AI-Powered Search for Manufacturers

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 AI-powered search and AI assistants automatically weave together your digital thread — giving every engineer, technician, and knowledge worker a single, intelligent access point to all of it, with AI-generated answers grounded in your actual enterprise data.

Alstom used Sinequa to eliminate redundant parts manufacturing across a 3-million-part catalog, saving $40M — plus an additional $6M through automated proposal generation. Siemens achieved 30% faster information discovery for technical teams. Airbus deployed Sinequa for over 700 engineers worldwide, providing a single secure point of access to all structured and unstructured engineering data. Volkswagen uses Sinequa to connect knowledge workers across its global manufacturing and R&D operations.

Two Core Solutions for Manufacturing Teams

AI-Powered Search for Engineering and Design

Engineers spend a disproportionate share of their day not designing — but searching. Finding whether a part already exists, locating the last test report for a component, verifying a specification against a prior design. Sinequa gives engineering teams easy access to all content across PLM, CAD, MBSE, case management systems, and content repositories — with the ability to verify AI-generated answers against authoritative source documents instantly. The result: fewer errors, less rework, accelerated product development cycles, and reduced training costs when engineers can find existing knowledge rather than rebuilding it.

AI-Powered Search 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 gives maintenance and support teams pinpoint access to all of this from a single interface, across manuals, repair guides, historical maintenance records, and technical documentation. 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 Assistant lets engineers and technicians ask natural language questions and receive concise, AI-generated answers with direct links to the source documents for verification. Powered by Azure OpenAI GPT-4 Turbo and grounded in your enterprise data — not generic model outputs.
• Optimize — 360° views of parts, products, assets, and projects surface all related content in one place. Search relevance improves over time based on actual user behavior and domain feedback.

From AI-Powered Search to Agentic AI in Manufacturing

Sinequa’s search and RAG 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 deployable AI from a prototype.