AI & Agentic AI Engineering

Common projects include AI assistants and copilots, retrieval-augmented search over your documents, AI agents that automate multi-step workflows, document extraction and classification, and AI features inside existing products.

Retrieval-augmented generation (RAG) is usually the best start: the model answers from your up-to-date documents, and changes don't require retraining. Fine-tuning helps when you need a consistent style or specialised outputs. Many production systems combine both.

AI agents are LLM-powered systems that can plan and take actions across your tools, for example updating a CRM, triaging tickets or preparing reports. We build them with permission limits, human approval for high-impact actions and full audit logs.

We are model-agnostic and select models per use case based on quality, latency, cost and data-residency requirements, including options hosted in your own cloud.

We design for least-privilege access, keep data inside your cloud where required, and add logging and guardrails so every AI action is traceable.

We build an evaluation set of realistic questions and tasks with expected outcomes, and run it on every model, prompt or retrieval change, so quality is measured before anything reaches users.