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.
Asset identity is the unglamorous blocker
The problem that stops more plant data projects than any other is that the same
machine is called three different things in three systems. Press 4 in the MES,
PRS-004 in the maintenance spreadsheet, and Line2_Press_B in the historian.
Until that is resolved, no question spanning two systems has a reliable answer, and every analysis silently undercounts. Reconciling asset identity is tedious, needs someone from the plant who knows the equipment, and is genuinely the first deliverable on most engagements. Any proposal that skips it is planning to discover it later at higher cost.
Before integration
- Three names for one machine across three systems
- Downtime reasons recorded as free text
- Shift reports compiled the next morning from memory
- Maintenance history in a spreadsheet per line
- Nobody can total the cost of one asset's failures
After
- One asset register, referenced by every system
- Downtime coded against a controlled list
- Reports generated at shift end from source data
- Maintenance history structured and queryable
- Cost per asset, per failure mode, answerable
Where the work overlaps other sectors
The integration half is data platform buildout applied to plant systems. The documentation half is document processing — quality records and supplier quotations are documents, and behave like documents. The procurement and reporting layer is back-office automation.
Recognising that matters commercially, because it means the scaffolding is not bespoke to manufacturing and the second and third automations cost less than the first.
What we will talk you out of
Computer vision for quality inspection, as a first project. It is the request we receive most and the one with the widest gap between demo and production. It needs labelled defect images at volume including the rare defects, controlled lighting, fixed camera geometry, and a retraining path for when the product changes.
Where those conditions genuinely hold it works well. Where they do not — which is most plants asking for it — the money is better spent on the reporting and documentation work that pays back inside a quarter, and the vision question revisited once the data foundation exists.
FAQ
Questions we get asked
Can you do predictive maintenance?
Eventually, and rarely first. Predictive maintenance needs labelled failure history at volume, and most plants have maintenance records in spreadsheets with inconsistent asset naming. The honest sequence is to structure the historical data, get pattern analysis working, and only then assess whether prediction is trainable. Anyone selling predictive maintenance before seeing your data is guessing.
Our machines are old and not networked. Is anything possible?
Frequently more than expected. Retrofit sensors, PLC data extraction, and structuring what operators already record manually all work. We scope what is achievable against your actual equipment rather than assuming a modern estate.
Where does the plant data live?
Wherever your constraints require. Latency-sensitive and safety-related processing stays on-premise or at the edge; analytics and reporting can go to cloud. We design the boundary explicitly rather than defaulting to one answer.
Will this interfere with production systems?
No. We read from OT systems, we do not write to them, and integration is designed as read-only with isolation from control networks. Automation that could affect machine operation is out of scope — that is a safety-instrumented domain with its own engineering discipline.
What does a first engagement cost and how long does it take?
Two weeks to audit what data your plant actually emits and what it would take to reach it, then a four-week sprint to land one thing in production. Quality documentation or shift reporting are the usual first choices because both produce visible time savings inside a month. The integration layer takes longer and is worth sequencing second, once the first automation has proved which data sources are trustworthy.
Our MES vendor charges for data access. Is that a blocker?
Frequently it is the whole negotiation. Some vendors price database or API access as a premium module, and the licence cost changes the business case materially. We scope both paths — the licensed interface and the alternatives such as historian reads or file exports — and give you the numbers so the commercial conversation is informed. Occasionally the honest recommendation is that the vendor fee is cheaper than engineering around it.
Will operators see this as surveillance?
They will if you deploy it without saying anything, and that reaction will be reasonable. Machine and line telemetry is about equipment, but shift-level reporting is about people, and the distinction matters to the people on the shift. What works is being explicit about what is measured, what it is used for, and what it is not used for — and involving the line supervisors in defining the reports. Automation that the floor quietly works around delivers nothing.
Do we need cloud for any of this?
Not necessarily. Latency-sensitive and safety-adjacent processing stays on-premise regardless. For analytics and reporting, cloud is usually cheaper and easier to operate, but a single well-specified on-premise server handles a surprising amount if your connectivity is poor or your policy forbids it. We design the boundary around your constraints rather than defaulting to one answer.
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