Where this fits
You want AI inside your product
Your customers would benefit from an assistant, summaries or smart search in the software they already use.
You have slow document work
People read, sort or extract from documents by hand, and the work follows a pattern.
Your prototype is not ready
It impresses in a demo but gives uneven answers, costs too much or cannot be trusted with real data.
What we build
- Assistants and chat
- Assistants that answer from your own content and can say when they do not know.
- Search over your data
- Retrieval that respects who is allowed to see which documents.
- Document processing
- Extraction and classification, with people reviewing the cases that need them.
- Agents and workflows
- AI steps that call your systems, with limits on what they may do and a record of what they did.
- Evaluation
- Test sets that measure answer quality, so a change can be judged before it ships.
- Cost and monitoring
- Usage limits, logging and alerts, so spending and quality stay visible.
What AI development services cover
Most business AI projects today do not train a model from scratch. They connect an existing model to your data and workflow, then do the engineering that makes the result reliable: retrieval, permissions, evaluation, monitoring and fallbacks.
A good first question is whether the task needs AI at all. If a rule or a report solves the problem, that will be cheaper and more predictable, and we will say so.
How an AI project moves from prototype to production
The prototype answers whether the idea can work. The remaining steps decide whether people can rely on it.
- Collect real examples
- Build a test set from real questions and documents, including difficult ones.
- Measure a baseline
- Score the first version so every later change can be compared with it.
- Add guardrails
- Limit the data and actions available, and decide when a person must approve.
- Release gradually
- Start with a small group, watch quality and cost, then widen access.
- Keep measuring
- Re-run evaluations when the model, prompts or data change.
Four decisions to settle early
- 01
What good looks like
An agreed test set and target, rather than a general feeling that the answers seem fine.
- 02
Where people stay involved
Which actions an AI may take alone and which need a person to approve them.
- 03
What data may be sent
What goes to a model provider, under which terms, and what must stay inside your systems.
- 04
Which model to use
Chosen on your evaluation results and running cost, and kept replaceable.
Common questions
Can you guarantee accurate answers?
No one honestly can. We measure accuracy against agreed tests and design the product so that mistakes are visible and recoverable.
Will our data train someone's model?
That depends on the provider and its terms. We check this during scoping and record what was chosen.
What does it cost?
It depends on scope and expected usage. You get a written estimate after scoping, including an estimate of running costs.
Can you add AI to our existing SaaS product?
Usually, yes. We look at where it would genuinely save your customers time, then add it behind your existing accounts and permissions.
Which model provider do you use?
We choose from evaluation results, cost and your data requirements, and keep the design replaceable so you can change later.
Tell us what you want to build.
A few lines are enough. We reply in writing with questions.