ORE is a Supply Foundry platform

Operational Response Engine

Signal in the noise. Clarity in action.

ORE connects your orders, inventory, suppliers, freight and customers, then answers the questions your team actually asks. A real calculation engine works out every figure first. The AI explains what it means and what to do next.

Connects to the systems you already run

  • MYOB Acumatica
  • Shopify
  • WooCommerce
  • HubSpot
  • SAP via OData
  • Microsoft Dynamics
  • SolBox
  • CSV, Excel and databases

See it answer

Ask it the way you'd ask your operations manager.

Pick a question. The figures here are example data, but the shape is exactly what ORE gives you: the finding first, the evidence underneath and the next action attached.

Every figure is computed from your connected data before the AI says a word about it. Bring your own question to the demo and we'll run it live.

Book a demo with your question
ore · inventoryExample data

Which SKUs will stock out in the next 14 days?

Three SKUs will run out before their next delivery can arrive. The most urgent is SKU-0225 Olex TPS Standard: 194 units on hand is about 11 days of cover, against a 14-day supplier lead time.

SKU-0225 Olex TPS Standard11 daysSKU-2204 Pallet wrap 500mm12 daysSKU-0418 Shipper carton 600x40017 daysSKU-1107 Packing tape 48mm38 days
Days of coverSupplier lead time

Next actionRaise a purchase order for SKU-0225 today. The supplier and suggested quantity are already filled in.

Days-to-stockout model, computed in 31 ms

ore · logisticsExample data

Why did our freight cost jump this month?

Freight rose $28.8K, from 6.1% to 7.4% of order value. Most of it came from Meridian Freight's regional runs, where the average delay also climbed to 2.1 days and orders were upgraded to express to catch up.

Meridian Freight, regional runs+$18.4KExpress upgrades on late orders+$6.1KFuel levy change+$3.2KEverything else+$1.1K
Share of the increase

Next actionCompare Meridian's regional rates against your two other carriers. Atlas has the lane-by-lane view ready.

Root-cause breakdown, computed in 44 ms

ore · customersExample data

Which customers should we be worried about?

Globex Corp ordered 45% less this quarter, putting $312K of annual revenue at risk. Two more accounts are cooling: order frequency is down and the gaps between orders are getting longer.

Globex Corp24Initech Wholesale41Umbrella Supply Co47Acme Retail Group82
Health score

Next actionA Signal has gone to Leo in sales with Globex's order history attached, so the call happens this week.

Customer health model, computed in 27 ms

ore · forecastExample data

Forecast revenue for the next 90 days.

Revenue is trending up, projected to grow 12% over the next 90 days, from $1.84M to $2.06M. The range widens further out, so treat the last month as a band, not a promise.

Last 12 weeksNext 90 days$2.06M
ActualForecast with range

Next actionDemand for your top 20 SKUs has been re-forecast too. Reorder points update automatically.

Seasonal forecast model, computed in 28 ms

The principle

The AI is the narrator, not the calculator.

Generic AI tools hand raw rows to a language model and hope it spots the trend. It can't reliably compute a percentage, has no statistical basis for saying revenue is declining, and will confidently invent a number.

ORE works the other way round. Every figure is computed by a dedicated analytics engine first, and the model is only allowed to explain the finished result.

How the platform works
  1. Your question

    "Which carriers are blowing out delivery times?"

  2. Intent

    Works out what you are really asking: a ranking, a trend, a comparison or a root cause.

  3. Query

    Pulls exactly the rows it needs from your connected systems.

  4. Analytics enginecomputes

    Computes totals, changes, correlations and statistical checks.

  5. Structured result

    Exact figures, with the chart and the evidence attached.

  6. AI narrativeinterprets

    Explains the finished result in plain language. It is told not to recalculate.

  7. Answer and action

    The finding first, then the chart, then what to do next.

How it works

From scattered data to the next action.

  1. 1

    Ingest

    Connect your ERP, eCommerce store, WMS or spreadsheets. ORE maps them into one structured picture of orders, stock, freight, suppliers and customers.

  2. 2

    Analyse

    The engine computes rankings, trends, comparisons, correlations and forecasts, with real statistics behind every one.

  3. 3

    Answer

    Ask in plain language. The reply leads with the finding that matters, with the figures and chart underneath.

  4. 4

    Act

    Prospectors raise Signals to the right person and reports arrive on schedule. The next step is always attached.

What it does

One engine, six ways to stay ahead.

Ask in plain language

"Top customers by gross profit this quarter" gets a ranked answer and the right chart, straight from your own data.

90-day forecasts

Revenue and SKU demand with upper and lower bounds, deliberately conservative when there isn't much history.

Anomaly detection

The order, freight cost or supplier behaving differently from the norm, tagged critical, high, medium or info.

Prospectors

AI analysts for inventory, purchasing, sales, customers, logistics and finance. They run every hour and report what changed.

Signals

Share a finding with a note, @mention a colleague and keep the decision attached to the data that prompted it.

Briefs and reports

Ask for the whole picture, or have monthly operations and supplier reviews generated on schedule as board-ready PDFs.

Built by Supply Foundry.

ORE comes from a Brisbane operations consultancy that also designs and builds warehouse, transport, CRM, ERP and payroll systems. If your data is scattered across spreadsheets and disconnected tools, we can sort that out first, then point ORE at it.

Bring the question your current tools won't answer.

Book a 30-minute demo on real supply chain data. We'll show ORE computing the answer, then talk through what it would take to point it at yours.