Enterprise Agentic AI for Manufacturing

Connect Your Digital Thread

According to CIMdata, engineers waste 15-30% of their time simply searching for information. Product details, parts specifications, test procedures, quality reports, design files, and maintenance records are scattered across PLM systems, ERP, MES, CAD repositories, file shares, and collaborative tools — each containing a fragment of the knowledge engineers need to do their jobs. None of it is connected. All of it costs time, quality, and money.

Sinequa’s Enterprise Agentic AI platform automatically weaves together your digital thread — deploying AI agents and AI assistants that give every engineer, technician, and knowledge worker intelligent, autonomous access to all of it, with answers and actions grounded in your actual enterprise data.

Two Core Solutions for Manufacturing Teams

Enterprise Agentic AI for Engineering and Design

Engineers spend a disproportionate share of their day not designing — but searching, cross-referencing, and manually piecing together knowledge. Finding whether a part already exists. Locating the last test report for a component. Verifying a specification against a prior design. Sinequa’s AI agents and AI assistants give engineering teams autonomous access to all content across PLM, CAD, MBSE, case management systems, and content repositories — surfacing answers, flagging redundancies, and cross-referencing documentation automatically, with every AI-generated output traceable to authoritative source documents. The result: fewer errors, less rework, accelerated product development cycles, and reduced training costs when engineers can find — and act on — existing knowledge rather than rebuilding it.

Enterprise Agentic AI for Maintenance and Support

Technicians resolving a complex maintenance issue need the right information fast — the troubleshooting procedure, the bill of materials, the record of a past repair on this specific unit, the service bulletin issued six months ago. Sinequa’s AI agents and AI assistants give maintenance and support teams autonomous, pinpoint access to all of this from a single interface, across manuals, repair guides, historical maintenance records, and technical documentation — and can trigger next-best actions without manual handoff. The impact: faster issue resolution, reduced unplanned downtime, higher first-time fix rates, and improved customer satisfaction for organizations that provide field service.

How Sinequa Works

• Connect — Unify content from any industrial data source through 200+ ready-to-use connectors: PLM (Siemens Teamcenter, PTC Windchill), ERP (SAP), MES, MRO, CAD systems, CRM, SharePoint, OneDrive, Teams, Box, Confluence, and file systems. Any format, any source.
• Organize — Automatically classify and enrich content using NLP and machine learning — with domain knowledge tuning for manufacturing vocabulary, part taxonomies, and organization-specific terminology.
• Converse — Sinequa’s AI Assistants let engineers and technicians ask natural language questions and receive concise, grounded answers with direct links to source documents for verification. Sinequa’s AI Agents go further — autonomously executing multi-step workflows, monitoring for anomalies, and surfacing insights before they’re requested.
• Optimize — 360° views of parts, products, assets, and projects surface all related content in one place. Agent performance and search relevance improve over time based on actual user behavior, domain feedback, and monitored outcomes.

Why Agentic AI in Manufacturing Requires a Trusted Data Foundation

Sinequa’s advanced RAG and enterprise search foundation is what makes agentic AI in manufacturing trustworthy. AI agents that monitor parts catalogs for redundancy, surface maintenance alerts before failures occur, or cross-reference a new regulatory requirement against existing technical documentation are only reliable when the underlying retrieval is accurate and grounded in authorized enterprise data. For manufacturers building their Industry 4.0 AI strategy, that foundation is not a feature. It is what separates production-ready agentic AI from a prototype.