The difference between a demo and a useful assistant is everything around the model: access to the right data, permission awareness, tools that let it act, guardrails that keep it predictable, and evaluation that shows whether it actually helps.
We build AI features into existing products and internal tools with those pieces in place, and we are direct about when a simpler solution would serve you better.
How we approach it
We pick one measurable use case, build a working version quickly, and evaluate it against real questions before expanding. Every action the assistant takes is logged, and anything that changes data goes through explicit approval until trust is earned.
What is included
- Use-case selection and value definition
- Retrieval over your documents and data
- Tool integration with business systems
- Guardrails, logging and evaluation
- User interface inside your product
- Cost and latency control
What you get
An assistant that answers from your data, not from the internet Predictable behavior with a clear audit trail A measured answer to whether AI helps here