AI
Where AI pays off, what it should be allowed to do and where a person still decides.
When it fits
AI pays off on work made of language, knowledge or a repeated judgment that somebody can check. Finding answers in your own material, preparing a draft, carrying a defined process from start to end.
We start from the task and how to test it. The choice of model comes after. Some work is better done by a plain rule or a direct integration, and we say so when that is the case.
What can be in scope
- Business assistants
- An assistant that works from knowledge you approve, so your people find answers, prepare work and decide faster.
- Agents and workflows
- AI doing a defined task, with the permissions, review points and checks the work calls for.
- An AI product
- What you know about your industry, turned into a product. We scope it with you and build a working first version.
What to bring
- Examples of the task
- What goes in, what a good result looks like and which mistakes would hurt. Include the cases where the right answer is to ask a person.
- Approved knowledge
- Which material it may use, and who keeps that material accurate. Hold back anything confidential until we have agreed how it is used.
- A review decision
- Who checks the output, and whether the system only prepares work or is allowed to act.
Decisions worth making early
- Assistance or action
- Drafting a reply and sending it are two different permissions. We draw the line between a suggestion, a human approval and an action the system takes alone.
- Evidence and uncertainty
- A confident answer is not a correct one. We decide what sources come with an answer, and what the system does when it is unsure.
- Evaluation and fallback
- We test on real tasks to find where it helps and where it fails. A person always has a clear way to step in.
How we check the work
A demo that works once proves nothing. We test realistic examples, requests it should refuse, missing information and the edges of its permissions. No AI system is infallible, so we design for the errors you can live with.
An AI workflow needs somewhere to live, clean inputs and an output somebody uses. We bring the software, the interface and the business thinking together around it.