δiscovery Lab™ for Sales
Your reps are committing deals built on conversations, not evidence
Four Questions Worth Asking
What Changes
When discovery actually works
Pipeline truth
Opportunities reflect real customer needs, not wishful thinking. Your pipeline stages mean what they say.
Forecast confidence
When discovery is real, forecasting is arithmetic rather than guesswork. You can defend your number.
Faster, bigger deals
Better discovery upfront means fewer surprises, less rework, and scope that reflects the full customer need.
Fewer wasted cycles
Disqualification happens early when discovery is structured. Your team stops chasing opportunities that were never going to close.
Coaching on quality
Coaching based on what your reps actually discovered. Not just whether they made the call.
What Your Managers Actually See
After every conversation, the platform scores what was actually discovered
DISCOVERY SCORE
Every conversation gets a score across Performance, Outcome, and Evidence. Your managers see at a glance which reps are discovering and which are presenting. No need to listen to a single call.
QUALIFICATION EVIDENCE
OPEN scores reveal whether the buyer shared their real situation. READY scores reveal whether they can act. Together, they tell you which deals are real. Evidence, not opinion.
HYPOTHESIS TRACKING
Before the meeting, the platform generates testable assumptions. After the meeting, it tracks which were tested, validated, or invalidated. With specific evidence from the conversation.
COACHING SPECIFICS
Not "make more calls." Specific: "Your discovery depth on Acme was 58. You missed the budget constraint and never tested the timeline assumption. Here's what to ask next time."
How It Works For Your Rep
Five stages. Every deal. Every conversation.
Works With Whatever You Already Use
You Might Be Thinking
Common Questions
Does this integrate with Salesforce?
The platform is designed to work alongside Salesforce, not replace it. δiscovery Lab captures the evidence that your CRM can't: what the customer actually confirmed, which assumptions were validated, and how qualified the opportunity really is. Salesforce tracks the deal. δiscovery Lab builds the understanding the deal depends on. Direct field-level integration is on the roadmap.
How is this different from Gong?
Gong records and analyses what happened on the call. δiscovery Lab works before and after: it structures what your rep needs to discover before the conversation, then evaluates whether real discovery actually happened afterwards. Gong tells you what was said. δiscovery Lab tells you what was learned.
What does my rep actually have to do differently?
Before each meeting, the platform generates structured preparation in minutes. Your rep reviews it. After the meeting, they upload the transcript. The platform does the rest: evaluation, scoring, evidence extraction, coaching recommendations. The total time per deal is less than writing a call summary. The difference is what comes out of it.
What does "discovery depth 58" actually mean?
Every conversation is scored on a 0-100 scale across three weighted dimensions: Performance (how well the rep executed discovery), Outcome (what was actually learned), and Evidence (how specific and verifiable the findings are). A score of 58 means "Developing". Your manager can see exactly where the gaps are and coach on specifics, not generalities. The rubric is consistent across every rep and every deal.
How quickly will we see impact?
Most teams see a shift in conversation quality within the first two weeks. Managers report being able to coach on specifics rather than activity from the first pipeline review. The compounding effect on forecast accuracy typically shows within one quarter. The earlier you start, the sooner your next board meeting has real numbers behind it.
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 pipeline you can trust and one you can't.
How do managers get visibility?
Managers see a view across all reps and all active deals. Who is discovering and who is presenting. Which conversations produced validated evidence and which produced polite interest. Which prospects are genuinely qualified versus optimistically staged. Coaching becomes specific to each rep's actual performance. Not a generic training exercise.
Do we need to change our existing tools or workflow?
No. δiscovery Lab works upstream of whatever you already use. Your methodology stays. Your tools stay. Your workflow stays. The quality of what goes into them gets better.
Can one person use it, or does it need a team deployment?
Both. An individual can start immediately and see value from the first conversation. Team deployment adds comparable scoring across reps, manager visibility, and coaching at scale. 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 sales teams understand what customers actually need.
Before you commit resources to assumptions.