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Mobile app giving AI-generated football match predictions across 50+ leagues; betting-focused, accuracy self-reported.
AI odds engine that computes its own match probabilities and compares them to bookmaker lines across 11 sports to flag positive-value bets.
Infrastructure company building large-scale GPU data centers and compute for AI, including Anthropic's compute buildout.
AI21 Maestro is an enterprise framework that optimizes cost, accuracy and latency of production AI agents.
Managed AI ranking engine powering personalized search, recommendations, and feeds via a SQL-like query language.
No public pricing
Free trial available
No public pricing
No public pricing
Free trial available
- ✦ML-based football match predictions
- ✦Confidence ratings per prediction
- ✦Coverage of 50+ leagues
- ✦Real-time match updates and alerts
- ✦Statistical team and player analysis
- ✦Model-derived 'true' odds compared against 40+ bookmakers
- ✦Continuously refreshed positive-edge value-bet stream
- ✦Free live match predictions with confidence percentages
- ✦Coverage across football, basketball, tennis, cricket, and more
- ✦Full audit trail of logged and settled bets
- ✦Chat-based betting bot assistant
- ✦Large-scale GPU and data-center infrastructure for AI
- ✦Power acquisition and data-center design/build
- ✦Fast deployment (gigawatts in ~6 months)
- ✦Operates both hardware and software stack
- ✦Agent cost optimization
- ✦In-flow result validation
- ✦Advanced RAG data extraction
- ✦Dynamic planning and orchestration
- ✦Cost attribution and budgeting
- ✦ShapedQL SQL-style query interface for retrieval and ranking
- ✦Hybrid semantic and keyword search
- ✦Continuous learning from user feedback signals
- ✦30+ native data connectors for warehouses and streams
- ✦Sub-50ms query latency
- ✦Python and TypeScript SDKs plus MCP support
- →Inform soccer betting decisions
- →Track predictions across many leagues
- →Get real-time match alerts
- →Bettors looking for statistically positive-value wagers
- →Users wanting free live win-probability predictions
- →Bettors tracking model performance transparently over time
- →Training and running large AI models at scale
- →Provisioning GPU compute for AI labs
- →Building dedicated AI data-center capacity
- →Optimizing production AI agents
- →Reducing agent compute spend
- →Improving RAG accuracy
- →Enterprise workflow automation
- →Personalizing 'for you' content feeds
- →Building product recommendation systems
- →Powering RAG retrieval with behavioral ranking
- →Adding hybrid search to an e-commerce site