Manufacturers · AI Dev Team
Stop Rescheduling the Line by Hand Every Time a PLC Throws a Fault
Your line goes down, and the scheduling board finds out the same way everyone else does: a phone call from the floor. A Build Pod embeds with your production and quality teams to build what off-the-shelf MES modules skip — live PLC status feeds that trigger automatic rescheduling, defect-inspection vision models trained on your own reject images and wired into the existing MES reject workflow, and RFQ intake that parses supplier PDFs and emails straight into ERP line items matched to real SKUs. You keep Ignition, FactoryTalk, or whatever runs your floor today; we build around it.
What we build for Manufacturers
Reschedule the production line automatically when a PLC signals downtime
We pull live machine status off your PLCs and feed it into a scheduling layer that reshuffles jobs the moment a line stops, instead of waiting for a planner to notice and rework the board by hand.
Catch defects on the line with vision models trained on your own rejects
We train inspection models on your actual defect images — not a generic dataset — and wire the output straight into your existing MES reject workflow so a flagged part gets routed the same way a human inspector would route it.
Turn inbound RFQ PDFs and emails into matched ERP line items
Supplier quotes stop landing in an inbox for someone to retype. We parse PDFs and emails, match line items to existing SKUs, and push the result into your ERP so quoting starts from real data, not a manual re-entry pass.
How a Build Pod fits
Most manufacturers already run an MES, an ERP, and a floor full of PLCs that don't talk to each other the way the vendor demos suggested. A Build Pod doesn't ask you to rip any of that out — we spend the first sprint mapping how status, quality, and quote data actually move through your plant, then build the glue: a scheduling trigger off machine status, a vision model tied to your reject bins, an RFQ parser that respects your SKU catalog.
Because it's a subscription, not a project contract, the pod keeps working after the first build ships — tuning the defect model as your product mix shifts, extending the RFQ parser to a new supplier format, adding a second line to the scheduling feed. You get engineers who know your PLC tags and your MES reject codes on retainer, not a handoff document nobody reopens.
Frequently asked questions
- Do you need direct access to our PLCs, or can you work off historian data?
- Either works. We prefer a live tag feed off the PLC or its OPC-UA server for real-time rescheduling, but if historian data is what's available, we start there and move to live tags once access is approved.
- How much labeled defect data do we need before the vision model is usable?
- We can get a workable first model running on a few hundred labeled reject images per defect class, then improve accuracy as more images flow through the line — you don't need a finished dataset before we start.
- Will the RFQ parser handle non-standard supplier formats, like scanned faxes?
- Yes — we build the parser around the formats your actual suppliers send, including scanned or low-quality PDFs, and flag anything it can't confidently match to a SKU for manual review instead of guessing.
Ready to ship AI for manufacturers?
A Build Pod gets working AI into your stack in 2–3 weeks. Month-to-month, cancel any time.
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