Responsible Gaming, Fraud & AML AI for iGaming
Three AI models that detect at-risk players, fraud, and money laundering before transactions tell the story.
Your data. Your models. Your platform.
Plug into the tools your team already uses
Every operator deserves
better than a black box
AIRG replaces opaque, single-domain vendor tools with three purpose-built AI models you own and control. Explainable, on-premise ready, and built for regulators.
The Problem
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.
The Solution
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.
The Difference
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.
Enterprise-Grade AI
Compliance Platform
Three specialized AI models on a shared transformer architecture — responsible gaming, fraud detection, and AML compliance running in parallel with sub-50ms latency. Purpose-built for US iGaming operators.
ORBIT — Responsible Gaming
Hierarchical transformer analyzing events at event, session, and player levels with DSM-5 aligned behavioral features for problem gambling detection.
AEGIS — Fraud Detection
Gated fusion of behavioral and fraud-specific signals detects bonus abuse, bot activity, organized fraud, and identity theft in real-time.
TRACE — Anti-Money Laundering
11 AML-specific transaction features including near-$10K structuring detection, wagering ratio analysis, and rapid fund movement tracking.
Real-Time Scoring
Sub-50ms GPU inference across all 3 models in parallel. REST, NDJSON streaming, and WebSocket APIs with per-event predictions.
Explainable AI
SHAP values, attention visualization, and feature attribution for every prediction. DSM-5 criteria mapping for assessments.
On-Premise Deployment
Deploy on your infrastructure. Player data never leaves your environment. Critical for data-sovereign jurisdictions.
Three AI Models. One Platform.
State-of-the-art multi-head attention transformers that process each event in real time — building sequential behavioral context for responsible gaming, fraud, and AML as every action unfolds.
ORBIT
Operator Risk Behavioral Intelligence Transformer
A real-time hierarchical transformer purpose-built for gambling behavior analysis. Processes each event as it arrives through multi-head attention across event, session, and player levels — building a sequential behavioral profile that detects problem gambling patterns before they escalate.
Prediction Tasks
Unified Risk Scoring
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 Events to
Actionable Insights
Three AI models processing every gambling event in parallel — delivering compliance-ready risk scores in milliseconds.
Ingest Events
Send gambling events and transactions via REST API, NDJSON streaming, or WebSocket in real-time.
Read MoreProcess & Tokenize
Events are tokenized with model-specific features — 7 for ORBIT, 9 for AEGIS, 11 for TRACE.
Read MoreParallel AI Scoring
All 3 models run simultaneously on GPU with normalized [0-1] risk scores in under 50ms.
Read MoreIntervene & Report
Automated interventions with full explainability, audit trails, and compliance reporting.
Read MoreAgentic Control Plane
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.
Continuous Agentic Delivery Loop
Write a story → Claude orchestrates → QA validates → Rejected? Loop back. Approved? Ship it.
Operator writes feature request with acceptance criteria
PM agent reads story, breaks into tasks, assigns agents
FE, BE, ML, Data, DevOps agents implement in parallel
Browser + unit tests produce severity-ranked reports
0 Blockers + 0 Highs = Approved. Otherwise → loop
Each loop is fully autonomous. Operators only touch Step 1 and Step 5.
Operator writes feature request with acceptance criteria
PM agent reads story, breaks into tasks, assigns agents
FE, BE, ML, Data, DevOps agents implement in parallel
Browser + unit tests produce severity-ranked reports
0 Blockers + 0 Highs = Approved. Otherwise → loop
Capabilities
Story-Driven Development
Describe features in plain English. The PM agent breaks stories into tasks, assigns them to specialized engineers, and manages the full delivery lifecycle.
Autonomous Iteration
The delivery loop runs continuously — define, build, test, evaluate — iterating automatically until all quality gates pass or the iteration limit is reached.
Quality-Gated Delivery
Every cycle produces QA reports graded by severity (BLOCKER, HIGH, MEDIUM, LOW). Only zero-blocker, zero-high results pass the evaluation gate.
Full Observability
Real-time dashboard showing agent activity, task progress, QA findings, delivery history, and live console output — all sourced from the actual filesystem.
10 Specialized Agents
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
Built for US Regulatory Compliance
Pre-configured for all 38+ US states with legal gambling. Meet AI/algorithmic trigger requirements out of the box.
Get CompliantState Compliance Status
+ 28 additional states with active configurations
Be the first to know when AIRG launches
We'll send you product updates, launch timeline, and early access invitations. No spam, ever.