Connecting the Digital Thread
Engineers and Product Designers. Augmented.
Empower teams to give teams a fast, accurate, and unified view of projects, products, and parts.
Connect the digital thread with the best AI-powered search for manufacturers Watch the demo
Connecting the Digital Thread
Empower teams to give teams a fast, accurate, and unified view of projects, products, and parts.
Manufacturing organizations face growing pressure to develop new products quickly, meet customer requirements while maximizing quality, minimize costs, reduce regulatory risk, and competitive. Engineers and product designers must make many choices based on information, including customer requirements, cost targets, available parts, manufacturing processes, service and warranty data, and more. Some of this mission-critical information will be available in the company’s PLM systems; however, much more is locked away in disparate, siloed enterprise systems like ERP, CRM, CAD, and CMS, creating significant challenges to find, access, integrate, and analyze relevant knowledge.
Today, everything is about digital transformation, but many companies appear to have forgotten that data lies at the heart of all digital endeavors. Companies risk failing in their digital initiatives without the proper understanding, appreciation, and management of data. Ultimately, they must ensure that the relevant data is always valid and available. This also means that users can find the appropriate data anywhere within an organization’s digital thread, or better put, digital web of data-generating processes and systems. Not just searched for but found, including the ability to gain actionable insight from the found data.
About 18 applications are opened daily by employees, leading to 8 hours spent weekly searching for information. Company mergers and acquisitions, partnerships, and joint ventures can exacerbate this.
In many cases, it’s easier to build a new part than to find the same one that already exists. With engineers spending up to 80% of their time designing parts from scratch that cost an average of $5,000 per year, wasted time and money is proliferating.
Failure to access required information can raise the risk of the product failing to meet market needs, lead to increased and unforeseen costs, cause the product to be late to market, be of unacceptable quality, and increase regulatory risk.
Failure to access required information can raise the risk of the product failing to meet market needs, lead to increased and unforeseen costs, cause the product to be late to market, be of unacceptable quality, and increase regulatory risk.
Sinequa breaks silos, enabling search and discovery across complex engineering and design environments
Built for large-scale industrial data and content, Sinequa’s platform now combines Semantic Search and Vector Search with industry leading Large Language Models (LLM) powered by an Enterprise GenAI assistant. Engineering teams gain fast, accurate, unified search and insight generation across projects, products and parts within an organizations’ design, supply chain, manufacturing, and services processes. Powerful question-answering capabilities and automated summaries help streamline workflows, collaboration, and improve decision-making throughout the product lifecycle.
Enables engineering teams to optimize production with an integrated feedback loop, leading to decreased re-work, improved in-field performance, and quicker turnaround times for critical investigations.
Avoid rework and reinvention by unlocking unstructured data to leverage lessons of the past.
Get a competitive edge by uncovering trends in internal and external knowledge.
Make better decisions faster with research assistance and exclusive content access.
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Improve Time to Market
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Increase Margins
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Increase reuse of parts
From design to execution, Sinequa connects the Digital Thread to give teams a fast, accurate, and unified view into projects, products, and parts.
Sinequa’s Intelligent Search platform’s in-depth analysis provides Alstom’s employees with a thorough understanding of unstructured data, including the text coming from very complex technical and normative documents. This allows greater efficiency and real time savings for Alstom’s data scientists.
Tristan Le Masne, Vice President Internal Audit & Internal Control, Alstom
