The Future of Invisible Edge AI
Power-efficient Analog In-Memory AI for every sensor on the planet
The Problem
Sensors everywhere.
Intelligence nowhere.
The world is drowning in data from billions of sensors — but almost none of it is processed where it matters most.
Cloud AI
Data round-trips to distant servers. Latency kills real-time decisions.
Digital Chips
Power-hungry processors generate heat. Unsustainable for edge deployment.
Analog Rivals
Incomplete solutions. Missing the full-stack integration needed for production.
Almost none with local intelligence
Our Approach
The bicycle motor for AI
Analog in-memory computing performs matrix multiplications directly in memory — the core operation behind all neural networks.
Analog Matrix Multiplication
Analog computation eliminates the digital overhead that wastes energy on every operation.
Deploy intelligence in sensors with no external power supply needed.
No cloud dependency. No data leaves. Privacy and speed by design.
Technology
Built from first principles
A vertically integrated stack — from silicon to software — designed for maximum efficiency at the edge.
Proprietary Analog Circuit Design
Custom-engineered circuits that perform neural network computations in the analog domain — bypassing the von Neumann bottleneck entirely.
Accelerated Matrix Multiplication
Physics-based computation at the speed of electron flow. No clock cycles, no memory fetches — just instant parallel processing.
Full-Stack Hardware–Software Co-Design
From transistor-level design to compiler toolchains — a vertically integrated stack optimized end-to-end for edge AI workloads.
Team
Silicon-native team
Engineers who think in transistors and dream in architectures.
Founder & CEO
MS VLSI, ex-semiconductor R&D
Head of Analog & Digital
Full-Stack Silicon Architect
PhD Microelectronics, 15+ tape-outs
Head of AI
Edge ML Optimization
PhD Machine Learning
Advisors
Senior Advisor
45+ years in semiconductor industry
Former VP Engineering, Global Foundry
Other Technical Advisor
Analog IC design pioneer
100+ patents, IEEE Fellow
Deep roots in silicon — from transistor to tape-out
MS/PhD engineers from top research programs
Advisors with 45+ years experience in chip design
Market Timing
Why now
Four converging forces make this the perfect moment to build analog edge AI.
Analog IMC Breakthrough
IBM demonstrates 14nm analog in-memory chip achieving record energy efficiency for AI inference. The science is proven.
Edge AI Demand Explosion
The edge AI market is projected to grow 20×. Every autonomous system, wearable, and industrial sensor needs on-device intelligence.
Affordable Mature Fabs
130nm and 65nm fabs are widely available and cost-effective. No need for bleeding-edge nodes — analog thrives on mature process.
Industry 4.0 Sensor Boom
Smart factories, predictive maintenance, autonomous vehicles — all demanding real-time AI at the sensor level with minimal power.
AI belongs at the edge.
We're building the silicon that makes it possible. Join us in redefining how intelligence reaches every corner of the world.