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The highly-technical vocabulary of the semiconductor industry requires models with specialized training, provided with direct access to rich, multimodal data and complex documents.
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Boomerang V2 model is built for retrieving semiconductor data
Explore Boomerang V2 benchmarks for semiconductor document retrieval, comparing image and mixed text-and-image search with Boomerang V1, Google, and OpenAI models.
Turning image-heavy technical documents into agent-ready Markdown
Learn how image taxonomy and parallel document processing convert image-heavy PDF, PPTX, and DOCX files into structured, agent-ready Markdown.
Boomerang V2: The next generation of multimodal enterprise search
Search beyond text with Boomerang V2 built for long, mixed-content documents: 8,192‑token context and 1024‑D embeddings with Matryoshka truncation for storage/latency tradeoffs. Benchmarked for semiconductor engineering.
Bender: Vectara’s AI agent for engineering operations
How we built our own engineering operations agent for responding to and resolving incidents and bugs
What is hybrid AI architecture? A practical guide to building flexible enterprise AI systems
Hybrid AI architecture connects private infrastructure and SaaS models through intelligent routing, balancing security, cost, performance, and model flexibility.
The agent that forgets: Stateful vs. stateless AI agents
Stateless agents are simple to scale. Stateful agents are built for continuity. This post breaks down the trade-offs between stateful and stateless AI agents, what changes when state moves behind the API boundary, and why persistence is only the first step toward trustworthy agent memory.
Your sovereign AI stack is probably leaking
Running your model in your own VPC doesn’t make your AI stack sovereign. The real risk is in everything the model talks to.
Moving beyond text conversion: The future of enterprise search
Enterprise search draws data from documents, charts and diagrams. Vectara is building a successor to Boomerang, our multimodal embedding model, and changing retrieval.
Building verified multimodal training data for semiconductor AI agents
RTL simulations, timing traces, pin diagrams, and trusted hardware specifications can provide independently derived answers for semiconductor AI training data before a language model enters the loop.