Our mission

Close the loop on
RTL correctness

Chip design is the last major engineering workflow AI agents haven't truly touched. Verification eats most of the effort, talent is scarce, and the tools are slow to change. Nexeda exists to make "is this design correct?" something an agent can answer — and fix.

The opportunity

Why now

Verification dominates

60–70% of a chip project is spent proving correctness — the part most AI tools skip.

👥

Talent is scarce

Demand for design & verification engineers far outstrips supply. Teams need leverage, not more headcount.

The pieces exist

Capable coding agents, production-grade open simulators, and a clear gap the incumbents are slow to fill.

Strategy

Earn trust before automating it

Engineers don't hand over their chips on day one. So Nexeda starts as a review-and-verification copilot — low liability, high trust — and grows into generation once the loop and the dataset have earned it.

FPGA-first, because specs are cleaner and correctness is checkable end-to-end. Humans stay in the loop; full autonomy is the destination, not the launch claim.

Land Review & verify — find bugs, write assertions, prove correctness.
Expand Generate — standard blocks, signed off automatically.
Scale Subsystems & ASIC — broader scope, timing closure, formal.
Roadmap

Where we're headed

A deliberate path from a trusted copilot to autonomous, timing-aware design.

Phase 0 · now

The verified loop

Review, verify and generate for standard FPGA blocks on the open toolchain, with a benchmark proving convergence. Live today in the Playground.

Phase 1

Design partners & eval flywheel

Ship review/verify to real teams, grow the benchmark to ~20 blocks, and measure bug-find rate and time-saved on real RTL.

Phase 2

Vivado & on-prem

Full place-and-route with timing-driven iteration and XDC generation on a dedicated worker; self-hosted deployment for IP-sensitive customers; formal checks.

Phase 3

Subsystems & ASIC

Multi-module scope and the ASIC flow — pushing toward higher autonomy as the dataset and trust compound.

What compounds

The model is rented. The moat isn't.

Eval & dataset

Proprietary spec → RTL → bug → fix traces from every run.

Toolchain depth

Robust log/waveform parsing and repair that actually converges.

Workflow trust

Verification-first adoption inside real engineering flows.

On-prem

Running where the IP lives — a requirement, not a feature.

Building in chip design + AI?

Whether you're an engineer who wants to try it, a design team exploring a pilot, or just curious — get in touch.

Get in touch Try the Playground