Industry
AI automation for manufacturers that lose engineering and office hours to RFQs, order paperwork, supplier chasing and documentation.
Sound familiar?
Manufacturers tend to have well-run shop floors and badly connected offices. The machines are scheduled and measured, while quoting, order entry, purchasing and quality paperwork still depend on email, spreadsheets and a few people who know where everything is.
That office work is where AI automation for manufacturing usually pays back first. Language models are good at reading RFQ packages, POs, supplier acknowledgements and specifications, pulling out the fields that matter and putting them into your ERP or quoting sheet. We build these as document processing pipelines with a review step, so an estimator or planner checks the extracted data instead of typing it.
The second common win is knowledge. Specs, work instructions, past quotes and customer requirements are usually spread across SharePoint, network drives and the ERP. A knowledge base assistant can answer "what tolerance did we quote for this customer last year?" or "which procedure covers this coating?" and link straight to the source document, respecting existing folder permissions.
We are careful about what AI should not do here. It should not decide whether a part is in tolerance or approve a deviation. Those stay with qualified people. What it can do is draft the NCR, collect the data and route it.
If you sell products direct or through dealers on Shopify, our apps can help too: WarrantyHub handles warranty registration, serial tracking and claims, and PDF Catalogue generates dealer catalogs and line sheets from your product data.
The problem
Ask a plant manager or operations director where time disappears and the answer is rarely the machines. It is the paperwork and coordination around them.
RFQs arrive as email threads with drawings, specification PDFs and terms attached. Before anyone can price the job, an engineer or estimator reads the whole package to pick out material, quantities, tolerances, finishes, certifications and delivery dates. Many of those quotes are never won, so a lot of skilled time goes into work that produces nothing.
Customer POs, schedule releases and change orders come in by email and get keyed into the ERP. A changed revision letter or delivery date buried in an email body is easy to miss, and that turns into the wrong part shipped.
Buyers spend hours each week emailing suppliers for order acknowledgements, ship dates and certs, then updating the ERP so planning has accurate dates.
NCRs, CAPAs, inspection records and customer complaint responses all need writing up in a consistent format. The investigation is the real work; the write-up often takes longer.
Current revisions, work instructions and customer-specific requirements live in several places. New staff ask experienced staff, and experienced staff stop doing their own work to answer. When a long-serving engineer or planner retires, a lot of that knowledge leaves with them.
Late orders, supplier performance, scrap and open quotes are usually compiled by hand from ERP exports and spreadsheets. The report is useful, but building it every week is not a good use of a planner's morning.
All of this can be partly automated without touching production systems or machine controls. We stay on the business side of the network and leave quality decisions with your quality team.
Most manufacturers do not need a plant-wide AI program. They need one workflow, usually RFQ intake or PO entry, running reliably against their own documents and ERP. Once that works and people trust it, the second and third workflows are faster to build because the integrations and review screens already exist.
Where we start
Incoming RFQs are read and summarized into a standard intake sheet: part numbers, materials, quantities, tolerances called out, finishes, certifications and due dates. Estimators decide faster whether to quote and start from structured data.
Customer POs and change notices are extracted into draft ERP sales orders, with revision, quantity and date differences against the existing order highlighted for a planner to approve.
Open purchase orders without acknowledgement or with slipping dates trigger supplier emails automatically. Replies are read and the confirmed dates are proposed back into the ERP.
Inspectors enter the facts in a short form or voice note; the system drafts the nonconformance report in your template and routes it for review and sign-off by quality.
Staff ask questions in Teams or a web app and get answers from specs, procedures and past quotes, with links to the exact document and revision it came from.
Open orders, late purchase orders, quotes awaiting a decision and open quality issues pulled from the ERP and trackers into one short report for the leadership meeting, so nobody spends Monday morning building it.
Integrations
NetSuite, Epicor, SAP Business One, Microsoft Dynamics 365 Business Central, Odoo, Katana and MRPeasy for orders, purchasing and inventory.
SharePoint, OneDrive, Google Drive and network file shares, indexed with existing permissions so people only see what they could already open.
QMS tools or the spreadsheets and forms you use today for NCRs, CAPAs and inspection records.
HubSpot, Salesforce and quoting spreadsheets, so RFQ intake and quote status are visible to sales.
Microsoft Teams, Outlook and Slack for approvals, alerts and the assistant interface staff actually use.
Shopify stores with WarrantyHub for registrations and claims, and PDF Catalogue for dealer price lists and catalogs.
FAQ
It can read title blocks, notes, tables and text callouts reasonably well, and summarize what a drawing specifies. It is not reliable for interpreting geometry or GD&T on its own, so we use it to speed up the review, not replace the engineer who signs off.
Normally no. We work with the ERP, email and document systems on the business network. If you want shop-floor data in reports, we read it from systems that already collect it rather than connecting to machine controls.
We use business-tier model APIs that do not train on your data, keep documents in your own storage where possible and respect existing access permissions. If a customer contract or export-control rule restricts where data can be processed, tell us early and we will design around it, including private or region-specific hosting.
It can make records more consistent and easier to find, which auditors appreciate. It does not make you compliant on its own, and quality decisions and approvals stay with your qualified staff.
Often yes, through its database, scheduled imports or an export the ERP already produces. We assess the options during scoping and tell you plainly if an upgrade would save more than a workaround.
Tell us where your estimators, planners or quality team lose the most time. We'll propose a first workflow, the systems involved and a realistic timeline. If a sample of real RFQs or POs is easy to share, we can show what extraction would look like before you commit to anything.
Each solution is built from one or more of these. Follow a link for the detail on scope, stack and process.
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