Platform
Engineering Intelligence Layer
Knowledge graph, domain engines, and agent orchestration spanning the full semiconductor lifecycle.
Semiconductor Engineering Infrastructure
AI Co-Workers for the Semiconductor Lifecycle
The biggest chipmakers built private AI to move faster on every node. ChipGPT brings that leverage to everyone else through specialized AI co-workers that produce silicon-grade work, validated by the tools your teams already run.
ChipGPT Opsprogram-alpha
Connected
Open Derivative Programs
3
this cycle
Verification Threads Tracked
12
open items
Recurring Patterns Flagged
7
review queue
Engineer-Hours Returned
—
institutional memory
Debug Workflows
DBG-0847Active
Interface revision — spec and RTL sync pending
Design
DBG-0844Review
Traceability matrix update — FMEDA review
Verification
DBG-0839Closed
Cross-program dependency resolved
Platform
Agent Activity
14:32Memory
Pattern matched against prior tapeout
14:18RTL
Specification and RTL synchronized after interface revision
13:55Verification
Traceability matrix updated against latest FMEDA review
13:41Platform
Cross-program dependency flagged
13:22Knowledge
Historical engineering decision surfaced
Program Continuity
Activeknowledge graph
Lifecycle Context
Retainedone yield-learning reference
Institutional Memory for Silicon
Agents learn your program, not just your prompt.
Platform
Engineering Infrastructure, Not Software
ChipGPT is the operating layer for semiconductor teams. Explore each capability in depth.
Lifecycle
Specification Through Production
ChipGPT operates above the workflow—not as a stage—powering every phase from RTL to yield.
Agents
Specialized Co-Workers
RTL, verification, bring-up, yield, failure analysis, and knowledge agents with governed outputs.
Architecture
Enterprise Infrastructure
EDA integrations, secure data ingestion, and deployment models built for tapeout scale.
Outcomes
Where Teams See Payback
Qualitative outcomes across verification continuity, design traceability, and institutional knowledge.
Enterprise
Security & Governance
VPC, on-prem, air-gap deployment with audit trails and human-in-the-loop controls.
Use Cases
By Engineering Role
Co-workers that preserve context, continuity, and institutional knowledge across the programs your teams run.
Design Engineers
- Preserve spec-to-RTL context across interface revisions
- Surface prior closure decisions during design reviews
- Maintain continuity between derivative programs
Verification Engineers
- Reduce time reconstructing regression and coverage context
- Match current failures against prior tapeout patterns
- Keep verification narratives aligned across handoffs
Bring-Up Engineers
- Carry debug thread history across lab sessions
- Connect silicon observations to pre-silicon artifacts
- Retain experiment rationale for the next engineer
Yield Engineers
- Connect yield learning signals across program history
- Retain institutional context when investigating shifts
- Cross-reference prior programs without restarting analysis
Test Engineers
- Preserve characterization and test-program continuity
- Surface historical guardband decisions during updates
- Maintain knowledge across test insertions and revisions
Failure Analysis Engineers
- Correlate failure signatures across prior silicon generations
- Surface historical root-cause investigations
- Reduce duplicate debug effort across programs
Trust
Your data stays in your environment. You own it, you can export it anytime, and we don't train models on it — ever.
Accelerate Engineering Through Specialized AI Co-Workers
We work with a small number of design-partner programs each cycle.
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