One capability. Any table. Your domain brings the question.
Every case below carries the same five things: the human question, the engine’s reading of it, what happens when the signal isn’t there, the leakage trap that would have flattered a naive model, and a prompt you can paste straight into Claude, ChatGPT, or Cursor.
02 · insurance renewal & claims triage
03 · lending & adverse-action
04 · B2B SaaS churn & expansion
05 · healthcare no-show & readmission
Ranked by where the differentiators are worth most — not by where the tooling is most crowded.
The three we lead with.
The overlays are the segment entrances. One engine and one architecture already work this way.
“Which accounts are most likely to pay if we work them?”
Payment history too thin to separate will-pay from won’t-pay above the bar in this window.
Leakage from payment_received_date or a settlement flag written after the fact.
Using the Hunter-Seeker MCP server, rank my accounts table by likelihood of payment within 30 days. Outcome column: paid. Entity: account_id. Return the top 25 with evidence, levers, and provenance.
“Which claims need a human referral?”
Referral base rate too low, or claim features don’t separate referred from cleared above lift 1.5.
adjuster_notes or a review-completed timestamp that only exists after the referral.
Using the Hunter-Seeker MCP server, rank my claims table by likelihood of needing referral. Outcome column: referred. Entity: claim_id. Return the top 20 with evidence and provenance.
“Which accounts are most likely to churn next quarter?”
No stable pattern above lift 1.5 — usage data too sparse, or churn base rate too low for this window.
Leakage from cancellation_date or downgrade_flag — leak-guard quarantines and names them.
Using the Hunter-Seeker MCP server, rank my accounts table by likelihood of churn in the next 90 days. Outcome column: churned. Entity: account_id. Return the top 20 with evidence, levers, and provenance.
Same engine, other tables.
“Which leads should sales call first?”
Conversion signal below the bar — too few converted examples in the training window.
won_opportunity_id leakage — a column that only exists after the win.
Using the Hunter-Seeker MCP server, rank my leads table by likelihood of converting within 30 days. Outcome column: converted. Entity: lead_id. Return the top 25 with evidence and provenance.
“Which machines are likely to fail this month?”
Sensor history insufficient — not enough failure examples to validate above lift 1.5.
maintenance_performed recorded after the failure — classic post-outcome leakage.
Using the Hunter-Seeker MCP server, rank my machines table by likelihood of failure in the next 30 days. Outcome column: failed. Entity: machine_id. Return the top 15 with evidence and what would move each out of the risk pattern.
“Which shipments will be late?”
Route and carrier features don’t separate late from on-time above the bar in this dataset.
actual_delivery_time leakage — the answer hiding in the features.
Using the Hunter-Seeker MCP server, rank my shipments table by likelihood of arriving late. Outcome column: late. Entity: shipment_id. Return the top 30 with evidence and provenance.
The complaints competitors collect — and what we do instead.
These come from reviews of the tools our buyers already own. Note that adoption, data quality, and trust — not model accuracy — cause most of the failures.
| The complaint | Our answer | Lands hardest in |
|---|---|---|
| “Reps won’t follow a score they can’t explain” — adoption craters | Driver groups, per-entity levers, and staged action with approval | Collections, claims, RevOps |
| “Couldn’t explain the prediction to the board” | The engine owns every fact; the language model only rephrases | Lending, insurance, collections |
| Leakage: models that looked excellent and failed in production | Leak-guard quarantine with plain-English reasons; out-of-time validation by default | Any high-stakes ranking |
| “Health scores are consistently inaccurate” | Calibrated scores, honest-empty below the bar, quota refunded | CS/churn, collections |
| Pricing shock, months-long implementation | Single-purpose, fast to value | Mid-market underserved by enterprise incumbents |
Complaints we would not fix, and might worsen: narrow scope, no forecasting, no unstructured data, human-in-the-loop friction, and refusing to answer. If those are dealbreakers, we are the wrong tool and we would rather say so now.