Breaking down knowledge silos in the semiconductor industry
Where accurate, complex multimodal data creates engineering efficiency for semiconductors.
4-minute read time
Semiconductor engineering runs on knowledge and most of it is hard to reach. I sat down with several semiconductor engineers to learn more. The data engineers need is multimodal and often on-premise: images, tables, waveforms, characterization plots, OCR, schematics, and text, scattered across Jira, Confluence, and SharePoint. The information exists. Finding the right answer at the right moment is the hard part.
Vectara’s agentic platform makes that knowledge answerable in natural language, grounded, multimodal, and deployable on-prem, in your VPC, or as SaaS, operated entirely by API. It is already in production at three of the largest global semiconductor companies. The value tends to concentrate in three engineering areas, each shown below.
Three engineering value areas, one platform underneath.
Chip Design
The teams: R&D and silicon design, RTL, IP-block development, place-and-route, simulation, the full cycle through tape-out.
The challenge: Chip design is one of the most expensive and complex engineering activities at a semiconductor company. Design costs alone can reach hundreds of millions of dollars at the most advanced nodes, and it gets harder at every node. Yet designers can’t easily search across decades of prior-tapeout learnings, characterization plots, IP documentation, and design-review minutes. Cross-corpus questions, on this IP block, on this process node, from this team’s prior work, are hard with traditional search, and bringing a new designer up to speed on a chip family takes months of tribal-knowledge transfer.
The value: A grounded assistant makes that history searchable, so designers spend less time hunting for context and more time designing. Cycle time is margin and time saved here compounds straight into faster, more competitive tape-outs.
Failure Analysis
The teams: FA engineers, quality and manufacturing engineering, RMA and customer-engineering teams.
The challenge: Once a chip is in the field, many things can go wrong, and top customers (hyperscalers, automotive, aerospace) expect a 24–48 hour initial analysis and root-cause resolution within roughly two to three weeks. Until the issue is resolved, shipments can pause. The data exists for years of similar parts, prior tickets, voltage distributions, schematic and characterization images, but pinpointing which variable caused the failure means searching across all of it at once. Multimodal retrieval is what makes that possible.
The value: A typical failure-analysis loop runs two to three weeks. In a customer proof-of-concept, Vectara helped bring that down to less than a week. Faster root cause means customers are unblocked sooner and shipments resume, which is a meaningful difference when a critical line is on hold.
Software & Support
The teams: Engineers building the SDKs, drivers, firmware, and developer docs that ship with the silicon, plus the customer-facing teams who run support portals and knowledge bases.
The challenge: A semiconductor company doesn’t just ship chips, it ships software stacks, datasheets, application notes, and a developer experience spanning hundreds of thousands of pages. Data is profuse in the semiconductor industry, and some organizations generate multiple terabytes of data a day. Customer support, internal developer productivity, and sales-engineering all depend on finding the right answer in that material quickly.
The value: Natural-language access to the full documentation set means engineers and customers get answers faster: more tickets resolved without escalation, smoother developer onboarding, and quicker turnaround on technical questions.
The value at a glance
Three engineering areas, each with a clear measure of value:
One platform, three areas of value
These aren’t three separate problems to solve with three separate tools. The same Vectara platform, built to manage the most challenging multimodal and complex data, API-first and deployable wherever your data has to live, supports all three. A natural place to begin is wherever the knowledge bottleneck is sharpest today, then extend the same capability across engineering. Silicon is the way.
