Design Verification Query Agent for Chip Development
Semiconductor GenAI case study for accelerated verification analysis across fragmented engineering data.
About Customer
The customer is a chip development organization running large-scale design verification cycles with data distributed across SQL metrics, NoSQL coverage logs, and defect-tracking APIs. Teams needed faster cross-system analytics for module-level decisions.
Verification leads wanted one conversational interface that could query multiple systems securely and return consolidated answers in minutes.
Problem Statement
Engineers and managers needed real-time holistic visibility, but verification data remained fragmented across incompatible stores. Critical questions required manual stitching of multiple reports before action could be taken.
A representative customer story came from late-stage verification closure reviews where leads had to reconcile coverage gaps, defect trends, and failing log clusters before sign-off. Each data source told part of the story, but assembling a complete answer required multiple tools and hand-built correlations, delaying go/no-go decisions at the most schedule-sensitive phase.
This slowed debug loops, delayed closure decisions, and increased tape-out risk when turnaround time mattered most.
- Days-long manual correlation across SQL, NoSQL logs, and defect APIs.
- Limited ability to answer cross-domain verification questions quickly.
- Higher risk of schedule slip due to analysis bottlenecks.
Solution Architecture
Zettabolt deployed a Multi-Agent Verification Analytics System where an LLM + RAG (Retrieval-Augmented Generation) layer translates plain-English questions into queries that span multiple, very different systems - RTL (Register-Transfer Level chip-design) metrics in SQL, coverage reports in log databases, and bug reports in a defect-tracking API. A custom Orchestration Agent breaks the question down and dispatches it to specialist Metrics, Log, and Bug agents that securely access each system through ZettaLens connectors, returning a single consolidated answer 40X faster than manual correlation and cutting verification cycle time up to 40%. Here is how we integrated the pipeline:
Implementation Highlights
- Built a multi-agent verification system using LLM + RAG for federated natural-language analytics across heterogeneous data stores.
- Added orchestration to route queries to specialized metrics, log, and bug analysis agents with context sharing.
- Used secure ZettaLens wrappers to query governed systems without compromising controls.
- Standardized final responses with cross-source evidence so teams could act without manual reconciliation.
Implementation context: The rollout focused on top verification questions that consumed the most manual effort. By converting these into reusable agent patterns, teams quickly moved from fragmented report navigation to consolidated, decision-ready responses with much lower turnaround time.
Business Impact
Verification leaders gained faster closure visibility and reduced manual report stitching, enabling earlier intervention on critical module risks.
