Assistant
Empowering Manufacturing thanks to Cognitive Search
Transforming Manufacturing Knowledge Access Across R&D and Customer Care
Manufacturers manage some of the most complex information environments in any industry. Product specifications, bills of materials, engineering change orders, FMEA documents, compliance records, technical manuals, and service case histories — spread across PLM, CRM, ERP, and document repositories that were built independently and were never designed to be searched together. For R&D engineers, this means spending hours navigating between systems to find information that should take seconds to locate. For Customer Care teams, it means slower ticket resolution because the right technical documentation, similar case history, or part substitution data is in a system no one thought to check.
Sinequa’s enterprise AI search platform addresses both problems through a single unified knowledge layer connecting every manufacturing data source into one intelligent search experience, with AI-powered relevance that understands manufacturing context and access controls that enforce the governance requirements that complex manufacturing environments demand.
This demo shows exactly what that looks like in practice for two of the functions where manufacturing knowledge access failures have the highest operational cost.
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What You’ll See in This Demo
For R&D Engineers
The demo shows how Sinequa enables R&D engineers to access the full product knowledge environment through natural language queries — without knowing which system holds the relevant information:
- Access product specifications, bills of materials, engineering change history, design files, and compliance documents in seconds from a single search interface
- Find internal experts and related knowledge across the organization instantly — surfacing colleagues who have worked on related designs, materials, or compliance challenges
- Eliminate the time currently spent navigating between PLM, CAD, document management, and compliance systems for information that should be immediately accessible
Leading manufacturers have documented the impact of this capability at scale: Siemens measured a 30% reduction in engineering research time following Sinequa deployment, and Alstom documented $46M in productivity value from AI-powered engineering knowledge access. Airbus deployed the platform across more than 700 engineers in aerospace design and manufacturing operations.
For Customer Care
The demo shows how Sinequa transforms Customer Care performance in manufacturing — where ticket resolution time directly affects customer satisfaction and service profitability:
- Surface the right troubleshooting steps, similar resolved cases, and technical documentation from a single interface — without requiring agents to search across multiple systems
- Identify part substitutions through BOM and drawing analysis — enabling faster response on availability and compatibility questions
- Reduce Time to Resolution and deflect tickets by giving agents immediate access to the full knowledge base relevant to each service interaction
Why the Architecture Matters, Not Just the Interface
The value of Sinequa’s manufacturing search is not the interface. It is the architecture underneath it: NLP-powered content analysis that understands manufacturing terminology across languages, AI-powered relevance ranking that surfaces the most current and most authoritative result for a given query, access control enforcement at the retrieval layer that ensures engineers and agents only see content they are authorized to access, and a connector architecture that connects to PLM, CRM, FMEA, ERP, and every other manufacturing system without requiring data migration.
This architecture is also the foundation for the manufacturing AI capabilities now in production at leading industrial organizations: AI agents that monitor the digital thread continuously, AI assistants that answer engineering questions from the full product knowledge environment, and advanced RAG systems that synthesize findings across structured and unstructured manufacturing data — all dependent on the search and retrieval quality this demo demonstrates.
Frequently Asked Question
Sinequa connects to the full range of systems that hold manufacturing knowledge — PLM platforms (Siemens Teamcenter, PTC Windchill, Dassault Systèmes ENOVIA), ERP systems (SAP, Oracle), CRM platforms (Salesforce, Microsoft Dynamics), document management and quality systems, CAD and design file repositories, FMEA databases, compliance and regulatory documentation systems, and a broad range of manufacturing-specific applications — through its library of more than 200 pre-built enterprise connectors. Integration is achieved without requiring data migration: Sinequa’s search layer indexes each system’s content with the access control metadata from that system, maintaining existing data governance while making the full manufacturing knowledge environment searchable from a single interface.
Manufacturing Customer Care teams face a retrieval challenge that directly affects resolution time: the information required to resolve a service query — the relevant troubleshooting procedure, a similar case from a previous service engagement, the part substitution option for an obsolete component, the technical specification for the product variant in question — is typically distributed across multiple systems that agents must search sequentially. Sinequa addresses this by unifying the full knowledge base available to Customer Care — service case history, technical manuals, parts databases, BOM and drawing repositories, compliance documentation — into a single search interface with AI-powered relevance that surfaces the most applicable content for each service query. The specific capabilities demonstrated in the demo include BOM and drawing analysis for part substitution identification and similar case retrieval that connects current service queries to previously resolved cases with matching symptoms, configurations, or components.
R&D engineers in manufacturing organizations face a knowledge access challenge that compounds across every design and engineering decision: the information relevant to any engineering question is distributed across PLM systems, document repositories, compliance databases, and the expertise of colleagues who have worked on related programs — none of it accessible through a single query. Sinequa’s enterprise AI search enables engineers to ask natural language questions that span the full product knowledge environment and receive responses that surface the relevant specifications, change histories, design files, compliance documents, and expert contacts simultaneously. The practical impact is a significant reduction in the time engineers spend on information retrieval — time that Siemens quantified as a 30% reduction in engineering research time following Sinequa deployment, and Alstom documented as contributing to $46M in measured productivity value across engineering and production knowledge workflows.
“Cognitive search” was a market category term used by Gartner in the late 2010s to describe enterprise search platforms that applied NLP and machine learning to improve search relevance beyond keyword matching. Sinequa was recognized in this category — as a Leader in the Forrester Wave for Cognitive Search and as a recognized vendor in Gartner’s Insight Engines category — before both Gartner and Forrester evolved their category frameworks to better reflect the integration of generative AI, RAG, and agentic capabilities. Sinequa’s current platform extends well beyond cognitive search: it incorporates advanced RAG for AI-generated synthesis from enterprise data, AI assistants that provide conversational knowledge access, and AI agents that monitor manufacturing data environments continuously and take governed actions on the knowledge they surface. The foundational capabilities this demo shows — unified search, NLP relevance, access-controlled retrieval — are the base layer on which these advanced AI capabilities are built.
Large manufacturing organizations produce and depend on technical documentation in multiple languages: engineering specifications authored in German, maintenance manuals published in French, quality standards documented in Japanese, and service histories recorded in English — all potentially relevant to a single engineering query in any of those languages. Sinequa’s NLP-powered content analysis processes technical content in any language at indexing time, applying semantic enrichment that enables cross-language search relevance: a query in English can surface the most relevant technical document regardless of the language in which it was authored. This capability is particularly critical for global manufacturers with multi-site engineering programs, cross-border service networks, and regulatory documentation requirements across multiple national jurisdictions — ensuring that language does not become a barrier to the institutional knowledge that multilingual manufacturing organizations have built.
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