880 thousand monthly signal-moments. One substrate. Automated triage. Human attention where it counts.
Four-paragraph SCQA. Grounded in Jason's 83-account BoB extraction from 2026-04-18. Every number traces to source.
Apollo manages roughly 1,400 paying teams. Every one generates signals daily across deliverability, adoption, workflow, CRM sync, AI usage, and sequence health. The monthly detection surface is approximately 880 thousand signal-moments across 21 signal classes. Today those moments are invisible at portfolio scale.
The intervention model is headcount-bound. 8 GTMEs, 80 accounts each, 640 teams actively monitored. The remaining 760 teams are operationally blind. Hiring cannot close this at unit economics that hold.
Can Apollo detect every signal automatically, match each one to a prebuilt resource, deliver it on the right surface, and route only the exceptions to a human operator?
Yes. The Resource Routing World Model is scaffolded end-to-end. 21 signals, 39 segment primitives, 11 JTBD experiments, 3 intervention tiers, one Snowflake substrate. Pilot on 83 accounts confirms detection. Projection: 1.3M to 1.6M dollars of annualized value across retained ARR, released capacity, and expansion conversion.
The MEDIUM-ITBD cohort carries the largest ARR share at 40.8% and is currently served the same way as HIGH (manual, GTME-led). Automating this slice is where the model pays.
Apollo's managed book generates intervention-worthy signals faster than a human team can hear them. The world model makes hearing cheap.
The visible intervention surface is under 20% of what's already firing.
Real extraction from Jason De Leon's managed BoB, 2026-04-18 snapshot. Every count below is directly observed. Hover any signal card for the top five accounts by ARR.
Medium ITBD is the single biggest unserved slice.
Two signals dominate the failure surface. Both are automatable.
Dorsey framework applied to Apollo. Raw capabilities feed a unified World Model. Intelligence layer routes. Surfaces deliver. Adapted from Eric Siu's Single Brain implementation, scaled to the GTM book.
"The model isn't the AI. The model is the data structure that lets the AI understand your specific business. It takes months to build because the data has to accumulate." The 83-book is month one. Apollo-wide is month three onward.
39 primitive segments compose into 150 to 300 materially populated cohorts. Bit-flag composition. Query by intersection. These primitives make the experiment framework addressable.
Team 12345 carries B1 + B4 + U2 + F3 + F11 + F14 + J3 + J7 = "AI + Workflow active mid-market Pro managed team doing AI adoption and workflow automation." Targetable cohort. Specific experiment. Specific resource.
Click any card to expand the full experiment spec. Metrics tagged V are extracted from Jason's book, P are projected, A are assumed pending full-book scoring.
Every fired signal routes through this decision tree. Tier promotions are automatic on failed attempts. Human time is reserved for cases where it matters.
Automated delivery. Email drafts, Slack nudges, in-app prompts, pre-built resource library lookup. Default path for every signal with a matching resource and a clean fit.
Dynamic generation. Tailored messaging, modified workflows, content-engine drafts. Triggered when 3+ JTBD segments active, Enterprise plan, or Tier 1 failed twice.
Live enablement, deployment support, hands-on intervention. Fires on composite_risk_score CRITICAL, Enterprise + Expansion, or Tier 2 failure. Meetings reserved for these cases.
Automating Tier 1 plus Tier 2 alone converts 85% of today's intervention volume from meeting-bound to resource-routed.
Every row below is grounded either in the 83-book extraction or in a stated projection method. Finance should validate the two highest-risk assumptions (baseline churn rate, expansion conversion lift) before the deck converts to funding commitment.
Churn prevention and capacity release are roughly equal. Expansion is small but most sensitive to early wins.
Monthly intervention volume ramps 8x in year one as coverage extends to the full paying book.
Deliverability repair (J11) carries both the largest cohort and the largest lift. AI adoption (J4) is the most sensitive to early wins.
Annual cost: approximately $145K (Snowflake credits plus inference plus 0.75 FTE operations). Annual value: $1.44M midpoint. Revenue-to-cost ratio: 9.9x. Payback: roughly 5 weeks. Break-even: 140 teams served, 10% of managed book.
| Scenario | Cohort basis | Low | Mid | High | Confidence |
|---|---|---|---|---|---|
| A. Churn prevention 140-210 CRITICAL teams × ARR × 10pp lift |
Projected CRITICAL cohort | $594K | $649K | $704K | Medium |
| B. Capacity released 4.3 to 4.7K hours × $150/hr blended |
Tier 1 + Tier 2 volumes | $651K | $680K | $709K | High |
| C. Expansion captured 210 J2 teams × +6pp conversion × $8.5K |
J2 Adopt-Expand cohort | $71K | $107K | $143K | Low to medium |
| TOTAL | $1.32M | $1.44M | $1.56M |
Week 1 is already shipped. The remaining three weeks are scoped, scaffolded, and waiting on one DataOps cycle to unblock the full-book run.
Everything downstream of these three asks is scaffolded. The decision needed this month is whether to fund the productionization slot in week 5.
Three allocations this sprint. Everything else is already sitting at the pre-launch line.
One cycle to stand up the GTME_ANALYTICS Snowflake schema. Full DDL bundle ready at Douglas/deliverables/snowflake-ddl-gtme-analytics-2026-04-22.sql
Ongoing ownership of the resource library. Signal-to-resource mapping, threshold tuning, authoring pipeline for five gap resources.
Week 5 productionization slot for the routing layer plus in-app surface hooks. Hourly scheduled extract to GTME_ANALYTICS and Iris Notion sync.
$1.3M to $1.6M in annualized value. 1.5 GTME FTE of capacity released, without adding headcount. 880K monthly signal-moments move from invisible to actionable. The 760 uncovered teams start getting intervention without waiting for a meeting slot.