SolutionsRunning the team
You cannot inspect forty deals. You can inspect what is stuck.
Your week has room for maybe six real deal conversations. You have forty deals. The six you end up having are chosen by which reps volunteered a problem, which is precisely the opposite of the selection you want, because the deals in trouble are the ones nobody raises.
01What is really happening
Pipeline review selects on candour, not on risk.
The reps who raise problems early are usually your strongest. The deals that quietly die belong to people who believe another week will fix it. So the review systematically inspects the healthiest part of the pipeline and misses the part that is actually failing.
- A confident narrative is indistinguishable from a healthy deal in a meeting.
- The rep who most needs help is the least likely to ask.
- By the time a deal is raised, the recoverable window has usually closed.
02What Adrata reads
Every deal, every hour, without anyone volunteering.
Deals are watched continuously for stalling and accumulating risk rather than examined when someone remembers them. A deal surfaces when the evidence says it is in trouble (coverage gaps, a silent room, a missing decision-maker), not when it reaches a stage boundary on the board.
- Deals ranked by what is genuinely stuck, not by close date or size.
- The bottleneck named as a sentence, with the evidence attached.
- Risk that accumulates quietly, surfaced before the quarter ends.
03The move
Turn the review from a narration into an inspection.
You walk in already knowing which deals are stuck and why, so the conversation starts at the coaching moment rather than spending forty minutes getting there. The rep is not being interrogated; you are both looking at the same evidence.
- The handful of deals worth your week, chosen on evidence.
- The specific coaching moment attached to each one.
- A review your rep can prepare for, because the criteria are visible.
The model behind it
You should be able to argue with the recommendation.
A move you cannot interrogate is a move you will ignore the first time it is wrong. These are the models doing the work in this scenario.
The right to say nothing
Is there enough evidence here to justify a recommendation at all?
Learned components stay inert until they beat the simple heuristic on held-out data, and every model has a floor below which it abstains rather than guessing. On a new workspace the honest answer is often "not yet", and the system is built to say so instead of manufacturing confidence.
Off-policy evaluation with a safety gate
Would a change to the recommendation logic actually have done better?
Every recommendation is logged with the probability it was selected, which makes it possible to score a proposed new policy against history using inverse-propensity, self-normalised, and doubly-robust estimators. A new policy is promoted only if its lower confidence bound beats the incumbent, with effective sample size and reward-hacking guards checked first.
Calibrated timing
When will this actually close, and how sure can you be?
Deal timing is modelled with competing risks: won, lost, and stalled are different fates, not one binary. The intervals come from conformal prediction, which produces ranges with a stated coverage guarantee rather than a single confident date.
Where it runs
The surfaces this scenario uses.
Who this is for
The people who live in this moment.
Running the team