Pipeline read you can trust
Assess every account against defined criteria rather than optimistic self-reporting.
— Sales & Growth
Upload a campaign export with a row per contact, lead or send, and a column recording the result (e.g. 'Converted' = Yes/No).
1–2 weeks → ~25 minutes
For a full population, not a sample
No coding required
— USE CASES
Assess every account against defined criteria rather than optimistic self-reporting.
See what repeats across the pipeline in scope instead of the loudest recent deal.
Each finding lands with a recommended next move and the evidence behind it.
— HOW IT BEHAVES
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Each row of your export is processed on the same basis, so no account is skipped however long the table is.
Findings on the pipeline in scope are written up as a document that reads like professional output, with each claim tied back to a account.
Detail rows are summarised into the grouped view of the pipeline in scope without losing the underlying accounts.
Reading the pipeline in scope usually depends on what reps put in the CRM and what a leader remembers from calls. In practice it shows up as pipeline read you can trust: assess every account against defined criteria rather than optimistic self-reporting. The value sits in the rigour, not the typing — yet the rigour is exactly what gets traded away when there is only 1–2 weeks of capacity for it.
Here the same job runs as a Skill. Your material goes in; a scored view of each account with reasons shown comes out, alongside a prioritised action list for the team. The criteria, ordering and review points that make the answer trustworthy are encoded in the Skill itself — which is the difference between a structured method and a prompt someone pastes in. The practical effect: 1–2 weeks of manual work becomes a ~25 minutes run, held to an identical standard on the tenth engagement as on the first.
Campaign response predictor exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
Upsell propensity map
Upload a purchase-history export with a row per account and a column recording whether they expanded (e.g. 'Expanded' = Yes/No).
Deal win-likelihood scorer
Upload one CRM export with a row per opportunity and a column recording the result (e.g. 'Outcome' = Won/Lost, blank for open deals).
Customer churn & renewal risk
Upload one account export with a row per customer and a column recording what happened at renewal (e.g. 'Renewal outcome' = Renewed/Churned, blank for accounts not yet up).