Build AI Agents, RAG Pipelines & Intelligent Systems That Deliver Real Results

With a problem that has a measurable cost today and enough data to work from. We would rather scope one narrow use case you can evaluate honestly than a broad programme nobody can judge. Our AI readiness assessment, Jumbi, exists to make that first call properly.

An API is faster to reach a working product and shifts the operational burden. Self-hosting matters when data cannot leave your environment, when cost at your volume outgrows per-token pricing, or when you need a model you control. We size both against your actual volume rather than assuming.

Usually not first. Retrieval and prompt design solve more problems than expected, and they are far cheaper to iterate. Fine-tuning earns its place for consistent formatting, a specialised domain vocabulary, or shrinking a model to cut cost - after you have a baseline to compare against.

With a graded evaluation set built from your real inputs, run on every change, so a prompt or model swap shows its effect before release rather than after. Without that, "it seems better" is the only available measure, and it is not one.

Ground answers in your own sources, constrain scope, validate output structurally where it feeds another system, and design the human checkpoint deliberately. Where being wrong is expensive, the design should make a human the last step, not a fallback.

Redact before the call, use providers and tiers that contractually exclude training on your data, or keep the model inside your own environment. Which of those applies depends on your obligations, and we scope it before any data moves.

Inference cost scales with volume, context size and model choice, and it is easy to design something that works in a demo and is uneconomic in production. We model that cost during design, not after.

Yes. Most of the work in an AI project is the data path and the integration around the model rather than the model itself, and that is where we spend the effort.