Do the people who know your domain best
struggle to tell you what they know?
What they say
“We need a better dashboard”
What they actually do
“Every Monday I spend 3 hours copying numbers between two systems that don’t talk”
What they need
“Minimize the time to reconcile data across disconnected systems without manual error”
From what they say — to what they need
Unearth what your
experts knowusers needteams do
AI‑powered interviews that go beyond feature requests to extract the real workflows, pain points, and requirements your team needs to build the right thing.
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The best insights aren’t in surveys or workshops.
They’re in the stories no one thought to tell.
The process
From invitation to specification
Invite experts
Send a link to your domain experts. They click, verify their email, and start talking. No accounts, no onboarding, no friction.
AI interviews
Our agent conducts deep discovery interviews — reconstructing specific past events, not asking hypotheticals. It gets past what people think they do to what they actually do, using research-grade techniques adapted from the best human interviewers.
Confirm understanding
Experts see their knowledge rendered as workflow diagrams, force quadrants, and outcome cards. They confirm with Agree, Not Quite, or Wrong. The modifications are the most valuable data.
Structured specs
Receive actionable outcome statements, step-by-step workflow breakdowns, pain points ranked by frequency and severity, and edge cases — all grounded in real practitioner behaviour and confirmed by the experts themselves.
Use cases
What becomes possible
Structured requirements, cross-expert patterns, and edge cases that would take months to extract manually — delivered in days, confirmed by the experts themselves.
Building software for accountants
What they say
“Accountants say they want better reporting”
What Unearth finds
Across 8 interviews, Unearth maps the full month-end close workflow — 23 steps, 4 systems, 6 handoff points. It surfaces that 70% of errors originate at a single manual re-entry step. Outcome statements, pain points ranked by severity, and edge cases (partial invoices, multi-currency reconciliation) emerge — all confirmed by the practitioners themselves.
The result
You ship a product that eliminates the actual bottleneck, not a dashboard nobody asked for. Your competitors are still running surveys.
Scoping a healthcare platform
What they say
“Clinicians ask for a patient scheduling tool”
What Unearth finds
Unearth interviews 12 clinicians across 3 departments. Cross-expert aggregation reveals scheduling is a symptom — the real breakdown is referral triage with no shared visibility. It produces a complete job map with desired outcomes for each step, contradictions between departments, and a confidence-scored priority matrix.
The result
You deliver a scoping document in days that would take 6 weeks of workshops — with evidence your client can verify. That's the difference between a $50K engagement and a $500K one.
Validating a legal-tech idea
What they say
“Lawyers say contract review takes too long”
What Unearth finds
5 interviews reveal the pain isn't review speed — it's tracking which clause variations were approved across 40 similar deals last quarter. Unearth extracts the full negotiation workflow, identifies 3 undocumented workarounds senior associates use, and produces JTBD outcome statements that redefine the problem space entirely.
The result
You pivot before writing a line of code. Instead of building a faster review tool (commodity market), you build clause precedent intelligence (no competition). Your seed pitch writes itself.
Placing roles you've never hired for
What they say
“Hiring manager says they need a senior data engineer”
What Unearth finds
Unearth interviews 3 people on the team about their actual daily work. It discovers the role is really about migrating legacy ETL pipelines under compliance constraints — not building new ones. The must-have skills, the tools they actually use, and the workflow the new hire will inherit are all extracted and structured.
The result
Your job spec reads like an insider wrote it. Candidates self-select accurately, hiring managers stop rejecting shortlists, and your time-to-fill drops because you understood the role before you sourced a single candidate.
What’s different
Built on research methodology,
not prompt engineering
Not a chatbot. A discovery engine.
Reconstructs specific past events with sensory detail — time, place, who was there, what happened. The agent never asks “Why?”, never accepts vague answers, and never lets solution-talk persist. It gets to the real story.
Solution → Problem
“I need a spreadsheet template” becomes “Minimize the time spent categorising 200 transactions monthly without automation.” Every solution someone describes is treated as a symptom — systematically unwound to the real underlying need.
Visual confirmation, not verbal playback.
Understanding isn’t read back as text. It’s rendered as workflow diagrams, force quadrants, and outcome cards. Experts confirm with Agree, Not Quite, or Wrong. The modifications — not the agreements — are the most valuable data.
Cross-expert intelligence
When multiple people describe the same pattern — even with different terminology — the system links them. Produces confidence-scored findings aggregated across all interviews, surfacing where people agree and where they contradict.
Stop guessing.
Start discovering.
Turn expert knowledge into structured requirements — automatically.
Start discovering →