RFQ and quotation support
Reading incoming enquiries and drawings, matching against your historical jobs and cost data, and assembling a draft quotation for an estimator to price and approve.
Industries
Manufacturing AI that starts in the office, not the cell
Most manufacturers expect AI to arrive on the shop floor. In practice the fastest, cheapest returns are in the office around it: quotations, technical documentation, quality records and the planning conversations that run on stale spreadsheets.
The pressure right now
What we hear from manufacturing
- RFQ and quotation turnaround losing work to faster competitors
- Technical documentation and work instructions perpetually out of date
- Quality and non-conformance records that cannot be analysed for patterns
- Maintenance history locked in engineers' heads and paper logs
- Planning decisions made from a spreadsheet that was accurate on Monday
Where it pays off
Six places AI earns its keep here
Not everything on this list will apply to you. Most organisations start with one and extend once it has been measured.
Technical documentation
Work instructions, method statements and operator guides drafted and kept aligned with engineering changes, with version control that reflects what is actually on the line.
Quality and non-conformance
Consistent classification of NCRs and defect reports, with the pattern analysis that a manual system can never produce - feeding real root-cause work.
Maintenance knowledge
Turning years of maintenance logs and engineer notes into something searchable, so the fix found in 2019 is not rediscovered from scratch.
Supply chain correspondence
Supplier chasing, order confirmation checking and discrepancy identification across the email traffic that surrounds every purchase order.
Planning support
Assembling the current picture across ERP, quality and maintenance data so the planning meeting starts from facts rather than reconciliation.
Where we would start
The first three moves
1
Quote faster
Quotation turnaround is usually the clearest commercial lever and involves no production risk.
2
Make the quality data usable
Consistent classification of existing records unlocks analysis you cannot currently do at all.
3
Leave the line alone at first
Vision inspection and process control are real, but they are a different discipline with safety implications. Earn the credibility in the office first.
Risk and regulation
The part most suppliers skip
Where the risk sits
- Safety-critical processes and machinery control - out of scope for general AI systems
- Quality management obligations under ISO 9001 and customer-specific standards
- Product traceability and record integrity for regulated or aerospace/automotive supply chains
- Intellectual property in drawings, tooling and process knowledge
- Export control where technical data crosses borders through a cloud service
How we handle it
We do not build machinery control or safety-instrumented functions. Those live in properly assessed control systems with their own standards and competence requirements.
Drawings and process knowledge are among the most valuable assets you hold; where they cannot go to a third-party service, we deploy inside your own network - including fully offline where the estate requires it.
Quality records produced with AI assistance keep a full audit trail, so the ISO evidence position is stronger after the change rather than murkier.
Where this fits
Where to go next
Questions
Questions from manufacturing
It is a mature field with specialist vendors, and for most defect types you should buy rather than build. We help with the selection and the integration into your quality records, which is where those projects usually underdeliver.
Usually not. Even read-only extract access is enough for most of these workflows, and document-based processes - quotes, documentation, quality records - often barely touch the ERP.
Yes. Fully on-premises deployment is well established for manufacturers, and it is often the only acceptable option where drawings are export-controlled or contractually restricted.
Start with an audit of what you already run
Two to four weeks to an evidenced picture of your AI use, spend and risk - and a ranked list of what to do first.