Industry
AI consulting for manufacturing
The constraint in most plants is not model capability, it is the data path from the line to a system anyone reviews. Where that path exists, review and planning workflows move quickly. Where it does not, an AI programme is a data engineering programme wearing a different name, and it should be funded as one.
- Start with
- Prototype Sprint
Workflows that carry evidence quickly
- Visual and document-based quality review supportEscapes per thousand units and reviewer time per lot
- Maintenance work-order triage and history summarisationTime from report to correctly assigned technician
- Supplier document and specification comparisonHours per supplier change request
- Shift handover summarisation from log dataHandover time and issues missed per week
What we advise against starting with
Closed-loop control changes
Safety cases and certification make the review cost exceed the saving.
Demand forecasting where history is short or regime-changed
The model learns a pattern the business has already left.
Full scheduling automation
Constraints live in people's heads and are not written anywhere the system can read.
Systems
What we integrate with in this sector
- MES
- ERP
- CMMS and maintenance systems
- Quality management systems
- Historian and time-series stores
Constraints
Design inputs, not afterthoughts
- Safety certification and change-control processes on line-adjacent systems
- OT and IT network separation, which constrains where inference can run
- Product traceability and retention obligations
- Union and works-council consultation where work content changes
Use cases
Anonymised workflows from this sector
Finance
Reducing month-end reconciliation review time in a mid-market manufacturer
Review time per close cycle fell from 38 hours to 11 hours
9 weeks
Procurement and engineering
Shortening supplier change-request review in an industrial manufacturer
Review hours per change request fell from 4.2 to 1.6, with queue age falling from 18 days to 5
8 weeks
FAQ
Questions about AI in manufacturing
- Do we need a data lake first?
- No. You need one usable path for one workflow. A lake built before a use case is a cost centre with no pass line.
- Can inference run on-premise?
- Yes, where residency or latency requires it. We price the operational burden honestly before you choose.
Other sectors
Where else we work
Next step
Start where the evidence arrives fastest in manufacturing
Bring one workflow and the constraint you think blocks it. We will tell you on the first call whether it is a good first case.