SALES OPERATIONS // FREE LIVE DEMO
Deal Health Predictor
Deals that are quietly dying — stalled replies, shrinking meeting attendance, a champion who's gone quiet — don't get flagged until the close date slips and it's too late to save them.
WHAT IS DEAL HEALTH PREDICTOR?
An AI deal health predictor reads a deal's recent activity pattern — response lag, missed meetings, champion silence, a stalled proposal — and scores how at-risk the deal actually is, names the specific warning signs behind that score, and recommends either a concrete save action or a graceful let-go, instead of a deal quietly dying until the close date slips.
HOW IT WORKS
Deal risk almost never shows up as one dramatic event — it shows up as a pattern of small signals accumulating: replies getting slower, a champion skipping calls, a proposal sitting unanswered. Individually each signal is explainable (people get busy); together they form a recognisable risk pattern that most CRMs don't score because they track stage and close date, not behaviour trend. This agent is prompted to read the pattern as a whole and to resist the temptation to soften the score for a deal that still looks fine on paper (right stage, right close date) but is behaviourally dying underneath.
The recommendation deliberately isn't always 'try harder' — for a genuinely cold pattern (champion gone silent, no secondary stakeholder), the agent recommends a graceful let-go over the pipeline, because chasing a dead deal costs more than it saves and inflates a forecast that then has to be walked back later.
You are a deal-health risk analyst for B2B sales. Given a deal's recent activity pattern and current stage, identify specific at-risk signals (response lag, meeting no-shows, champion silence, stalled proposal), score overall deal health 0-100, and recommend one concrete save action or a recommendation to let it go gracefully. Do not soften a genuinely at-risk score to be optimistic. Output ONLY a JSON array of typed blocks matching the OutputBlock union — no prose outside the JSON.USE CASES
Weekly pipeline risk sweep
Run every open deal past a certain stage through the agent weekly to catch quiet decay before it shows up as a slipped close date.
Forecast-call red-flag prep
Before a forecast review, flag which 'commit' deals actually show at-risk behavioural signals underneath the stage label.
Handoff between reps
When a deal changes owners, the new rep gets an honest health read instead of inheriting an optimistic status update.
Renewal / expansion risk
The same activity-pattern logic applies to at-risk renewals — a quiet champion pre-renewal is the same signal as one pre-close.
RESULTS & BENCHMARKS
Time to assess deal risk
projected (modeled)
Weekly optimisation time (production)
real, anonymized
LIVE DEMO
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FAQ
Common questions
What signals does the agent look for?
Response lag, missed or declined meetings, champion silence, and stalled proposals — individually explainable events that together form a recognisable at-risk pattern.
Does it always recommend pushing harder to save the deal?
No — for a genuinely cold pattern it recommends a graceful let-go instead, since chasing a dead deal costs more than it saves and inflates a forecast that later has to be corrected.
Can it connect to my real CRM activity data?
The live demo works from a described activity pattern you paste in; a production integration would pull the same signals directly from CRM activity logs.
RELATED AGENTS
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"No" gets logged as lost and the prospect drops out of the pipeline entirely, when most objections are actually "not now" or "not like this" — a fixable disqualification, not a real rejection.
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Reps push deals through the pipeline on gut feel about budget and decision-making power, and half of them stall in late-stage negotiation because no one actually qualified those two things early.
Built by Hammad Yousuf — AI Marketing Automation Engineer, 540K+ YouTube subscribers.
See the production missions these agent patterns run in, or hire the whole system.