Evidence● DEMONSTRATED Operating in this session● DESIGNED Methodology for deployment● MODELED Illustrative calculation● DEPLOYMENT Requires real data & timeAI generates language · Jallybean owns the decision architecture · No action executes autonomously
1The Problem why a dashboard isn't enough
Entertainment organizations have abundant signals but no governed system for converting them into accountable decisions.
SIGNAL EXISTS → INSIGHT GENERATED → DECISION UNMADE → OPPORTUNITY MISSED → NO RECORD OF WHY
The performers who become icons are not more talented. They are more intelligently positioned. They detect the greenlight earlier. They submit before the window closes. They know which lever to pull and who owns it. Jallybean makes that systematic rather than accidental — and creates an institutional record of every decision made and every outcome that followed.
Jallybean = Governed Decision Infrastructure
SIGNAL LAYER
Entertainment Intelligence 26 sources · 10 signal types
Emerging: accumulated signal → decision → outcome graph
Workflow Moat
Now: embedded authorization and responsibility model
Emerging: switching = organizational process change
Domain Moat
Now: entertainment-specific signal taxonomy (10 types) + prescription ontology
Emerging: calibrated per-genre, per-role lever models
Calibration Moat
Now: artist-profile-specific thresholds and peer cohort models
Emerging: prescription confidence improves with each confirmed outcome
Switching-cost moat (long-term): Jallybean becomes the institutional memory of the entertainment enterprise. Every decision record, approval event, and outcome attribution is stored. Switching means losing the organizational intelligence that has accumulated.
2One Signal the full thesis on a single illustrative signal
✦ Signal to Outcome — End to End
Every module in this demo is this loop, scaled to all six HoldCo verticals. The architecture is invariant. The prescription layer changes per signal type and artist profile.
SIGNAL DETECTED
DEMONSTRATEDDeadline Hollywood ingested. Classification: GL — GREENLIGHT. Netflix orders 8-episode observational comedy series. Casting director attached. Seeking lead comedic voice 32–45 with touring credibility. Rate: reported range.
RELEVANCE SCORED
DEMONSTRATEDArtist profile match: 87/100. Factors: format match (observational), age window match, touring credential verified, streaming audience tier, casting director prior engagement pattern.
PRESCRIPTION GENERATED
DEMONSTRATED5 specific actions with owner, channel, and deadline. Trigger: GL signal above relevance threshold. Decision window: 14 days before submission efficiency degrades.
APPROVAL GATED
DEMONSTRATEDA-2 authorization: Artist + Manager joint approval required. 24-hour decision window shown in simulation. APPROVE / MODIFY / REJECT — see Act 4.
EXECUTION ROUTED
DEMONSTRATEDOn approval: each action routed to its owner. Deadline tracker opens. Completion status recorded. Nothing executes autonomously.
OUTCOME MEASURED
MODELEDSubmission Day 12 → Callback Day 21 → Chemistry read Day 34. Series engagement: reported rate range × 8 episodes. Downstream HoldCo effects on brand rate and merch velocity: modeled, directional only.
ATTRIBUTION DESIGNED
DESIGNEDOnly the attributed delta is credited. Comparison cohort: comparable submissions in same window without Jallybean prescription. Attribution confidence graded from cohort separation quality.
LEARNING LOOP
DEPLOYMENTOutcome recorded. GL response playbook confidence updated. Casting director affinity weight refined. Prescription quality improves with each confirmed outcome. Requires real deployment data.
Illustrative signal. Outcomes and financial effects are modeled, not measured deployments. Attribution methodology is a design specification, not a demonstrated capability.
3The Decision prescription with full rationale — not a black box
Book Late-Night Appearance — Next Available Window
Trigger: Late-night booking frequency = 0 in trailing 127 days Benchmark: Genre cohort median = 3 bookings per 90-day period Opportunity: True crime comedy cultural moment aligns with demonstrated observational format Expected lever effect: Attention momentum score +6–10 points (estimated range, not a guarantee) Cost: Low — publicist outreach only Reversibility: High Confidence: 72% DESIGNED METRIC
● LIVE AUDIT RECORD — THIS SESSION
Every APPROVE / MODIFY / REJECT generates a timestamped, identity-stamped record. In production: hashed, chained, committed to governance repository. Segregation of duties: proposal (system) ≠ authorization (human) ≠ execution (designated operator).
5The Outcome simulated fast-forward from approval to booking
MODELEDThe timeline below is a simulated fast-forward. Outcome and financial effect are illustrative model outputs, not measured results. Attribution methodology is a design specification. Decision velocity (approval to submission) is the one operationally measured metric shown — it is demonstrated in this session.
Financial Effect MODELED
Series engagement at reported rate range × 8 episodes → guarantee range established
Streaming velocity lift post-credit → directional, not quantified
Brand rate effect post-Netflix credit → directional, not quantified
Merch velocity halo → directional, not quantified
Directional effects are described rather than quantified because the specific magnitudes are not empirically derived. This is a deliberate presentation choice consistent with the evidence framework.
Attribution Design DESIGNED
Intervention cohort: Submissions within Jallybean prescription window Comparison cohort: Comparable-profile submissions without Jallybean Measured delta: Callback rate, submission-to-booking velocity Attribution confidence: Graded from cohort separation quality Credited: Attributed delta only — not gross outcome
6The Career Decision Graph the institutional memory — the actual product
The dashboard isn't the product. The graph is.
Every node in the graph has: timestamp · source · confidence · owner · authorization level · evidence class · outcome · attribution confidence. Over time, this becomes the institutional memory of an entertainment enterprise — and the proprietary data asset that makes Jallybean's prescriptions better than any agency's intuition.
This is not a language model. This is accumulated operating intelligence. DEPLOYMENT EVIDENCE REQUIRED
Career Decision Graph — One Signal Record (Illustrative Structure) DESIGNED
Signal
GL · Netflix Comedy Greenlight Source: Deadline Hollywood Ingested: [timestamp]
DEMONSTRATED
Interpretation
Format match: 87/100 Window: 14 days Urgency: HIGH
Signal detection → human approval. Measures time-to-governed-action. Fast detection is worthless without fast authorization. Target: under 24 hours for A-1; under 48 hours for A-2.
In this demo session: approval-to-routing is demonstrated live. Detection-to-approval is illustrated only.
Outcome Velocity DESIGNED
TBD
Approval → measurable outcome. How long until a governed intervention produces an attributable result in the entertainment cycle.
Genre-specific. Comedy GL cycle: 30–90 days illustrative. Actual measurement requires real engagements.
Attribution Confidence DESIGNED
TBD
Confidence in incremental effect. Graded from cohort separation quality, sample size, baseline stability, intervention contamination. HIGH / MEDIUM / LOW per decision record.
Only HIGH-confidence attributions count toward economic yield claims.
Economic Yield DESIGNED
TBD
Incremental attributable value ÷ platform cost. One prevented missed opportunity or one incremental booking theoretically pays for months of the platform.
The eventual killer metric. Currently TBD: no production deployments. Will be validated through initial customer engagements.
The eventual demonstration that changes the pitch: "Jallybean recommended X interventions under condition Y. Approved Z. Confirmed outcomes: N. Prescription confidence updated from A% to B%." At that point, the pitch stops being "look at this intelligent entertainment dashboard" and becomes "we are building the institutional memory and governed decision infrastructure for entertainment enterprises."
7Executive Board six verticals · one governance layer · one decision graph
Attention Momentum Score DEMONSTRATEDWeights: DESIGNED
The Attention Momentum Score is a decision-support instrument estimating artist attention momentum against a calibrated artist-specific benchmark. It is not a measurement of fame or a guarantee of outcomes. Component weights are illustrative — production weights are calibrated per artist profile and role from deployment data. The 78-point threshold is an illustrative benchmark, not an empirically derived industry standard.
71
ATTENTION MOMENTUM SCORE / 100
APPROACHING — 7 pts to illustrative 78 benchmark
▶ Simulation INTERACTIVE
Adjust inputs. When score drifts below benchmark, engine proposes an intervention. APPROVE / MODIFY / REJECT.
08
0.1M20M
060
Top 100Top 10,000
Score
71
approaching
vs 78 Benchmark
APPROACHING
⚡ PROPOSED INTERVENTION · AWAITING DECISION
Prescribed Action
Calibration: at defaults this matches the illustrative base case (71 pts). The 78 benchmark is illustrative methodology. Production thresholds calibrated per artist at deployment.
Cultural Intelligence & HoldCo Threat Board DEMONSTRATED
What Jallybean isThe governed decision layer between entertainment intelligence and execution. Every signal, decision, approval, and outcome is recorded. That record becomes the institutional memory of an entertainment enterprise.
Who buys itEntertainment professionals running careers as enterprises: performing artists, craft professionals, producers — and their management teams. Economic buyer: manager or HoldCo operator. User: artist + team.
Revenue modelAnnual SaaS subscription per HoldCo entity × vertical count. Recurring and contract-anchored. Pricing range is an illustrative hypothesis, not yet market-validated. One prevented missed opportunity or one incremental booking theoretically justifies months of subscription.
Retention mechanismJallybean becomes embedded in the team's authorization cadence and decision workflow. Switching = losing the accumulated decision graph. The institutional memory is non-portable.
What gets betterPrescription confidence improves with every confirmed outcome. Signal → decision → approval → intervention → outcome. Each confirmed record updates the calibration. This is the data moat that accumulates with time and volume — and does not exist yet. It requires real deployments to build.
Not an LLM. Not a career coach. Not autonomous. The governed decision infrastructure for entertainment enterprises.