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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.
Developer API suite (Reader, Embeddings, Reranker) that turns web content into LLM-ready data for search and RAG.
AI21 Maestro is an enterprise framework that optimizes cost, accuracy and latency of production AI agents.
No public pricing
Free trial available
No public pricing
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
- ✦Free AI sports picks and predictions
- ✦AI Daily Parlay Builder (Premium)
- ✦Free AI Cheat Sheets
- ✦Contact form for support
- ✦Reader API converts URLs to Markdown
- ✦Multimodal multilingual embedding models
- ✦Reranker for stronger search relevance
- ✦Web search endpoint returning SERP data
- ✦MCP server for use inside LLMs
- ✦Native inference inside Elasticsearch
- ✦Agent cost optimization
- ✦In-flow result validation
- ✦Advanced RAG data extraction
- ✦Dynamic planning and orchestration
- ✦Cost attribution and budgeting
- →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
- →Using AI-driven insights to make more informed sports betting decisions.
- →Building daily parlays with automated AI selections (Premium).
- →Ground LLMs with clean web content
- →Build semantic and RAG search
- →Rerank retrieved results
- →Give AI agents live web access
- →Optimizing production AI agents
- →Reducing agent compute spend
- →Improving RAG accuracy
- →Enterprise workflow automation