AI for manufacturing

Put your factory’s knowledge to work.

Your team knows the machines, the problems and the workarounds. We help you see where AI could put that knowledge to use, then build with you.

Explore what’s possible

A free 15-minute chat about what your business could do next.

Factory knowledgeExample

Find the fix your team used last time.

Fault reportRepair historyMachine manual

The repair history, together

A similar fault, the recorded repair and the relevant manual instructions, brought together for the technician to check.

The technician checks what applies and approves the work.

Examples of what we could build with your team.

Where AI can help.

Six possibilities to explore. Your team checks the results and approves decisions.

Investigate recurring defects

The same defect keeps appearing. Compare affected batches, inspection results and process changes to give the engineer a starting point.

What it needs

Data to start with: Inspection records, batch traceability, process settings and non-conformance reports.
A focused pilot: One product family and one recurring defect, checked against past investigations.
Your team’s role: Quality engineers establish the root cause and approve corrective action and product release.
What we would measure: Time spent gathering evidence, investigation completeness and repeat defects.

Find past fixes

A machine stops with a familiar fault. Find how it was repaired before, alongside the right manual instructions.

What it needs

Data to start with: Equipment manuals and maintenance logs. Condition monitoring also needs reliable sensor history and failure or inspection records.
A focused pilot: One asset type. Start with fault-history search; validate any early-warning model separately.
Your team’s role: Maintenance staff approve the work and follow site procedures before intervention or restart.
What we would measure: Time to find useful information, missed faults and false alerts during a monitored trial.

Carry work between shifts

Turn scattered shift notes into a handover showing open jobs, repeat problems and what the incoming team needs to check.

What it needs

Data to start with: Shift reports, open actions, version-controlled work instructions and reviewed operator notes.
A focused pilot: One line, with outgoing and incoming shift leaders checking the brief together.
Your team’s role: The shift leader approves the handover. Process owners review knowledge before it becomes an instruction.
What we would measure: Handover preparation time, missing actions and time spent looking for instructions.

Replan after a delay

A material delivery is late. Compare schedules against stock and capacity, and show the planner which customer orders would be affected.

What it needs

Data to start with: Orders, inventory, routings, capacity, lead times and agreed scheduling constraints.
A focused pilot: One production area, comparing suggested schedules with the planner’s existing process.
Your team’s role: Planners approve schedule changes and customer commitments. Constraint checks stay explicit.
What we would measure: Replanning time, constraint violations and delivery performance during the pilot.

Prepare the next quote

An enquiry arrives with incomplete specifications. Find comparable jobs, flag the missing details and prepare a costing sheet for the estimator.

What it needs

Data to start with: RFQ documents, approved costing rules, previous quotes and job records.
A focused pilot: One repeatable product family, with estimates compared against previously reviewed quotes.
Your team’s role: Engineering checks feasibility and specifications. Estimators approve costs, price and lead time.
What we would measure: Quote preparation time, missed requirements and corrections before approval.

Spot a known defect

Flag a missing label, surface defect or damaged part from a camera image. Send uncertain cases to an inspector.

What it needs

Data to start with: Representative labelled images, an agreed defect definition and consistent camera and lighting conditions.
A focused pilot: One part, one defect type and one inspection station, including difficult edge cases.
Your team’s role: Quality staff set acceptance criteria, review uncertain results and retain release authority during the pilot.
What we would measure: Missed defects, false rejects and performance at the required line speed.

You don’t need to choose a project yet. The AI Strategy Workshop helps your team find a useful place to start.

About the workshop

Said Lopez, founder of LeverageAI — **Said Lopez**Founder, LeverageAI · Author of Lean AI

Manufacturing experience.

Before building AI systems, Said worked in manufacturing engineering, new product introduction and process improvement.

We start with your operators, technicians and engineers. Their experience helps us choose what to build.

Read about Lean AI

Questions.

Do we need a fully connected factory to start?

No. A useful first project might work with approved manuals, shift reports or exports from your existing systems. Predictive maintenance and automated inspection need a different level of data and equipment readiness. We assess that before choosing the pilot.

Can this work with our ERP, MES or maintenance system?

We start by checking what your systems can share through APIs, database access or scheduled exports, and what your permissions allow. The first pilot can often use a limited, read-only dataset. We agree any write-back or live integration separately.

Will AI change machine settings or make release decisions?

The examples here support people with information, drafts and recommendations. Your team approves changes to schedules, machine settings, maintenance work and product release. Any future control-system integration would need its own engineering scope and validation.

What happens to our production data and process knowledge?

We agree which information the system can access, who can see it, where it is processed and how long it is retained. Hosting, model providers and access controls are part of the project design, including any requirements around customer drawings or confidential processes.

How do we choose the first use case?

Start with a recurring problem your team can describe and measure. In the workshop, we look at its cost, the available data and the people who will use the result. Then we agree one pilot, its success criteria and the review needed before expanding it.

Let’s find your next step.

Tell us about your factory. We’ll explore where AI could help your team.

Explore what’s possible

A free 15-minute chat about what your business could do next.

Put your factory’s knowledge to work.

Find the fix your team used last time.

Where AI can help.

Investigate recurring defects

Find past fixes

Carry work between shifts

Replan after a delay

Prepare the next quote

Spot a known defect

Manufacturing experience.

Questions.

Let’s find your next step.

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