pAud
pAud provides an operational intelligence platform for warehouse and distribution center teams, using object-centric process mining to predict operational failures before they impact the floor. The platform reads data from existing WMS, OMS, TMS, and ERP systems to identify shared-cause delays and rank potential interventions by simulated outcome. It offers a free AI Lite tier that analyzes historical exports to surface recurring failure patterns within three days.
- Logistics & Supply Chain
- Data & Analytics
- Artificial Intelligence
- Software Only
- Industrial Automation
Funding
Founders
Founder details are not available yet.
Product
Problem
Warehouse operations generate millions of data points daily across WMS, TMS, and OMS systems, but traditional process mining tools flatten this data into single-case timelines that miss shared causes of delays. When a resource like a picker or a packing lane is delayed, the impact appears as hundreds of unrelated order-level anomalies rather than one systemic issue, leaving teams blind to what is actually about to break until it is too late.
Solution
pAud provides an operational intelligence platform that reads across a warehouse's existing WMS, OMS, TMS, and ERP systems to detect emerging risks and predict failures before they impact the floor. Using object-centric process mining, the platform reconstructs event logs that track multiple objects—orders, pickers, vehicles, and dock slots—as interconnected entities, revealing shared-cause patterns that flattened models miss. The system learns from 6 to 12 months of historical data to build failure patterns, then generates predictions with traced reasoning chains, showing exactly when and where a disruption will occur. For each predicted issue, pAud ranks available interventions with simulated outcomes, allowing the person on shift to approve actions directly from the console without writing to or replacing existing systems.
Target Audience
Primary users are warehouse shift managers and operations leaders who need real-time visibility into emerging risks, as well as network-level executives responsible for monitoring exposure across multiple distribution centres.
Features
- Object-centric process mining that models orders, pickers, stations, and vehicles as separate objects to correctly attribute shared-cause delays
- Read-only integration with WMS, WCS, DCO, OMS, ERP, and TMS systems, requiring no rip-and-replace of existing infrastructure
- Predictive control centre that forecasts failures hours in advance, with each prediction traced back to the specific events that triggered it
- Decision surface that ranks available interventions by simulated outcome, letting operators compare baseline versus intervention scenarios side-by-side
- Process explorer and variant explorer that discover the actual operational flow from data, ranking hundreds of process variants by frequency and duration deviation
- Executive view that aggregates risk across multiple sites, showing which locations are exposed and whether interventions are already underway
- Interactive operator console replaying nine hours of a distribution centre at sixty times real speed for hands-on evaluation
- Free AI Lite tier that accepts CSV, Excel, XES, or OCEL 2.0 exports and delivers a failure pattern library within three days, with data deleted within 14 days