AI Development Services

Discovery comes first. We pin down the decision or task the AI should improve, who uses the output, what a wrong answer costs, and how success will be measured. A feature that drafts support replies has very different tolerances from one that flags fraud, and that difference shapes every later choice.

Next is data readiness. We look at what data exists, where it lives, how clean and current it is, and whether it can legally be used for the purpose. Then we build a narrow prototype against a written evaluation set - real examples with known good answers - so quality is measured rather than judged by a demo. Only when the prototype clears that bar do we harden it for production: authentication, logging, cost controls, fallbacks, monitoring and a release process that re-runs the evaluation on every change.

Each stage is a milestone with transparent pricing, so you can stop or change direction after discovery or the prototype without committing to the full build. You own the code, prompts, evaluation sets and IP, and we sign an NDA before kickoff.

Retrieval-augmented generation (RAG) is usually the right start when the answers live in your documents, policies or product data: a foundation model answers from passages retrieved at query time, so content stays current and sources can be cited. Fine-tuning helps when you need a consistent style, format or narrow skill that prompting cannot reliably produce - it changes behaviour, not knowledge.

Agents fit when the job needs several steps and real actions - looking something up, updating a record, sending a message - with tools exposed through APIs or MCP servers and clear limits on what the agent may do without a human. Classic machine learning remains the better choice for structured, tabular problems such as forecasting, scoring or anomaly detection, where a smaller model is cheaper, faster and easier to explain than an LLM. Many good systems combine two of these.

Evaluation is what separates a demo from a product. We measure accuracy, groundedness and refusal behaviour against your evaluation set, add regression tests for past failures, and track quality in production with sampled human review. Guardrails sit around the model: input filtering, output validation against schemas, permission checks before any action, redaction of personal data, and a human-in-the-loop step wherever a mistake would be costly.

Running cost is driven by tokens, model choice and hosting. We estimate cost per request before launch, then reduce it with prompt and context trimming, caching, routing simple requests to smaller models, and batching background work. Self-hosted open-source models trade per-token fees for GPU hosting and operations effort, which pays off only at steady, high volume or when data must stay in-house.

This page is the overview; each specialism has its own page. LLM development covers model selection, prompting and fine-tuning. RAG development covers ingestion, chunking, retrieval and citation. AI agents development and MCP server development cover tool-using agents and the integrations they call. Computer vision and machine learning development cover image understanding and predictive models, and AI integration services covers adding AI features to an existing product or platform. Most engagements start here with discovery and then draw on whichever of these the problem needs.