The dashboard measures the gap. The course and the agent close it. The rep changes behavior. The dashboard measures the improvement. Five workstreams from today's call are four positions on one flywheel, plus the manager coaching around it.
Every turn of the loop raises the number. The manager runs the loop on their existing cadence. The rep never leaves it.
Each is a workstream, labeled WS1 through WS5. Read them as arcs of the same flywheel, not a to-do list.
Spec, price, features, packaging. The forcing function that gives Nathan a target Monday. Sourced from the existing Momentus PRD, not reinvented.
Formalize the live analysis schema as the SPICED data model on the bowtie, then define the agents and their entry, exit, and handoff. This is the M1 protocol thesis made concrete.
Point the pipeline at our own Gong calls. Use the dashboard in one real 1:1. Make it useful to us before we sell it. This is the proof point and the case study.
The dashboard diagnoses; the agent acts. It reads the rep's gap, hands the playbook section and a ready prompt, follows up on cadence, and is how we actually move the lagging number.
This is the piece that connects everything. The dashboard names the gap; the course teaches the rep how to use AI, including which frontier model for which task, to actually close it. Same outcome the dashboard measures, reached by the rep's own hands. WS5 is not a separate content project, it is the Upskill arc of the flywheel pointed straight at the number.
Per rep, four parts: what the customer said, what the rep said, why right or how to improve, the next action. Grounded in the verbatim line.
Their deals, each with rolling SPICED state, confidence, and risk. Warm-but-doomed deals flagged, not scored healthy.
Team altitude: adoption, segments, funnel, the consistency gap between top reps and the rest.
Working shape: per-seat per month plus a token-metered analysis cost, sized against the roughly one to three dollars per learner we modeled on the course. Packaged standalone or bundled with the refreshed training. These are hypotheses to test with the MDs, not settled numbers, and the routing economics that justify a price are not measured yet.
Not the full map at low resolution. A worked example Nathan can build Monday, grounded in a real scored account.
| Dimension | Score | State | Gap |
|---|---|---|---|
| Impact | 2.0 | weak | Rep never pushed pain to a business consequence |
| Critical Event | 1.5 | weak | No compelling event pinned |
| Decision Process | 2.2 | weak | Approver path unmapped |
| Situation | 3.5 | solid | no gap |
"The only way we can process money is physical check or ACH. We don't do credit card payments. And if the money was lost, it's going to come back on me."
"I'm so glad you said that. We have a demo just on Momentus payments, our native payment channel."
"That is a real risk. If a payment slipped through on a 25,000 dollar buyout, what would that cost in lost revenue or rework, and how often does that near-miss happen?"
The whole win is walking in with one clear system Nathan can engineer against. That is WS1 and the WS2 slice. WS4 (the agent) and WS5 (the course) build on the data model, so they follow, not lead. Everything else waits.