δiscovery Lab™ for Product
Your roadmap is only as defensible as the customer evidence behind it
Four Questions Worth Asking
What Changes
When discovery actually works
Build what matters
Features backed by validated evidence, not the loudest voice in the room. Your roadmap reflects what customers need.
Research that ships
Discovery rigorous enough that engineering acts on it. No more "but did you actually talk to customers?"
Fewer failed launches
Problems validated before solutions are built. You stop shipping features that solve problems nobody has.
Faster prioritisation
When every initiative has proof weight, the backlog argument ends. Trade-offs become arithmetic.
Consistent discovery
Every team discovers to the same standard. Quality doesn't depend on which PM ran the interview.
What Your Product Leaders Actually See
After every customer conversation, the platform evaluates what was actually learned
EVALUATION FRAMEWORK
Every discovery conversation is assessed on two axes: OPEN (did the customer reveal their real situation?) and READY (can they act?). Each is broken into scored dimensions with narrative coaching. Not just a number, but why.
ASSUMPTION TRACKING
Before each conversation, the platform generates testable assumptions from research. After the conversation, it tracks which were validated, invalidated, or untested. With specific evidence. This is what "evaluated" means: scored against a rubric, not a gut feel.
CROSS-TEAM COMPARISON
Compare discovery quality across teams. Not just output velocity. See which teams are doing real discovery and which are running assumption-confirmation calls. Same framework, same rubric, comparable results.
BOARD-READY EVIDENCE
Present the board with evidence that roadmap decisions reflect customer need, not stakeholder politics. Every build decision has a proof weight. Traceable back to specific customer statements.
How It Works For Your Team
Five stages. Every initiative. Every customer conversation.
Works With Whatever You Already Use
You Might Be Thinking
Common Questions
How is this different from Dovetail or Productboard?
Dovetail and Productboard store and synthesise research after it's been collected. δiscovery Lab works upstream: it improves the quality of the discovery conversation itself. Better input, better synthesis. They're complementary, not competing.
What does "evaluated" actually mean?
Every conversation is scored against a structured rubric on two axes. OPEN measures whether the customer revealed their real situation. READY measures whether they can act. Each axis is broken into scored dimensions with narrative explanation. Not a sentiment score. Not a thumbs up. A rigorous assessment of what was actually learned.
Does this work for UX Research as well as Product Management?
Yes. Any role that conducts customer conversations to inform product decisions. PMs, UX researchers, product designers, customer success. The evaluation framework applies to any structured discovery conversation. The difference is consistency: every person on every team discovers to the same standard.
How does this help me defend roadmap decisions to the board?
Every build decision gets a proof weight. Traceable back to specific customer statements, mapped to specific testable assumptions, with source attribution and strength ratings. When the CEO asks "why are we building this?", you have an evidence trail. Not a slide with quotes.
Can I compare discovery quality across teams?
Yes. Same framework, same rubric, comparable scores. You see which teams are doing real discovery and which are running assumption-confirmation calls. Coaching becomes specific to each team's gaps. Not a generic training programme.
What does a discovery cycle actually look like?
Before the meeting, you define what needs to be learned. Testable assumptions about the customer's situation. The platform generates structured preparation around your hypotheses. After the meeting, you upload the transcript and the platform evaluates what was actually discovered. Evidence accumulates across conversations, so each meeting builds on the last. The whole cycle takes less time than writing a call summary, and produces something you can actually use.
How does the evaluation work?
The platform evaluates every conversation on two axes. Performance measures how well you discovered. Question quality, structure, depth. Assessment measures what you learned and how qualified the prospect actually is. You get scores, breakdowns, and specific coaching recommendations. Based on rigorous AI evaluation against structured rubrics. Not a sentiment score.
What counts as evidence?
Not call notes. Not "the customer seemed interested." Evidence is a validated customer statement mapped to a specific testable assumption, with a source, a strength rating, and a proof weight. The distinction is the difference between a roadmap you can defend and one you can't.
How do product leaders get visibility?
Leaders see a view across all teams and all active initiatives. Which conversations produced validated evidence and which produced anecdotes. Which assumptions have been tested and which are still guesses. Discovery quality becomes visible and coachable, not assumed.
Do we need to change our existing tools or workflow?
No. δiscovery Lab works upstream of whatever you already use. Your frameworks stay. Your tools stay. Your workflow stays. The quality of what goes into them gets better.
Can one PM use it, or does it need a team deployment?
Both. An individual PM can start immediately and see value from the first customer conversation. Team deployment adds comparable scoring across PMs, leader visibility, and consistent standards. Most organisations start with one team, prove the impact, then expand.
How long does it take to get started?
An individual can start today. Team onboarding typically takes one focused session. Your first real conversation goes through the platform within the first week. No lengthy implementation, no IT project, no training programme to schedule.
Where does our data go?
Your data stays yours. Hosted on EU infrastructure. Conversation transcripts and evaluation data are not used to train AI models. Access controlled per user and per team.
What does it cost?
Individual access is available immediately. Team and enterprise pricing depends on team size and scope. Talk to us about your situation.
δiscovery Lab™ helps product teams understand what customers actually need. Before you commit engineering resources to assumptions.