SA
Silicon Agents Pitch Deck
Current MVP status · verification-first semiconductor AI platform
Silicon Agents / Infosys Business Incubator Pitch

AI Copilots for Chip Design Workflows

Silicon Agents is a semiconductor workflow intelligence layer that converts raw verification and test artifacts into ranked, evidence-grounded engineering actions. The current MVP proves the wedge where fabless teams lose the most time: verification review, coverage closure, and regression triage.

Agent 01: verification wedge
Agent 02: yield expansion
Run history + audit trail
Enterprise policy + orchestration
Open live Agent 01 demo Open run history
Current MVP status
Live
Runnable product with UI, backend, LLM orchestration, exports, feedback, and persisted history.
Primary wedge
Agent 01
Coverage closure and regression triage for verification teams approaching tapeout.
Proof posture
Benchmark + audit
Known artifacts, measurable scorecards, human approval loop, and saved run evidence.
Why this matters

The bottleneck is not raw data. It is engineering interpretation.

Verification teams

Senior DV engineers spend expensive hours reading coverage reports and regression logs before deciding what to fix next.

Yield and test teams

ATE anomalies, mis-bins, and SPC drift often require multiple manual handoffs before a prioritized action emerges.

Enterprise consequence

Schedule risk grows in the gap between artifact generation and engineering action selection.

Silicon Agents does not need to replace EDA tools. It needs to reduce the time senior engineers spend converting reports into next actions.
What exists today

The MVP is already more than a static demo.

2 agents
01 + 02
Verification-first wedge with yield workflow expansion already live in the same platform.
5 product pages
Shell
Home, Agent 01, Agent 02, Enterprise Config, and Run History.
Run audit trail
SQLite
Provider, latency, scorecard, decisions, feedback, and exports are persisted per run.
Exports
HTML / Jira / Email
Built for sponsor review and workflow adoption, not just an AI chat output.

Agent 01 delivered

  • coverage and regression ingestion
  • 5-step streamed reasoning
  • ranked findings with evidence and review actions
  • benchmark scorecard and verification brief export

Agent 02 delivered

  • ATE and SPC ingestion
  • ranked yield and anomaly actions
  • benchmark parity with Agent 01
  • yield brief, Jira, and email export support
Why this is credible

The MVP already demonstrates a scalable platform pattern.

Common orchestration core

Both agents share the same product shell, run history, feedback loop, export layer, benchmark scoring path, and enterprise policy model.

Enterprise policy + run profile split

Top-level policy can be set by senior engineers or program leads, while day-to-day run profiles remain operational and reusable.

Two-stage LLM flow

Orchestration first, analysis second. This is important because enterprise clients need control over style, evidence, and escalation policy.

Human-in-the-loop by design

Recommendations are visible, ranked, exported, accepted or rejected, and stored for later audit. This matters in semiconductor workflows.

Current proof signals

This MVP is enough to prove the wedge if it is pitched honestly.

What this MVP proves now

  • the workflow problem is real and clearly framed
  • a verification-first wedge can be turned into a real product shell
  • the architecture already supports multiple agent families
  • enterprise controls, exports, and auditability can be layered in without rebuilding the core

What this MVP does not claim yet

  • deep native EDA integration
  • multi-user auth or tenancy
  • broad parser coverage for every client report format
  • production deployment inside a semiconductor enterprise environment
For Infosys, this is strong enough as a Seeder-stage MVP because it proves product thinking, architecture direction, and workflow fit. The right positioning is not “finished platform.” The right positioning is “credible wedge with scalable architecture.”
Roadmap

Next phase of the MVP: enterprise API integration for EDA workflows

Now

Workflow intelligence MVP

UI, agents, orchestration, scorecards, run history, exports, and human review loop are in place.

Next

Expose integration APIs for enterprise EDA flows

Add external ingestion and trigger endpoints so existing verification farms, regression systems, dashboards, and engineering workflow tools can push artifacts into Silicon Agents and consume structured results.

Then

Client workflow embedding

Integrate with real project artifacts, support customer report variants, and align outputs with real engineering review systems.

API phase target

Make Silicon Agents callable from external flows, not only from the UI.

Why this matters

That is the step that turns a sponsor demo into a systems-integration opportunity for Infosys.

Infosys fit

Infosys can win on managed delivery, workflow embedding, client-specific policy, and semiconductor services integration.

Why Infosys can care

This is not just an AI demo. It is a services-platform wedge.

For semiconductor clients

  • faster first-pass review of coverage and regression outputs
  • more structured prioritization of action items
  • visible evidence, history, and export into downstream workflows

For Infosys

  • semiconductor AI services wedge, not generic LLM tooling
  • high-value integration-led offering around existing client toolchains
  • expandable from verification into yield, test, and signoff review workflows
The long-term moat is not only the model call. It is workflow embedding, domain-specific orchestration, client policy control, and trusted engineering review loops.
Ask

Support the next phase: take the verification wedge into enterprise integration.

What support unlocks

  • integration API layer for enterprise EDA workflow connection
  • broader artifact corpus and parser tolerance
  • pilot-grade instrumentation and client-specific onboarding
  • deeper proof with real project-style artifacts

What the current MVP already proves

  • strong technical ownership
  • clear semiconductor wedge selection
  • scalable architecture thinking
  • the ability to build beyond a first-year-college-project level into a credible product direction
Show Agent 01 live Show saved runs and exports