Enterprise AI for Manufacturing
Most enterprise AI tools search your collaboration layer well. Sinequa indexes the systems of record where manufacturing knowledge actually lives: PLM, CAD drawings, QMS, ERP, and EAM systems.
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Backed by independent recognition from Gartner, Forrester, and SPARK Matrix™.
$0M
Productivity gains
$0M
Warranty avoidance
$0M
Development savings
Measured over three years.
With Sinequa, our engineers can ask a complex, multi-part question and get a concise answer plus the underlying evidence. That’s a game-changer for design, warranty, and failure analysis.
Oliver Scott Beard Principal Technical Architect – AI Systems, Cummins
In manufacturing, the answers your people need don’t sit in chat threads and shared docs. They live in the current revision of a part. The engineering change order. The quality record. The supplier spec. The 30-year-old drawing that’s still in production.
That knowledge lives in PLM, CAD and engineering drawings, QMS, ERP and EAM systems of record, most AI search tools were never built to reach. And your AI tools are only as useful as the systems they can actually get inside.
Sinequa reaches the systems of record where your engineering and manufacturing knowledge actually lives, not just the collaboration and office tools your teams talk in.
Demos and connector lists rarely surface the differences that decide a manufacturing deployment.
PLM, CAD and engineering drawings, QMS, ERP, EAM systems are core infrastructure in manufacturing and most platforms can’t effective pull all the information that lives in them.
A connector on a list isn’t the same as indexing a system reliably across all its data. The question isn’t “is it supported”, it’s “can an engineer trust the answer it returns, trace it back to the source, and act on it with confidence.”
Cloud, hybrid, on-premise, even air-gapped, and how long the vendor has actually run that way in production. For IP-sensitive and export-controlled data, deployment control isn’t a preference.
Global teams design in one language and build in another. Real multilingual means a query in English surfaces the relevant German or Japanese document, not just a translated interface.
Part numbers, engineering terminology, normative and regulatory language. General-purpose relevance and relevance you can engineer for your domain are not the same thing.
Document-level access controls enforced at query time, across billions of documents and complex permission models, so an answer never exposes what a user isn’t cleared to see.
These requirements are drawn from the patterns we see across the industry’s most complex deployments. Read the State of Enterprise Agentic AI 2026 →
See how Sinequa indexes the systems of record where manufacturing knowledge actually lives — PLM, CAD and engineering drawings, QMS, ERP, EAM systems — and returns answers an engineer can trust. Ready to see it in your environment?
Book a custom demoIn manufacturing, a wrong answer is not a minor inconvenience. It is a delayed change order, a recalled part, or a compliance failure.
That knowledge lives in PLM, CAD, QMS, and ERP systems, the systems general AI tools can reach but were never built to understand.
Sinequa has integrated with systems like PTC Windchill, Siemens Teamcenter, SAP, Oracle, and IBM for years. That kind of depth shows up in three ways:
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Beyond the hype, what does agentic AI actually look like inside a $5B+ revenue organization? This research explores the gap between “Agent-Washing” and the realities of deploying AI agents in complex, regulated, and legacy-heavy environments — exactly the ones Sinequa is built for.
