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Learn how we built Mockingbird V3 MM, a multimodal model for semiconductor AI agents that can produce grounded answers and work through multi-step engineering tasks.
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A well-cited answer from the wrong chip revision can mislead a design decision. An agent that understands the context behind the question can side-step this pitfall.
Business Suite provides key stakeholders with a single pane of glass for evaluating the business performance of your agents.
Explore Boomerang V2 benchmarks for semiconductor document retrieval, comparing image and mixed text-and-image search with Boomerang V1, Google, and OpenAI models.
Learn how image taxonomy and parallel document processing convert image-heavy PDF, PPTX, and DOCX files into structured, agent-ready Markdown.
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.
The highly-technical vocabulary of the semiconductor industry requires models with specialized training, provided with direct access to rich, multimodal data and complex documents.
How we built our own engineering operations agent for responding to and resolving incidents and bugs
Hybrid AI architecture connects private infrastructure and SaaS models through intelligent routing, balancing security, cost, performance, and model flexibility.
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.
