From raw data to real-time decisions. VenueOS ingests, simulates, recommends, and learns — continuously optimizing every revenue lever in your venue.
Every component engineered for clarity, responsiveness, and absolute control.
Continuous venue telemetry stream with sub-second latency across all revenue channels.
Dynamic recommendation pathing based on real-time load and operator capacity.
Automatic state failover ensuring zero data loss during live events.
Reinforcement learning calibrates pricing and staffing variables in real time.
Architecture
Tickets, POS, CRM, staffing CSV data flows through Bronze → Silver → Gold → Star quality layers
Monte Carlo fan-level simulation across 7 game intervals with configurable parameters
AI generates pricing, staffing, and F&B recommendations with confidence scores
Approved recommendations deploy through downstream systems with full audit trail
RL experience buffer captures outcomes, calibration loop converges model accuracy
Capabilities
Monte Carlo engine generates fan-level behavior predictions across 29 sections and 7 game intervals. Run what-if scenarios with 4 sliders: pricing, staffing, inventory, promo.
Throughput
150K+simulated transactions per run
Claude-powered natural language recommendations with full lifecycle: Review → Approve → Execute → Reconcile. Role-based access control with approval levels.
Output
200+recommendations per season
30-dimensional feature vectors feed a bandit-style policy trained on real outcomes. Experience buffer captures every action-outcome pair for continuous improvement.
Training Data
500+RL episodes in training buffer
15,000 fan profiles with identity resolution across CRM sources. 6 behavioral segments from Season Holders to Lapsed with churn prediction and LTV scoring.
Segmentation
6behavioral segments
WebSocket-powered 2-second update pulse broadcasting revenue, occupancy, star signals, and cognitive traces to connected operators in real-time.
Latency
2-secondupdate pulse
Exponential learning rate decay with cross-game drift detection. Model accuracy converges from 18% error to under 3% over 30 calibration cycles.
Accuracy
18% → 3%error convergence
Integrations
59 API endpoints. Full OpenAPI spec. Webhook reconciliation built-in.