AI Development

AI that earns its place in your product

Most AI projects stall somewhere between an impressive demo and a product people rely on every day. The demo works on ten hand-picked examples; the real world brings messy documents, ambiguous questions, permissions, costs and users who need to trust the answer. We focus on closing that gap. That means clean data pipelines, retrieval that reliably finds the right information, evaluation you can measure, and the essential engineering around access control, logging and spend.

We are model-agnostic. Depending on the job we use Claude, OpenAI, open-source models or managed platforms such as AWS Bedrock, and we design so you can switch providers later without a rewrite.

Who this is for

• Product teams adding summarisation, smart search, classification or generation to an existing app.
• Operations teams buried in documents such as invoices, contracts, claims or applications.
• Founders who need a credible AI product built properly, not a prototype held together with prompts.
• Companies with internal knowledge spread across wikis, tickets and drives that nobody can search well.

How we make AI reliable

Every project starts with a test set: real questions and documents with known good answers. We measure accuracy before and after each change, so improvements are proven rather than assumed. Answers are grounded in your sources with citations, sensitive data is filtered before it reaches a model, and every request is logged so you can see what the system did and why. We also tune for cost and speed, using caching, smaller models where they are good enough, and prompt optimisation, so running costs stay predictable as usage grows.

What you receive

• A production-ready AI feature or application, integrated with your systems and sign-in.
• An evaluation suite and dashboard tracking quality, latency and cost.
• Admin controls for your team to manage content, prompts and access.
• Documentation and a handover session, with optional ongoing support.

Common questions

Will our data be used to train public models? No. We use provider settings and enterprise endpoints that exclude your data from training, and we can deploy within your own cloud account when required.

How long does a first version take? A proof of concept on your real data usually takes a few weeks. We agree the scope and success measures before we start, and you see working results early.

What if the AI gets something wrong? We design for it: confidence thresholds, human review for high-stakes actions, citations so users can check answers, and monitoring that flags drops in quality.