Every engagement ships these as concrete artifacts you own — not slides, not hand-waving.
What data you actually have (versus what you think you have), where it lives, and what's usable for AI today.
10–15 candidate use-cases scored on impact, feasibility, time-to-value, and risk — with our recommendation on the top 3.
For each shortlisted bet: a one-pager showing the model, the data flow, the integration points, and the build-vs-buy call.
Phased plan with timelines, team shape, vendor decisions, and a credible cost envelope you can take to a CFO.
Workshops with 6–10 stakeholders across ops, product, and engineering. We pull threads, not run agendas.
Deep-dive into 2–3 candidate datasets and current systems. We read code, watch screen-shares, and trace real workflows.
Quick build-tests on top candidates to validate technical feasibility — not a polished demo, a forcing function.
Final readout to leadership with prioritized roadmap, budget, and a go/no-go on each bet.
Best-in-class where it matters; boring and battle-tested everywhere else.
Sprint length and team shape vary with corpus depth and stakeholder count. Typical engagement is 1–2 weeks; sprint cost is credited toward the build phase if you proceed.
Because 60% of enterprise AI projects fail before production. A 2-week diagnostic is cheap insurance against picking the wrong bet — and most clients find at least one sacred-cow project we tell them to kill.
Almost no one does. The audit specifically calls out what's usable today, what needs cleaning, and what'll need new instrumentation. That's part of the deliverable.
No. This sprint is AI-specific. For broader product or platform strategy, we'd scope differently.
Yes — mutual NDA before the kickoff is standard. We've signed under PSU banks, NBFCs, and several state-government scopes.