Ideas in. Intelligence out.

It depends on scope, not on a rate card. The main cost drivers are how many workflows the system covers, how ready your data is, which systems it must connect to, the model and its usage costs, and your security or compliance needs. On a discovery call we go through them with you and agree the scope before building.

It depends on scope and on how ready your data is. Every project moves through the same steps: discovery, prototype, pilot, production, then ongoing improvement. The prototype comes first, so you see the idea working on your own data early, and the timeline for each later step is agreed before that step starts.

Not always. Applications built on large language models (LLMs) can often start with your existing documents and a few dozen good examples, because the model is already trained. Custom machine learning models, such as demand forecasting or visual inspection, usually need more labeled historical data. Discovery is where we check what you have against what the use case needs.

Yes. Most of the work is connecting AI to what you already run: databases, document stores, CRMs, ERPs and internal APIs. We build those integrations and add AI inside the workflows your team already uses, instead of asking people to switch to a new tool. Discovery maps which systems the first version needs to reach.

A prototype shows the idea working on a sample of your data, usually without production integrations. A pilot puts a working version in front of a small group of real users, inside their actual workflow, and measures it against the success metrics agreed in discovery. The pilot result decides whether, and how, to move to production.

A short description of the problem, an idea of what success looks like, and sample data or documents if you have them. It also helps to have one person who knows the workflow well and can answer questions during discovery. Book a discovery call and we will take it from there.