which API we're most comfortable calling.

Why RAG, not fine-tuning, for most integrations

Retrieval-Augmented Generation (RAG) grounds a general-purpose model in your specific documents, database, or knowledge base at query time — no retraining required, and the model's answers stay current as your underlying data changes. Fine-tuning has its place for narrow, stable tasks where you need a model to consistently produce output in a specific format or tone, but it's slower to iterate on and needs retraining every time your data meaningfully shifts. For most product features — support bots, document Q&A, semantic search — RAG gets you to production faster and stays accurate longer.

Ready to add AI to your product? Get a free LLM assessment → Tell us the feature you have in mind; we'll propose the model, the architecture, and a fixed-price estimate within 48 hours.