By industry
AI & Automation for Manufacturing
Connect the plant floor to the systems that plan it
We design around
- DPDP Act 2023
- ISO 9001 documentation
- IATF 16949 (automotive)
- ISO 27001
- 1 shift
- Production reporting latency, from a day
- 75%
- Quality documentation time reduction
- 100%
- Maintenance history made analysable
What we hear
The problems that bring people to us
If several of these describe your week, there is almost certainly something worth automating.
- Plant floor data trapped in systems the ERP cannot see
- Quality documentation and non-conformance reports written by hand
- Maintenance history in a spreadsheet, so nothing can be analysed
- Supplier RFQs and quotations processed manually
- Production reporting compiled a shift behind
What we build
Where automation pays off in manufacturing
OT to IT integration
A modelled data layer that brings SCADA, MES and machine telemetry together with the ERP, so production, quality and cost can be reasoned about in one place instead of three.
Quality documentation automation
Non-conformance reports, inspection records and CAPA documentation drafted from structured inputs, for a quality engineer to review and sign. Keeps the audit trail complete without consuming engineer time.
Maintenance records that can be analysed
Structure historical maintenance data, then surface recurring failure patterns by asset and line. The prerequisite for any predictive maintenance work later.
Supplier and RFQ handling
Extract from supplier quotations, normalise against your part master, and compile comparisons automatically. Removes the spreadsheet stage from procurement.
Shift and production reporting
Generated from source systems at shift end rather than compiled the next morning, with variances flagged.
The gap between the floor and the office
Manufacturing usually has plenty of data and very little access to it. Machine telemetry sits in SCADA. Production actuals sit in MES. Cost and inventory sit in the ERP. Quality records sit in a shared drive. Each is fine in isolation, and no question that spans two of them can be answered without someone building a spreadsheet.
Closing that gap is a data engineering problem before it is an AI problem, and doing it in the wrong order is why so many plant AI pilots go nowhere.
Sequencing that works
Integrate first. Get production, quality, maintenance and cost into one modelled layer with consistent asset identity. Nothing downstream works without this.
Automate the documentation. Quality records, non-conformance reports and shift reports drafted from structured data. Immediate time back for engineers, and it improves the data quality further.
Analyse the history. Recurring failure patterns by asset, line and shift. This often surfaces the answer people were hoping a predictive model would give them, at a fraction of the cost.
Then assess prediction. With structured history and consistent asset naming in place, we can honestly evaluate whether predictive maintenance is trainable on your data. Often it partly is, for specific asset classes.
FAQ
Questions we get asked
Talk to someone who has worked in manufacturing
Bring a process that annoys you. In 30 minutes we will tell you whether AI helps, what it would cost, and where it would fail — even if the answer is don't bother.
Or email [email protected] · we reply within 1 business day