Creating a Knowledge Engine for Complex Semiconductor Products and Processes

Transforming Semiconductor Knowledge Into Actionable Expertise with AI

Industry

Semiconductors

Services

Enterprise AI Integration

Knowledge Engineering

Virtual Expert

AI Support

Approach

Ingested thousands of complex semiconductor data sheets and fragmented systems, transforming them into a structured knowledge graph that powered accurate, source-linked answers for engineers and partners.

How a mature knowledge graph transforms highly complex products and processes into customer loyalty and revenue.

The semiconductor industry is both the engine and the bottleneck of modern technology. From automobiles and industrial systems to consumer electronics, global innovation depends on the precision and reliability of semiconductor components.

But behind every chip lies a mountain of complexity: tens of thousands of part numbers, sprawling data sheets, intricate pin configurations, and ever-changing compliance requirements. For global leaders in microcontrollers, analog, power, and SoC products, this complexity multiplies with each acquisition, product line, and customer request.

Field application engineers (FAEs) and digitalization teams are expected to be encyclopedias of this information, helping customers design winning combinations of components under tight deadlines and even tighter tolerances.

In reality, knowledge lives across fragmented CRMs, ERPs, wikis, and 500-page PDFs. The cost of inefficiency is high: unanswered customer requests, costly re-spins, and lost design wins in an industry where being “designed in” early is everything.

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THE CHALLENGE

A leading global semiconductor manufacturer faced a familiar but costly problem: complexity buried in documentation.

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With over 40,000+ components spread across multiple product lines and acquired business units, the company’s field application engineers (FAEs) and digitalization team were drowning in:

Fragmented systems – data spread across multiple CRMs, ERPs, and wikis.
Massive, inconsistent product data sheets – ranging from a few pages to 500+, filled with pin diagrams, parametric tables, and compliance footnotes.
Limited capacity to serve customers – FAEs could only respond to ~20% of inbound requests, focusing narrowly on top-tier accounts, leaving the “mass market” underserved.
High stakes for accuracy – even a 1% error in interpreting technical specifications could mean failed designs, costly re-spins, or liability risks.

The result: long design cycles, strained engineering teams, and missed revenue opportunities from smaller but high-potential customers.

THE SOLUTION

The company partnered with Implicit to explore how its KnowledgeOS platform could bring order to the chaos.

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Implicit’s approach:

Connect – Ingested thousands of semiconductor data sheets and design assets, unifying information across formats and systems.
Understand – Applied AI to auto-generate a taxonomy-driven knowledge graph of components, pins, subcomponents, error codes, and compliance requirements. This included entity extraction from text, diagrams, and complex parametric tables.
Enable – Delivered a question-answering agent that FAEs, support teams, and partners could use to instantly surface precise, source-backed answers, complete with diagrams and references to original documentation.

Key capabilities included:

Accuracy guardrails: Human-in-the-loop refinement of taxonomies to meet “zero tolerance” thresholds for hardware specs.
Multimodal understanding: Extraction not just from text, but from tables, schematics, and pin diagrams. This is critical for electronics design.
Flexible deployment: Options to run as SaaS in a secure cloud or within the company’s private environment to meet strict IP and compliance needs.
Extensibility: APIs that allow integration into customer-facing portals (e.g., design support, “winning combinations” configurators) or internal CRM/workflow tools.

RESULTS

By transforming unstructured PDFs into a searchable, structured knowledge layer, the company positioned itself to:

Expand FAE reach: Reduce time spent parsing documents, allowing engineers to answer more customer requests, including the underserved “long tail” of smaller design wins.
Increase accuracy & confidence: Source-linked answers ensure engineers can validate every recommendation against the original data sheet, reducing re-spin risk.
Accelerate design cycles: Customers and FAEs can discover compatible microcontrollers, peripherals, and configurations in minutes instead of days.
Enable revenue growth: With better coverage of mass-market accounts, the company can capture more design wins earlier in the product lifecycle.
Lay the foundation for advanced optimization: Once structured, normalized data is in place, future use cases include automated reference design generation, supply chain-aware part selection, and compliance-based design filtering.

The Bottom Line

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For a semiconductor company where documentation complexity was throttling customer support and design wins, Implicit’s KnowledgeOS unlocked a new path: turning static PDFs into actionable, AI-powered product expertise.

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The result isn’t just faster answers - it’s a scalable way to win more sockets, improve accuracy, and serve a broader swath of customers without adding headcount.

Testimonials

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"I'm impressed by Implicit, a company that uses Al to help find, target, and summarize information for the Dept. of Defense and do the same for our enterprise data sources (structured and unstructured)."

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