Yield drift
Process
Scrap moving against an unchanged recipe, caught as a signal rather than as a month-end variance.
- Scrap rate
- Time to detection
- First-pass yield
Industries
Manufacturing
Engineering knowledge. Quality documentation. Production intelligence. Supplier collaboration — answers grounded in your own drawings, lots and process history.
The problem
The plant already knows. It just cannot say it in time.
A manufacturer is unusually well instrumented and unusually badly served by it. The historian has the vibration signature that preceded the last two bearing failures. The quality system has the lot that drifted. The PLM has the change order that obsoletes stock already committed. The warranty data has the build week that is about to become a campaign.
All of it is recorded. Almost none of it is connected, and the connection is the entire value. A scrap rate that moves three points against an unchanged recipe is not a mystery — it is a question whose answer is spread across four systems and two file shares, and the shift that could have acted on it ended six hours ago.
The second problem is language. A significant part of what a manufacturer knows is written down in drawings, specifications, deviation reports and supplier correspondence — documents, not rows — and that is precisely where retrieval grounded in your own corpus earns its keep, provided every answer arrives with the passage it came from.
Use cases
Where models earn their place
Time-series and anomaly models on the process side, grounded retrieval on the document side, and most of the useful ones combining the two.
Failure forecasting
Maintenance
A vibration signature that matches the pattern preceding prior failures, with hours of warning rather than none.
- Unplanned downtime
- MTBF
- Maintenance cost
Supplier quality drift
Inbound
Defect rate climbing across consecutive lots from one tier-two supplier, before it reaches the line.
- PPM defect rate
- Line stoppages
- Supplier scorecard
Engineering change in flight
PLM
A change order that obsoletes stock already committed to live orders, surfaced against the commitments rather than in isolation.
- Obsolescence write-off
- Change cycle time
- Order impact
Warranty clusters
Field
Field failures tracing back to a single station's drift in one build week, found before it becomes a campaign.
- Warranty cost
- Campaign scope
- Time to root cause
Engineering knowledge
Documents
Specifications, deviations and supplier correspondence made answerable — with citations, not confident guesses.
- Search time
- Reuse of prior art
- Onboarding time
Connected to
The systems the plant already runs on
Historians, execution systems and document stores, read where they are. For plants under residency or air-gap constraints, the whole thing runs inside the perimeter.
ERP — production & materials
Manufacturing execution
SCADA & historian
Quality management
Maintenance management
PLM & engineering
Warranty & field service
Supplier EDI
Governance
Traceability is the requirement, not the paperwork
Manufacturing is regulated less by prudential rules than by traceability obligations — and a model that cannot show its working is useless in a root-cause investigation regardless of what any regulator says.
So the same controls apply as everywhere else on the platform: full data-to-decision lineage, versioned model configuration, change history, and cited source passages on anything retrieval-based. When a warranty campaign is being scoped, the question which lots, and on what evidence has an answer with the records attached.
On the model estate itself, EU AI Act, NIST AI RMF and ISO 42001 apply as they do to every deployment, and the OWASP LLM Top 10 governs the retrieval and agentic components. Where the plant is subject to residency or air-gap constraints, the platform runs entirely on your own hardware with nothing leaving the network.
Worked designs
How we would build it
Reference deployments: a problem shape, the design we would propose for it, and what we would expect to be measured afterwards.
Talk to us
Start with one decision
Bring us a line, a quality problem or a document store nobody can navigate. The first assessment is about your data, not our platform.