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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.
No-code platform to build, share and monetize custom AI-powered tools; solid adoption.
No public pricing
Free trial available
No public pricing
No public pricing
- ✦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
- ✦No-code AI app builder
- ✦Embeddable AI tools
- ✦AI model training
- ✦AI tool sharing and management
- ✦Prompt engineering integration
- ✦AI Studio creation
- →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
- →Creating and embedding GPT-4 apps in websites
- →Training AI on specific expertise using documents and data
- →Building AI tools for online courses, online stores, dashboards, and blogs
- →Automating tasks in Google Sheets
- →Coding qualitative data