MONTREAL.AI / SKILLOS

# Autonomous RSI Revenue Experiment Factory

Recursive self-improvement on causal revenue experiments, growth guardrails, and scalable profit capture.

Current status
PASSED_AUTONOMOUS_RSI_REVENUE_EXPERIMENT_FACTORY_MARKET_PROOF

No human review. No customers. No private data. No API keys. No previous-domain reuse. Deterministic holdout benchmark.

+93.9 pts fully-correct gain
82.4%value capture
96 causal confidence
$17,063,901,894.23 synthetic annual value captured

## Recursive self-improvement curve

## Before / after on holdout revenue-experiment states

Metric | Baseline | SkillOS RSI
--- | --- | ---
Fully correct decisions | 6.1% | 100.0%
Growth-state accuracy | 6.1% | 100.0%
Experiment-design accuracy | 6.1% | 100.0%
Intervention accuracy | 6.1% | 100.0%
Guardrail accuracy | 6.1% | 100.0%
Value capture rate | 1.4% | 82.4%
Causal confidence score | 11.6 | 96
Material miss rate | 60.5% | 0.0%
False positive rate | 27.9% | 0.0%
Avg experiment cycle | 31.674 days | 0.386 days

## Final learned revenue-experiment skills

- skill_price_elasticity — Detect pricing upside and run a segmented elasticity test before a broad price change.
- skill_activation_bottleneck — Detect onboarding activation bottlenecks and fix the first-value moment before scaling acquisition.
- skill_sales_cycle_compression — Detect enterprise sales-cycle friction and compress the cycle with reusable evidence packs.
- skill_lifecycle_retention — Detect retention lift from lifecycle nudges and validate with randomized holdouts.
- skill_cannibalization_guard — Detect cannibalization risk and pause gross-booking campaigns until net-new incrementality is proven.
- skill_segment_heterogeneity — Detect hidden segment winners and avoid losing gains in averaged results.
- skill_lagged_retention — Detect lagging retention metrics and validate proxy metrics before scaling.
- skill_cross_sell_sequence — Detect cross-sell sequence opportunities and trigger only after activation milestones.
- skill_paid_saturation — Detect paid-channel saturation and stop spending past the marginal return frontier.
- skill_margin_guardrail — Detect growth that destroys margin and block rollout until profit guardrails pass.
- skill_high_intent_channel — Detect underallocated high-intent demand and scale it only with incrementality and CAC-payback guardrails.
- skill_power_analysis — Detect underpowered tests and redesign sample size before deciding.
- skill_novelty_effect — Detect novelty-effect false positives and require delayed readout before rollout.
- skill_referral_network — Detect referral network potential and seed it in high-trust cohorts with fraud guardrails.
- skill_seasonal_window — Detect seasonal demand windows and validate lift with geo holdouts.
- skill_trial_abuse — Detect free-trial abuse and add verification without harming legitimate conversion.
- skill_seo_compounding — Detect compounding SEO opportunities and fund topic clusters tied to qualified pipeline.
- skill_clean_hold — Recognize clean growth states and avoid unnecessary experiments.

## Proof gates

- ✅ business domain scalable revenue experiment factory workflow
- ✅ not email workflow
- ✅ not invoice workflow
- ✅ not cloudops workflow
- ✅ not cyberdefense workflow
- ✅ not silicon workflow
- ✅ not metamaterials workflow
- ✅ not generic corporate os workflow
- ✅ not unit economics profit engine workflow
- ✅ not marketplace flywheel workflow
- ✅ no human review required
- ✅ no customers contacted
- ✅ no private data used
- ✅ no api keys required
- ✅ deterministic reproducible benchmark
- ✅ recursive self improvement releases at least 10
- ✅ rsi validation improves monotonically
- ✅ train cases at least 480
- ✅ validation cases at least 240
- ✅ holdout cases at least 960
- ✅ final rules at least 18
- ✅ fully correct gain at least 85 points
- ✅ growth state accuracy at least 99 percent
- ✅ experiment design accuracy at least 99 percent
- ✅ intervention accuracy at least 99 percent
- ✅ guardrail accuracy at least 99 percent
- ✅ value capture rate at least 75 percent
- ✅ causal confidence score at least 90
- ✅ material miss rate zero
- ✅ false positive rate zero
- ✅ experiment cycle reduction at least 90 percent
- ✅ synthetic value captured positive

Boundary: This is a fully autonomous reference proof using deterministic synthetic/redacted-style business data and benchmark assumptions. It is not audited customer ROI, financial advice, investment advice, or a guarantee of future outcomes.

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