Consulting & Delivery

Product Discovery & Strategy

From vague idea to testable AI product brief in 3 weeks: user research, opportunity mapping, and specs your engineers can actually build against.

What is product discovery and strategy?

The Answer

Product discovery and strategy is user research, opportunity mapping, and testable product briefs for AI-native features. We conduct interviews, analyze competitive positioning, and hand your team a prioritized roadmap with validated assumptions so engineering ships features customers actually want.

What you get

Three outcomes we commit to before we start.

01

8 to 12 real user interviews

Not surveys, not analytics: actual 45 minute conversations with the people who'd use the feature. Every recommendation traces back to specific quotes from specific users.

02

Prioritized opportunity map

Every idea scored on desirability, viability, feasibility, and ethical risk (specific to AI features). Your team knows which idea to build first, which to shelve, and which to kill outright.

03

Engineering ready spec

Not a deck: a spec: user flows, success criteria, eval metrics, edge cases, and 'what does 'done' look like'. Your engineers can start building the day the discovery ends.

The Guaranteed Production Pilot

Fixed scope · Written target

A working Product Discovery feature shipped to production in weeks: no ML hire required.

Production grade Product Discovery your users actually touch: real code, real evals, benchmarks your next round can point to. Not a prototype you have to rebuild.

Speed

First production milestone in 5 days. Full pilot live in 6 weeks, fixed scope, weekly demos.

Zero effort

Done for you end to end: you get the production code, the eval harness, and 100% IP ownership. No lock in, no data hire.

Risk reversal

We commit the first milestone in writing. Miss that first week milestone and the first week is free.

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Service FAQ

People also ask about product discovery.

Two additions to standard discovery: (1) capability testing (is the AI actually good enough at this task today, or do we need to wait/fine tune? (2) ethical/risk framing) hallucinations, bias, and user trust need explicit design decisions, not afterthoughts. We do both alongside standard user research.