01 — NORMALIZE
Build the evidence ledger
Normalize objectives, success criteria, milestones, stakeholder roles, technical result, commercial outcome and available decision evidence across the cohort.
Founder-led · fixed scope · async
When technical validation passes but the deal still does not close.
DealCause reconstructs what happened between a technically successful enterprise evaluation and the final commercial outcome — from sanitized historical evidence, with visible counterevidence and explicit UNKNOWN where causality cannot be supported.
No CRM integration
No mandatory call
3–5 historical evaluations
How it works
Separate technical success from buying-decision failure.
Built for sales-led B2B SaaS and AI teams running repeated enterprise POCs, POVs or equivalent technical evaluations.
02 — ADJUDICATE
Claims, counterevidence, UNKNOWN
Distinguish what the record actually supports from plausible explanations that remain unproven or contradicted.
03 — TEST
Turn patterns into experiments
Translate recurring mechanisms into bounded changes for the next evaluation cohort, with explicit hypotheses and measurable outcomes.
Good fit when
- You run repeated enterprise POCs, POVs or technical evaluations
- Technical validation consumes meaningful SE or engineering capacity
- Some evaluations pass technically but still fail commercially
- You have at least 3 historical evaluations with usable evidence
- You want a written retrospective rather than another platform rollout
A strong fit does not guarantee a causal answer. If the record cannot support one, the conclusion stays UNKNOWN.
What you do not need to provide
- CRM credentials or system access
- Customer names when pseudonyms are sufficient
- Secrets, passwords or API keys
- Unredacted regulated or sensitive data
- Mandatory discovery workshops
- New buyer interviews for the first pass
Before evidence is shared, DealCause confirms the minimum sanitized input required and whether the available record is sufficient for a useful diagnostic.
Validation engagement
Small enough to evaluate. Specific enough to test willingness to pay.
The initial engagement is intentionally bounded so both sides can assess the method without a platform deployment or open-ended consulting project.
Fixed scope
$2,500 USD
For a 3–5 evaluation retrospective, subject to the evidence gate. If the sanitized record is not sufficient for a useful analysis, there is no reason to force an engagement.
Input3–5 historical evaluations
InteractionAsync; one written clarification round
OutputWritten diagnostic package
IntegrationNone required
Data postureSanitized / pseudonymized where possible
Decision gateEvidence sufficiency checked first
Deliverable
A written forensic, not another dashboard.
The output is designed to help a revenue and presales team reason about the gap between technical validation and commercial outcome without hiding uncertainty.
What the final package is designed to answer
What changed after technical validation? Which explanations survive the evidence? Which remain UNKNOWN? Where was technical capacity consumed? What should be tested differently in the next cohort?
Executive synthesis
The few mechanisms that matter most, without overstating certainty.
Evidence & counterevidence map
Support, contradictions and missing evidence for each material claim.
Cross-evaluation pattern analysis
Recurring mechanisms across the cohort rather than a generic list of loss reasons.
Capacity implications
Where technical effort was consumed without a corresponding buying decision.
Next-cohort experiments
Specific, falsifiable changes to test in future enterprise evaluations.
Boundaries
Credibility comes from saying what the evidence cannot prove.
DealCause is positioned as a bounded managed forensic, not as a claim that generic AI or existing presales tools cannot solve parts of the problem.
No invented causality
If a causal explanation is not supported by the record, it remains UNKNOWN.
No platform migration
The initial diagnostic does not require a CRM integration, new system of record or ongoing software commitment.
Existing tools may be enough
If your current workflow already resolves these cases reliably, a separate DealCause forensic may not add enough value.
Questions
Easy to evaluate before you share data.
Do we need to integrate DealCause into our stack?
No. The initial diagnostic is deliberately bounded and works from a small sanitized historical evidence pack.
What if Homerun, Vivun, our CRM AI, or our internal process already solves this?
Then you may not need DealCause. The evidence-schema step is designed to determine whether a separate forensic adds useful resolution beyond the workflow you already have.
Will DealCause claim to know why a deal was lost when the evidence is weak?
No. Unsupported causal explanations remain explicitly UNKNOWN.
Do we need to share customer names or sensitive credentials?
No. The initial analysis is designed around redacted or pseudonymized material. Credentials, secrets and unredacted sensitive data should not be sent.
Do we need a call before deciding?
No mandatory call is required for the initial evaluation. The first step is the one-page evidence schema and an asynchronous fit check.
Start with the evidence schema.
See exactly what the diagnostic needs before deciding whether the historical record is safe and useful to share.