Where agents fit. What to build first. What not to build.
Most AI initiatives fail in scoping, not in engineering. Discovery is where the leverage is — and where we spend a disproportionate share of our time before writing any code.
What we do
- →AI opportunity mapping across your business
- →Build vs. buy vs. wait analysis per workflow
- →Agent vs. workflow vs. simple LLM-call decisions
- →Roadmap design with cost, risk, and value modeling
- →Evaluation criteria for vendor demos
- →AI policy and governance frameworks
What we believe
- Most workflows do NOT need an agent — a function call suffices.
- The right first project is small, scoped, and has fast feedback.
- Production beats prototype, every time.
How a discovery engagement runs
01
Stakeholder interviews
Two weeks talking to the people doing the work. What's painful, repetitive, or high-leverage?
02
Workflow inventory
Map every candidate workflow against automatability — score by value, feasibility, and risk.
03
Prototype shortlist
3–5 candidates ranked by expected value × feasibility. We'll tell you which ones don't need AI at all.
04
Written report
A roadmap you can take to your board: what to build, when, with whom, and what NOT to do.
Considering AI but not sure where to start?
Most engagements begin with a 30-minute call. We'll come back with whether discovery is the right next step — and what good output looks like.