Media Intelligence Pipeline
MediaMedia Knowledge Graph / Data InfrastructureGrowth
Media Intelligence Pipeline
Universal 9-layer media intelligence pipeline — ingest, enrich, and reason over 1.6M+ movies with Story DNA, provenance, and multi-API access
A 9-layer media intelligence pipeline that ingests from 12 real connectors (IMDb, TMDB, Wikipedia, Wikidata, Reddit, OpenSubtitles, YouTube, IMSDb, Rotten Tomatoes, News, Rubika, Kaggle), builds a knowledge graph with Story DNA, and exposes intelligence via REST, GraphQL, MCP server, and Embeddings APIs. 1.68M+ items processed.
9-layer architecture (Discovery → Intelligence → Platform)12 real connectors (IMDb, TMDB, Wikipedia, Wikidata, Reddit, +7 more)1.68M+ items processed (1.4M Kaggle + 218K Rubika + 2K TMDB live)Story DNA — 64-dim embeddings per movieProvenance & Confidence layer (every fact traceable)Multi-API platform: REST + GraphQL + MCP server + Embeddings7 specialized AI endpointsDAG enrichment engine on 1TB storage server
media-intelligence.app.demo
Vision
"Media knowledge is fragmented across dozens of sources. Media Intelligence Pipeline unifies them into one knowledge graph — ingest, extract, canonicalize, enrich, and reason about movies at scale with full provenance and confidence."
Development progress95%
StageGrowth
Est. to MVPProduction-ready now
Suggested pricing$299-$9,999/mo
The problem
Why this matters
Fragmented media data
Movie data scattered across IMDb, TMDB, Wikipedia, scripts, subtitles, Reddit, news — no unified view.
- Dozens of sources
- No canonical IDs
- Manual cross-referencing
- No provenance
No deep understanding
Existing platforms have metadata, not intelligence.
- No Story DNA
- No theme analysis
- No relationship graphs
- No prediction models
No API access
Media data is locked behind walled gardens.
- No GraphQL
- No MCP server
- No embeddings API
- No programmatic intelligence
The solution
How it works
9-layer pipeline
Discovery → Connectors → Storage → Extraction → Canonicalization → Enrichment → Knowledge Graph → Intelligence → Platform.
12 real connectors
Live crawlers for IMDb, TMDB, Wikipedia, Wikidata, Reddit, OpenSubtitles, YouTube, IMSDb, Rotten Tomatoes, News, Rubika, Kaggle.
Story DNA
64-dimension embeddings per movie — genre, pacing, tone, structure, archetypes, themes, symbols.
Provenance & Confidence
Every graph edge traceable to source with confidence score — no black-box facts.
Multi-API platform
REST + GraphQL + MCP server (5 tools, 12 resources) + Embeddings API for similarity search.
Capabilities
Key features
The core capabilities that make this product distinctive.
Dashboard
Portfolio overview with ingestion stats, graph metrics, and intelligence queries
Browse Media
Search and filter 1.68M+ movies with enriched metadata
Movie Workspace
6-tab deep dive per movie — overview, graph, Story DNA, themes, relationships, predictions
Graph Explorer
Interactive 5-subgraph visualization (Story, Audience, Media, Production, Social)
Intelligence Query
Natural language queries grounded in the knowledge graph with confidence + provenance
Recommendations
DNA-based movie recommendations with narrative explanations
Compare Movies
Side-by-side radar + prediction table + theme overlap analysis
Timeline
Chronological media evolution and trend analysis
Catalog Insights
Aggregate analytics across the full 1.68M-item catalog
Ingestion Pipeline
Visual DAG of the 9-layer pipeline with real-time processing status
Layer Inspector
8 layer-specific views (L1-L9) with raw data and extraction details
Connector Manager
12 connector configs — TMDB, Rubika, Kaggle, Wikipedia, Reddit, +8 more
Under the hood
Architecture
9-layer vertical pipeline + cross-cutting provenance layer
1
L1 DiscoveryConnector registry + source discovery
Find and register media data sources
2
L2 Connectors12 live crawlers (HTML, REST, SPARQL, JSON)
Acquire raw data from IMDb, TMDB, Wikipedia, Reddit, etc.
3
L3 Raw Storage1TB remote storage server (PHP API + CORS)
Store raw crawled data with deduplication
4
L4 Extractioncheerio + custom parsers
Extract structured data from raw HTML/JSON
5
L5 CanonicalizationEntity resolution + ID mapping
Unify entities across sources into canonical IDs
6
L6 AI Enrichmentz-ai-web-dev-sdk + 7 AI endpoints
Generate Story DNA, themes, relationships, predictions
7
L7 Knowledge Graph5 sub-graphs (Story/Audience/Media/Production/Social)
Build interconnected knowledge per movie
8
L8 IntelligenceQuery engine + recommendation engine
Derive insights, predictions, recommendations
9
L9 PlatformREST + GraphQL + MCP + Embeddings
Expose intelligence via multiple API surfaces
10
Cross-cuttingProvenance + Confidence tracking
Every fact traceable to source with confidence score
Built with
Tech stack
Frontend
Next.js 16TypeScriptTailwind 4shadcn/uiRechartsFramer Motion
Backend
PrismaSQLiteZustandz-ai-web-dev-sdk
Data Ingestion
cheerioCustom crawlersDAG enrichment engine
Storage
1TB remote serverPHP APICORS-enabled
APIs
RESTGraphQLMCP serverEmbeddings (64-dim)
Infrastructure
Caddy reverse proxyDockerStandalone build
The path
Roadmap
Where each product stands and where it's going.
Phase 1Done
Core pipeline + connectors
- 9-layer architecture
- 12 connectors
- Storage server
- DAG engine
Phase 2Done
Knowledge graph + AI
- 5 sub-graphs
- Story DNA
- 7 AI endpoints
- Provenance layer
Phase 3Done
Platform APIs
- REST
- GraphQL
- MCP server
- Embeddings API
Phase 4Planned
Scale + monetize
- Paid API tiers
- Additional connectors
- Real-time ingestion
- Enterprise SSO
For partners
Investment & partnership
An honest look at what's done, what's missing, and what I'm looking for.
What's done
- 9-layer pipeline architecture
- 12 real connectors (IMDb, TMDB, Wikipedia, Wikidata, Reddit, OpenSubtitles, YouTube, IMSDb, RT, News, Rubika, Kaggle)
- 1.68M+ items ingested and canonicalized
- Story DNA (64-dim embeddings)
- Provenance & Confidence layer
- 7 specialized AI endpoints
- REST + GraphQL + MCP + Embeddings APIs
- 25-section dashboard UI
- DAG enrichment engine
- 1TB storage server deployment
What's missing
- Paid API tier gating
- Real-time ingestion (currently batch)
- Enterprise SSO
- Additional connectors (Letterboxd, Trakt)
Looking for
Strategic InvestorTechnical PartnerData Partner
Needs
- API infrastructure partner
- Streaming platform design partner
- Media industry advisor
- Cloud compute credits for AI enrichment
Market potential
$50B media analytics TAM — streaming platforms, studios, and research institutions need unified media intelligence
Competitive advantages
- 9-layer pipeline is architectural IP — not a wrapper
- 12 real connectors vs competitors' 1-2
- 1.68M+ items at scale vs curated datasets
- Provenance on every fact — auditable and trustworthy
- Multi-API platform (REST + GraphQL + MCP + Embeddings) — no competitor offers all four
- Story DNA construct — unique 64-dim movie understanding
Target audience
- Streaming/OTT platforms
- Media analysts
- Recommender system builders
- Content licensing teams
- Media researchers
- Film industry data scientists
Business model
- API consumption — Pay-per-call for REST/GraphQL/MCP/Embeddings APIs
- Data licensing — Licensed access to the 1.68M-item canonical dataset
- Platform seats — Per-seat SaaS for the intelligence dashboard and workspace
Investor Intelligence
Valuation & Documentation
Estimated Valuation
AI-powered$11.5M$9.5M - $14.0M
Berkus + Scorecard + Risk Factor SummationUpdated 8/1/2026Confidence: 65%
Valuation Factors
Market Size (TAM)
920%
Team & Execution
715%
Product Completion
815%
Competitive Moat
915%
Revenue Potential
715%
Scalability
810%
Timing & Market Readiness
810%
⚠️ This is an AI-generated estimate for reference only. Actual valuation depends on due diligence, market conditions, and negotiation.
READMEproject documentation
Sensitive data (tokens, API keys, passwords) has been redacted
Watch the Media Intelligence Pipeline promo
A 30-second overview of the product vision and value proposition
Try the Media Intelligence Pipeline demo
An interactive mock demo with realistic data, dashboards, and an AI assistant. No real backend — just the experience of using the product.
Mock data only · No source code exposed · Safe to explore
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