Three AI models that detect at-risk players, fraud, and money laundering before transactions tell the story.
Your data. Your models. Your platform.
Every operator deserves better than a black box
The industry depends on single-domain, cloud-only vendors with black-box models. Operators can't explain decisions to regulators, can't deploy on-premise, and lack coverage across responsible gaming, fraud, and AML.
AIRG deploys three specialized AI models — ORBIT (RG), AEGIS (Fraud), and TRace (AML) — on a shared transformer architecture with embedded MLOps. Train on your data, deploy in your infrastructure.
3 parallel AI models vs. single-domain. Full model ownership vs. black box. On-premise + cloud vs. cloud lock-in. <50ms latency vs. >100ms. DSM-5 aligned explainability vs. opaque scoring.
Three specialized AI models on a shared transformer architecture — responsible gaming, fraud detection, and AML compliance running in parallel with sub-50ms latency
Hierarchical transformer analyzing events at three levels (event, session, player) with DSM-5 aligned behavioral features for problem gambling detection.
Gated fusion of behavioral and fraud-specific signals detects bonus abuse, bot activity, organized fraud, and identity theft in real-time.
11 AML-specific transaction features including near-$10K structuring detection, wagering ratio analysis, and rapid fund movement tracking.
Sub-50ms GPU inference across all 3 models in parallel. REST, NDJSON streaming, and WebSocket APIs with per-event streaming predictions.
SHAP values, attention visualization, and feature attribution for every prediction. DSM-5 criteria mapping for responsible gaming assessments.
Deploy on your infrastructure. Player data never leaves your environment. Critical for NY, tribal, and data-sovereign jurisdictions.
Pre-configured for all US jurisdictions with state-specific rules and audit trails.
AI-triggered interventions via popup, email, SMS, account restriction, or cooldown.
Full incident lifecycle with priority levels, assignment, and audit trail.
Risk trends, Sankey diagrams, intervention effectiveness, and SSE-powered monitoring.
AES-256 encryption, Clerk SSO, RBAC with 6 roles, and signed 7-year audit trails.
REST API, NDJSON streaming, and WebSocket for bidirectional real-time feeds.
Organization-level data isolation with role-based access and per-tenant subscriptions.
Full model lifecycle: register, train, evaluate, validate, deploy, monitor, and retrain.
A unified hierarchical transformer architecture specialized for responsible gaming, fraud detection, and anti-money laundering — running in parallel on every player event.
Operator Risk Behavioral Intelligence Transformer
The first hierarchical transformer purpose-built for gambling behavior analysis. Processes events at three levels — event, session, and player — to detect problem gambling patterns before they escalate.
Events → Event Embedding (7 features) → Session Encoder → Player Encoder → Multi-Task Heads
All three models produce normalized risk scores [0.0 - 1.0], enabling cross-domain risk comparison and composite risk dashboards. Each model runs in parallel on every event with rule-based fallbacks when a model isn't loaded.
From raw gambling events to actionable interventions in milliseconds — three AI models processing every event in parallel
Send gambling events and transactions via REST API, NDJSON streaming, or WebSocket
Events are tokenized with model-specific features — 7 for ORBIT, 9 for AEGIS, 11 for TRace
All 3 models run simultaneously: ORBIT (RG), AEGIS (Fraud), TRace (AML)
Normalized [0-1] scores across all domains with multi-task prediction heads
Automated interventions with full explainability, audit trails, and compliance reporting
Parallel Model Execution
A team of 10 specialized AI agents that autonomously develop, test, and iterate on your platform. Describe what you need in plain English — agents handle the rest.
PM analyzes QA findings, identifies blockers, creates prioritized tasks
Engineer agents implement fixes autonomously in isolated git worktrees
QA agents run browser tests and unit tests, producing severity-graded reports
PM evaluates results — zero blockers means approved, otherwise loop back
Describe features in plain English. The PM agent breaks stories into tasks, assigns them to specialized engineers, and manages the full delivery lifecycle.
The delivery loop runs continuously — define, build, test, evaluate — iterating automatically until all quality gates pass or the iteration limit is reached.
Every cycle produces QA reports graded by severity (BLOCKER, HIGH, MEDIUM, LOW). Only zero-blocker, zero-high results pass the evaluation gate.
Real-time dashboard showing agent activity, task progress, QA findings, delivery history, and live console output — all sourced from the actual filesystem.
Each agent is a domain expert with its own tools, context, and responsibilities. The PM orchestrates the team through the delivery loop.
How a story becomes a feature
Pre-configured for all 38+ US states with legal gambling. Meet AI/algorithmic trigger requirements out of the box.
+ 28 additional states with active configurations
AIRG is in active development. Join the waitlist to get early access and be notified as soon as we're ready to launch.
We'll send you product updates, launch timeline, and early access invitations. No spam.