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AI layer for ERP, WMS and MES AI on top of the systems you already run.

Torno connects to your ERP, WMS or MES, reads the data you already capture, and warns you before margin, materials or ship dates slip, with the next action for each. No rip-and-replace.

Bring us one workflow → See what it finds
Supplier drift +4.1 daysSupplier K vs quoted lead time
Before the runLine 3
Part 7741 · 2,400 units
6.0%expected scrap · line average 2.1%
Do this first: switch to resin lot 2290 and pair the new operator for the first hour.
Illustrative data
What it is

A layer of intelligence, not another system.

Your ERP records what happened. The Torno layer reads that record every day, learns how your operation behaves, and tells you what is about to go wrong and what to do about it. Your system stays exactly as it is.

  • Read-only by default. Nothing in your system changes.
  • First findings in weeks, on one workflow and your own data.
  • A person decides. The layer recommends; your team acts.
Not another report

Most tools stop at what happened. Torno tells you what will happen, and what to do.

1

What happened

Reporting

Scrap on Line 3 hit 4.8% last month.

2

Why it happened

Analysis

One resin lot from Supplier C, and a new operator on second shift.

3

What will happen

Prediction

Tomorrow’s run of part 7741 has a 6% scrap risk.

4

What to do now

Action

Switch to lot 2290 and pair the operator for the first hour.

It reads every record, every day, with the full history in view, so it doesn’t overlook what a busy team would.

What it finds

Where margin, materials and dates slip, found before they cost you.

Scrap and waste

Materials bought against goods shipped, by job, line, shift and supplier lot. Warns before a run that is likely to scrap.

Manufacturers

Cost-to-serve

True margin by order, customer and channel after freight, returns, rework and payment terms. Flags when loss-making orders start to climb.

Manufacturers · Distributors

Supplier and delivery drift

Lead times slipping and orders likely to miss their ship date, with the reason and a fix, while there is still time to act.

Manufacturers · Distributors

Inventory

SKUs about to stock out and stock that has stopped moving. Reorder against predicted demand, not last month’s rush.

Distributors · Manufacturers

Pricing and quoting

Quotes and discounts that land below the cost to serve, and the customers where it keeps happening.

Distributors · Manufacturers

Capacity and bottlenecks

Which line, cell or dock will jam next week, and which jobs to move before it does.

Manufacturers · Distributors
What you see

Each order at risk, the reason, and the fix.

An example for a distributor: the layer reads purchase orders, receiving dates, stock by warehouse and promised ship dates, and every hour lists the orders likely to miss.

  • What it findsThe inbound PO, warehouse or carrier behind each slipping order.
  • What it predictsThe chance each order misses its date, updated as the day changes.
  • What you doShip from another warehouse, split the order, or chase the supplier today.
Delivery risk · distributorIllustrative
Orders due this week1,940
At risk of missing23
Warning given2.8 days early
Fill rate95.4% target 97%
Orders at risk, with the cause and the fixupdated hourly
OrderShipsRiskWhy it’s at riskDo this
51220Thu78%Inbound PO from Supplier K running 4 days lateShip from the Dallas warehouse (in stock)
51236Thu66%Pick backlog at Columbus over capacitySplit: 60% today, rest Friday
51241Fri52%SKU 88-104 will stock out WednesdayRaise the open PO by 400 units
51258Mon38%Carrier pickup slots full on FridayBook the second carrier today
ImpactNo expedited freight

on 19 of 23 at-risk orders, because the warning came while rerouting was still cheap.

Questions it answers

Built for the questions your team asks every week.

Manufacturers

  • Which jobs lose money after setup, scrap and rework?
  • Which line, shift or supplier lot drives our scrap?
  • Which orders will miss their date, and why?
  • Where will capacity run out next week?

Distributors

  • Which customers cost more to serve than they pay?
  • Which SKUs will stock out, and which have stopped moving?
  • Which inbound POs are drifting, and what does that put at risk?
  • Where is freight eating the margin?

Print-on-demand or decorated apparel? See the Print-on-Demand page →

How it plugs in

No rip-and-replace. It reads what you already have.

  1. 1

    Connect

    Read-only access to your system.

  2. 2

    Map

    Understand your data, and what’s missing.

  3. 3

    First findings

    On one workflow, on your own data.

  4. 4

    Review

    Go through the results with you.

  5. 5

    Expand

    Add the next module only if it earns it.

If the data isn’t captured

We extend the module that should record it.

Where a system is holding you back, our Sprint Pods modernize it without stopping operations.

Modernize & Integrate →
If the module doesn’t exist

We build it.

Where you need something new, we build it AI-native and connect it to the layer.

AI Product Engineering →
A flat annual subscription per module, scoped with you.

We build it and prove it on your data first. The subscription starts only once it works. No one-time project fee.

Your data stays yours

Built on security, governance and compliance.

Security

  • Read-only access by default.
  • Your data stays in your environment.
  • Works with your existing access controls.

Governance

  • Never shared with other clients.
  • Never used to train anyone else’s models.
  • A full log of what the layer read and suggested.

Compliance

  • Bring your own LLM: connect the model account your organization already approves.
  • You decide which data the layer can see.

Your competitors never see your numbers, and you never have to trust a black box with them.

Built inside a live operation

Proven first at Stakes Manufacturing.

We spent more than a year embedded with a print-on-demand apparel manufacturer, modernizing its ERP module by module without a single production pause. The AI layer we built there now shows where spoilage is happening across the company.

Read the Stakes case study →
0 pausesProduction kept running through a full ERP modernization.
13+ monthsEmbedded with the Stakes team.
AI layerShows where spoilage is happening across the company.
Trusted by teams at Crateful
FAQ

Questions we hear first.

Do we have to replace our ERP?

No. The layer reads the data your current system already holds, with read-only access. Nothing in your system changes.

Which systems does it work with?

Your ERP, WMS, MES, CRM and spreadsheets, including in-house systems. If we can read it, we can use it; we confirm this in the first call.

What data do you need to start?

Usually what you already have: orders, job or work-order records, purchase orders, receiving, inventory and shipments.

How fast do we see a result?

First findings come from one workflow on your own data, in weeks rather than months.

Is this just another dashboard?

No. It predicts problems before a job or order goes wrong, and recommends what to do. A person on your team decides.

Is our data safe?

It stays in your environment, is never shared or used to train other models, and you can use your own LLM.

What does it cost?

A flat annual subscription per module, scoped with you. We prove it on your data first, and the subscription starts only once it works.

When would you modify or rebuild instead?

Only when your current system is the constraint. Then our Sprint Pods modernize that part or build what’s missing, without stopping operations.

Start here

Bring us one workflow.

The one where margin, materials or dates slip most. Pick one, or bring your own.

Book a 30-minute call → Topic: Scrap & waste
  1. 1
    Pick the workflowThe one costing you most.
  2. 2
    A demo on your dataBuilt on your own records, read-only.
  3. 3
    Pay once it worksThe subscription starts only then.