Custom AI development: AI built on your data and tested before it ships

Budget for two things: the build and the running costs. The build is driven by how many systems the AI connects to, how much data must be cleaned and indexed, how high the accuracy bar is, how polished the interface needs to be, and your compliance requirements. Running costs scale with usage: model fees per request, hosting, and search infrastructure. We estimate both during discovery, test cheaper models wherever they meet the accuracy target, and set spend limits so monthly costs stay predictable.

A focused prototype typically takes 2–4 weeks, and a production system 2–4 months, depending on integrations, data preparation, and the accuracy bar. Discovery comes first, usually 1–2 weeks, to test whether the idea works on your data at all. After that, every 2-week cycle ends with a demo, so you can stop, adjust, or expand based on real results instead of a long specification.

Not when the system is built on business-grade services. The paid APIs from OpenAI, Anthropic, Google, AWS, and Microsoft exclude your inputs and outputs from model training by default, and some offer limited or zero data retention for eligible uses. When data can't leave your environment, we deploy open-weight models in your own cloud account or on your own servers. Either way, the system retrieves only the documents the person asking is already allowed to see.

Usually not. Most business applications get better results, sooner and for less money, from clear instructions, retrieval over your documents, and well-designed tools. Fine-tuning earns its place when you need a consistent format or tone at high volume, or a smaller, cheaper model to match a larger one on a narrow task. We decide with evals: if a fine-tuned model doesn't beat the baseline on your test set, it doesn't ship.

Start with whatever meets your accuracy target at the lowest total cost and risk. Hosted models from OpenAI, Anthropic, and Google are usually the most capable and the fastest way to launch. Open-weight models you run yourself give you more control over data, vendor lock-in, and cost at very high volume, but you take on hosting and upkeep. Many systems use both, and we design so you can switch models without a rewrite.

We measure it. Before building, we assemble an evaluation set of real questions or documents from your business with known correct answers. Every version of the system is scored against it for accuracy, grounding in your sources, and safe behavior, and we agree on the passing bar with you before launch. After launch, we sample live answers, collect user feedback, and add each failure to the test set so a repeat gets caught.

An AI system needs operating, not just hosting. Models get updated or retired, your documents change, and users ask questions nobody predicted. We monitor accuracy, cost, and response times, re-run the eval suite whenever a model or prompt changes, and review flagged answers. You can keep us on a support plan, or we hand over the code, documentation, and evals so your own team can run it.