In three weeks you get a written map of where AI would and would not pay off in your business, ranked by effort and payback, with enough detail that any competent team could build the top items.
This one is a guide, not a fixed install yet: the method is written and the open questions are listed below. If your situation fits, the call is where we find out.
Larger non-tech mid-market firms (roughly 50 to 500 staff) with real revenue and pressure to use AI but no strong internal team to sort hype from value: professional services, regional financial services, insurance, healthcare admin, logistics, and multi-location operators. A skeptical owner who wants an honest map before spending is the ideal buyer.
Leadership is told AI will change their industry, vendors pitch generic tools, staff experiment quietly with no guardrails, and there is no defensible view of which two or three uses of AI would actually pay for themselves in this business.
01 Kick-off call
A 30-minute call to scope the assessment: headcount, interviews, and which processes we walk through. Fixed fee agreed here.
02 Weeks 1 to 2: discovery
Leadership interviews, process walk-throughs with staff, and a review of your tool and data landscape.
03 Week 3: score and write
Use cases scored for value, effort, data readiness, and risk. You get the written report and one use case taken to a scoped build plan.
04 Readout
A session with leadership covering the ranked map, what not to do and why, and which shelf tools already cover which needs.
Questions
Who you talk to
No account managers, no handoff to a junior team. The 30-minute call is with us, the quote comes from us, and if you go ahead, we build it.
Behind us is a wider NodeStar engineering team, including a PhD in AI, and tech partners like Kolibri Labs.

Piroune Balachandran
Founder & CPO
Co-founded Enso with Alejandro Fenn. Six years in economic consulting at Cornerstone Research.

Alejandro Fenn
Founder & COO
Three-time founder; go-to-market and partnerships. Also co-founder of Enso.

Konrad Łykowski
Head of ML & CloudOps
Designed a 60 PB AI vision system and built Australia's largest Kubernetes clusters.