An autonomous competitive intelligence platform that tracks 24 companies across 5 scoring dimensions, predicts market movements, and generates actionable business intelligence — running every night without human intervention.
SBPI is a competitive intelligence system built for the micro-drama vertical — a $2B+ mobile entertainment market with 24 tracked platforms ranging from ReelShort ($400M revenue) to early-stage African entrants. The platform produces three types of output:
Every company ranked across 5 structural dimensions. DramaBox leads at 82.8, with ReelShort close at 82.0. Rankings update weekly with full score breakdowns.
Momentum-based predictions for next-week movements. JioHotstar (+9.45 cumulative) and COL Group (+7.25) are the strongest bullish signals. Amazon (-5.8) and Netflix (-5.0) are bearish.
Per-company analysis of breaking news, deal flow, and market events — scored through all 5 dimensions. Material events trigger defensive recommendations.
Most competitive intelligence is reactive — it fires recommendations against every negative signal and creates false urgency. SBPI is designed to be proactive. It maintains a model of each company's strategic trajectory and reserves its recommendations for genuine structural threats — moments where something has changed that the company's existing plan does not account for.
The system actively learns to distinguish signal from noise through a self-improving optimization loop. Over time, it gets better at knowing when to speak up and when to stay quiet.
Each company is evaluated across 5 structural dimensions that capture different aspects of competitive position. Scores range from 0-100. The composite score is a weighted average.
The weighting reflects a structural thesis: distribution is the most defensible moat in mobile entertainment. Content and narrative matter, but without distribution they don't convert. Monetization is weighted lowest because it follows from the other four — if you have distribution, content, narrative, and community, monetization is execution, not strategy.
The platform runs a 9-phase nightly pipeline. Phases 1-6 are the core intelligence engine. Phases 7-9 are the new BI agent system — event research, defensive recommendations, and self-improving signal weighting.
Ingests weekly scoring data, checks last week's predictions against what actually happened, generates new forecasts, scores the quality of evidence behind each ranking, and produces a weekly insight digest.
Uses TPE (Tree-structured Parzen Estimation) to auto-tune 12 prediction parameters. The system gets more accurate over time without human intervention. Currently optimized across direction threshold, confidence base, magnitude bonuses, and mean reversion rate.
New this quarter: event discovery via SerpAPI searches real news sources, defensive BI generates mitigation strategies filtered for strategic relevance, and a signal weight optimizer learns to distinguish material events from noise.
| # | Company | Score | Delta | Tier | Signal | Under Pressure |
|---|---|---|---|---|---|---|
| 1 | DramaBox | 82.8 | +4.0 | Dominant | BULLISH | Community |
| 2 | ReelShort | 82.0 | -2.1 | Dominant | BEARISH | Narrative |
| 3 | Disney | 76.6 | +2.3 | Dominant | BULLISH | Content |
| 4 | iQiYi | 71.2 | +1.2 | Strong | — | Community |
| 5 | Google / 100 Zeros | 63.6 | — | Strong | — | Community |
| 6 | JioHotstar | 62.3 | +4.0 | Strong | BULLISH | Narrative |
| 7 | Holywater / My Drama | 61.6 | — | Strong | — | Community |
| 8 | Netflix | 60.8 | -2.0 | Strong | BEARISH | Content |
| 9 | GoodShort | 58.8 | +1.7 | Strong | BULLISH | Community |
| 10 | CandyJar | 58.6 | — | Strong | — | Narrative |
| 11 | ShortMax | 56.6 | — | Strong | — | Narrative |
| 12 | Lifetime / A+E | 55.5 | +1.4 | Strong | BULLISH | Content |
| 13 | Amazon | 50.2 | -2.6 | Emerging | BEARISH | Narrative |
| 14 | Viu | 48.2 | -1.9 | Emerging | — | Community |
| 15 | GammaTime | 46.1 | — | Emerging | — | Community |
| 16 | COL Group / BeLive | 44.6 | +3.2 | Emerging | BULLISH | Content |
| 17 | VERZA TV | 37.3 | — | Niche | — | Monetization |
| 18 | RTP | 31.3 | — | Niche | — | Content |
| 19 | KLIP | 22.4 | -2.7 | Niche | — | Distribution |
| 20 | Both Worlds / Freeli | 21.5 | — | Niche | — | Monetization |
| 21 | Mansa | 19.4 | +1.9 | Niche | — | Monetization |
Data: W12-2026 (week ending March 23, 2026). Scores are 0-100 weighted composites across 5 dimensions. Delta is week-over-week change. Tiers: Dominant (70+), Strong (55-69), Emerging (40-54), Niche (<40).
The BI agent system (Tracks A, B, C) transforms raw competitive data into actionable intelligence. The key innovation is a built-in filter that prevents over-reaction to short-term noise.
Structural changes to competitive dynamics. Events that could trigger tier transitions within 2-3 weeks if unaddressed. Requires defensive action.
W12 example: DramaBox +4.0 score surge driven by Disney Accelerator validation and $500M valuation signal.
Events worth tracking but not yet actionable. May escalate to material if trend continues. The system watches these without generating recommendations.
W12 example: Netflix mobile redesign acknowledges short-form but lacks production commitment.
Expected volatility within a functioning strategy. Normal weekly fluctuation that does not warrant a response. Filtered out by the signal weight optimizer.
Filtered: Minor app store ranking changes, routine press mentions, industry-wide headwinds.
The signal weight optimizer (Track C) tunes 7 parameters that control the threshold between "this warrants action" and "this is expected variance." It uses TPE optimization — the same machine learning approach used in Phase 6 for prediction parameters.
Materiality threshold: How large a dimension delta must be to classify as material (current: 2.0 points)
Structural change weight: Multiplier for events classified as structural vs. temporary (1.5x)
Competitor action weight: Multiplier for competitor-specific threats vs. market-wide headwinds (1.3x)
Trajectory window: How many prior weeks establish the strategic baseline (4 weeks)
Volatility dampener: How much historical volatility reduces urgency (0.3)
The optimizer runs after each weekly cycle. It evaluates whether past recommendations were appropriate, over-reactive, or under-reactive. Over time, it calibrates the system to match expert judgment — getting quieter when noise is high and more assertive when structural changes occur. Expert validation labels are the ground truth signal. After 8+ weeks, the system transitions to outcome validation: did companies that received MATERIAL recommendations actually experience tier transitions?
The platform maintains two layers of structured knowledge beyond the scoring data:
Built from trade press extraction (The Wrap, Deadline, TechCrunch, Variety). Maps companies, people, technologies, deals, and market forces. InfraNodus graph analysis surfaces structural gaps — disconnects in the competitive landscape that represent opportunity or risk.
ReelShort (0.54) — Category leader, connected to all major clusters
Microdrama (0.34) — Category term bridging Chinese and Western market discussions
GoodShort (0.26) — Y Combinator-backed challenger, bridging VC and entertainment clusters
GammaTime (0.24) — JioHotstar acquisition connecting Indian and global market clusters
Maps how value actually moves through the micro-drama ecosystem using REA (Resource-Event-Agent) accounting principles. Three files decompose the market into agents, resources, and flows.
1. No creator-to-platform flow — Independent creators cannot publish on dominant platforms
2. No cross-platform licensing — Content is siloed per platform
3. Quality tier gap — $5-15K Chinese vs. $50K+ US production, nobody owns the $20-40K middle
4. Genre concentration — Market dominated by romance/melodrama
5. No regulatory response strategy — US-China tensions are existential for market leaders
Chinese tech money — Patient, volume-oriented, vertically integrated (COL Group, Storymatrix)
US venture capital — Milestone-driven, differentiation-focused, exit-seeking (Y Combinator / GoodShort)
Legacy studio budgets — Incremental allocation from existing spend, risk-averse (Netflix, Disney)
Ontology: OWL/RDF (sbpi.ttl, 424 triples)
Store: Oxigraph SPARQL endpoint
Validation: SHACL shape constraints
Queries: 11 SPARQL query library
Bridge: InfraNodus → RDF conversion
Primary: SerpAPI (Google News, 988 searches/month)
Enrichment: Claude CLI (headless research)
Extraction: Firecrawl (authenticated scraping)
KG Analysis: InfraNodus MCP (24+ tools)
Subscription: The Wrap (paywalled content access)
Predictions: 4-method ensemble (persistence, momentum, mean reversion, KG-augmented)
Optimization: Optuna TPE sampler (12 + 7 parameters)
BI Agents: Event impact, defensive recommendations, signal weighting
Scheduler: 9-phase nightly cycle via launchd
Reports: Cloudflare Pages (global CDN, free tier)
Store: Local Oxigraph (HTTP + direct access)
Graphs: InfraNodus cloud
Code: Python 3, git-versioned
Weekly stack rankings across 24 companies. Nightly insight digests. Prediction engine with 4 methods. TPE parameter optimization. Event impact analysis with SerpAPI discovery. Defensive BI recommendations. Signal weight optimization. Knowledge graph with 119 entities. Value flow ontology mapping power structures.
Expert validation loop: Weekly human review of BI recommendations feeds the signal weight optimizer, improving material/noise classification.
Proactive BI agent: Phase 2 — opportunity recommendations (not just defensive mitigation). Architecture is already in the ontology, waiting for 4+ weeks of defensive BI data to calibrate against.
Content intelligence expansion: Deep crawl of The Wrap via Firecrawl. Additional source integration (Variety, Deadline, Sensor Tower data feeds).
Knowledge graph enrichment: Named executives, deal terms, production budgets extracted from ongoing article processing.
Vertical expansion: The content intelligence methodology is designed to be reusable. Same 5-layer approach (Discovery → Extraction → Synthesis → Ontology → Monitoring) applied to adjacent verticals.
Client-facing intelligence products: Per-company BI reports delivered as a service. Each tracked company gets their own dashboard showing where they stand, what happened this week, and what to do about it.
Prediction accuracy milestone: Target KG-augmented method outperforming baseline by 15%+ on directional accuracy with 8+ weeks of training data.