pAud  ·  Operational intelligence  ·  London

The copilot for your warehouse war room.

Local systems behave correctly. The network doesn’t.

pAud reads across your WMS, OMS and TMS and tells the person on shift what is about to break, while there is still time to act.

Already running with a major UK retailer.

Start freeTour the full product
Lane 3 · SLA breach11:42→ 15:00InboundRackingPackingDispatch
Built by people from
  • Deloitte
  • IBM
  • Fuse Energy
  • LSE

It went wrong long before it went red.

Late dispatches, missed cut-offs, overtime to catch up. They aren’t random. They repeat.

  1. Example · sample dataSupplier ASN latereceiving backlogreplenishment latewave starts shorttrailer misses cut-offThe billOrders a day late. A slot fine at the customer’s DC.Seen 9 times in 12 months
  2. Example · sample dataAgency shortfall on nightspick rate drops 02:00–05:00first trailers loaded lateday shift starts behindThe billOvertime on days to catch up. Every time.Seen 14 times in 12 months
  3. Example · sample dataSorter stops for 20 minutesno re-sequencingnext-day orders packed after carrier collectionThe billRe-delivery cost, and customers who don’t come back.Seen 6 times in 12 months
It always looks fine until 15:00.
What the floor saw
Red
What the data showed
  1. ASN updated lateERP / supplier
  2. Receiving backlog buildsYMS / WMS
  3. Replenishment late, Zone CWMS
  4. Pick wave starts shortWMS
  5. Loading behindTMS
  6. Cut-off missedTMS

312 orders a day late · slot fine at the customer’s DC · 6 hours overtime

Every warehouse has a library like this. Most have never seen theirs.

Build yours freeSee it running

How it works.

Reads the operational data your WMS, ERP, TMS and OMS already record. Read-only. Nothing gets replaced.

Your stack
  • WMSPicks, moves and stock positions
  • WCSConveyors, sorters, AMRs
  • DCOReplenishment and cover logic
  • OMSOrders, priorities and promises
  • ERPSAP, Oracle, NetSuite
  • TMSLoads, departures and carrier cutoffs
Read-only
  1. Learn12 months of history
  2. Predicthours ahead, live
  3. Actwhile there is still time
  1. LearnStart here · free

    Reconstructs an event log from the data your systems already record, and builds the failure patterns that repeat, using object-centric process mining. No rip and replace.

    A pattern it learned

    Sale list received with no quantities

    Central releases the list without quantities attached, so replenishment reacts to sale demand instead of planning for it.

    Seen
    8 times in 12 months
    Median disruption
    244 orders per occurrence
  2. Predict

    Flags the breakdown hours ahead, scored against live signal.

    The reasoning chain
    Every detection and prediction traced back to the events it was drawn from, so the shift manager can check it rather than trust it.
  3. Act

    Ships the ranked fix to the person who can authorise it.

    The decision surface
    The interventions that were actually available on the day, ranked, each with its simulated outcome.

What did your warehouse know before your last bad day?

Try it yourself

pAud AI Lite · free

Send the exports your systems already keep. In 3 days, see the failures that repeat, how often they hit, and where they start. Your data is deleted within 14 days.

Start your library

Do it with us

Guided

Send us 6 to 12 months of data from one DC. We build the library with you, then run a pilot on live data. No integration, no live system access to start.

Talk to us