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Optimized silicon for every product creator powered by agentic AI

The first AI cloud platform that designs, verifies, and delivers optimized silicon autonomously. Cut NRE by 80%. Ship 8X faster.

12 chips designed this month

Let Our AI Agents Design, Build, and Deliver Your Chips

The traditional approach is broken. Here's how we're fixing it.

Metric

Traditional

NativeChips

NRE Cost
Millions
Pay per usage
Team Size
10s to 100s of engineers
Product team + AI
Timeline
18-24 months
2-4 weeks
Success Rate
50% first-spin
95% first-spin
Expertise Required
PhD-level specialists
Product Level
Parallel Development
Not viable - sequential bottlenecks
Massive parallelism with AI
Requirements to Spec
Slow design exploration
Fast parallel exploration
Risk Level
High
Low

Why Traditional Fails

  • Massive upfront investment required
  • Need to hire and retain specialized talent
  • High risk of design failures and re-spins
  • Long development cycles delay time-to-market
  • Sequential bottlenecks prevent parallel development
  • Only accessible to large corporations

Why NativeChips Wins

  • Pay only for successful silicon
  • No chip design expertise required
  • AI handles all technical complexity
  • Massive parallelism enables concurrent development
  • Fast iteration and testing cycles
  • Accessible to companies of any size
AI Agent Process

How AI Designs Your Chip

See the step-by-step process our AI agent follows to transform your requirements into production-ready silicon.

16
Weeks to Silicon
1

Requirements Analysis

AI understands your specifications

2

Architecture Planning

Selects optimal design approach

3

RTL Generation

Creates synthesizable Verilog

4

Verification

Comprehensive testing suite

5

Physical Design

Place & route optimization

6

Tapeout Ready

Silicon-ready GDSII files

12 chips designed this month

From Idea to Silicon: Fully Autonomous

Every step inspectable. Every decision explainable. Every output production-ready.

1

Describe Your Chip

Natural language or technical specs. AI understands context and constraints.

2

AI Plans Architecture

Creates full documentation, generates task breakdown, selects optimal IPs.

3

Automatic Implementation

RTL generation, place & route, timing optimization.

4

Silicon-Grade Verification

CocoTB system tests, PyUVM unit tests, coverage analysis.

5

One-Click Manufacturing

Direct foundry integration, automated DRC/LVS, silicon in 16 weeks.

Solutions for Every Team

10X Your Chip Design Team's Output

Deploys in your cloud. Trains on your IP library. Follows your methodology. Integrates with existing EDA tools. Your engineers stay in control, AI does the heavy lifting.

Design 10 chips in the time it takes to design 1

Massive productivity gains

80% reduction in NRE costs

Dramatic cost savings

Maintain your review process

Full control and oversight

Learn from every tapeout

Continuous improvement

Production Ready

Built on Proven Technology

Leveraging battle-tested open source tools and industry-standard processes that have enabled thousands of successful tapeouts.

Currently Supported

ASIC designs for Microcontroller Applications with Edge AI Accelerators
Skywater 130nm Technology
System-level verification and unit testing
Silicon-proven Open Source EDA tools and workflows

Coming Soon

Mixed-signal support
Propriatary EDA tools and workflows
Additional foundries
40, 28 and 22nm nodes
Packaging options

Frequently Asked Questions

Can I really trust AI to design my chip?

Every decision is inspectable. All code is reviewable. Verification is comprehensive. You can intervene at any step.

What if I need to modify the AI's output?

Full VSCode integration in browser. Edit on the browser. Modify and re-run any step. Use as starting point or complete solution.

Who owns the IP?

You own everything. No claims on your designs. Your data stays private. Enterprise version runs in your cloud.

What are the limitations?

Currently digital only (analog coming). 130nm opensource today (more PDKs soon). Best for designs under 10M gates. Optimized for specific design styles.

How is this different from ChatGPT + Verilog?

Purpose-built for silicon. Trained on successful tapeouts. Integrated verification. Direct path to manufacturing.

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