A shortlist of where AI is worth it
Your workflows ranked by value, risk and data readiness, with how much freedom the model should have in each.
AI-first applications
We help you decide what the model should do, what plain code should keep doing and what it takes to turn an impressive demo into a feature people can rely on.

Does the AI have a clear job, or was it added because it could be?
Which answers need a source, a permission check, an exact calculation or a person to approve them?
After launch, can you tell when it gets something wrong, and can you fix it?
What does the user see when the model is down, unsure or wrong?
Your workflows ranked by value, risk and data readiness, with how much freedom the model should have in each.
What it can read, what it must cite, what needs a person to confirm and what happens when it fails, written so engineers can build it and managers can sign it off.
The stages, how to test each one, who owns it once it runs and what it will cost before you scale it.
Tell us what keeps going wrong. A few rough lines are enough.
We talk it through, then send a short note: what we will look at, how long it should take and what it costs.
We read the system, talk to the people doing the work and follow real cases from start to finish.
A plain-language report, a plan in order of priority and a session to walk your team through both.
We build and run our own products. The advice comes from that work.
Truyền ThuyếtA 69-chapter novel and gamebook written with AI under human direction, with continuity checks and editing passes we designed ourselves.You do not need a tidy brief. A few lines about the system, workflow or delivery problem are enough to start.