VLSI · EDA infrastructure & CAD · for chip startups and design centres

The CAD and IT team behind your chip.

We build and run everything your EDA tools run on — compute, shared storage, licences, remote desktops and IP protection — in code you own, on any cloud or your own servers. Start with your first engineers; grow to tape-out without a rebuild. Your engineers design the chip. We keep everything they design on working.

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RUNNING TODAYLive VLSI engineering infrastructure in production on public cloud.

Not a design house.

Your engineers design the chip. We run what they design on, so they never wait on IT.

Not a tool reseller.

Bring your EDA tools and licences, from any vendor. We install, version and keep them running.

Your CAD & IT team.

Infrastructure, tools, licences, accounts and security — one partner, from the first engineer to tape-out.

Sound familiar?

Six things chip teams tell us in the first call.

It's rarely the EDA tools. It's the infrastructure they run on — and nobody owns it.

“Our engineers are waiting in the queue, not designing.”The farm was sized for an average week. Tape-out isn’t an average week.Ch. 5 · Tape-out → “We’re paying for licences nobody seems to use.”No one can see which seats are busy, idle or double-booked.Ch. 6 · Cost → “Nobody can tell us where the compute went.”Jobs, nodes and spend aren’t tied to a chip or a team.Ch. 3 · Visibility → “A new hire took three weeks to run their first job.”Every environment is hand-built and slightly different.Ch. 1 · Day one → “Our customer wants proof their IP is isolated.”Access is shared, and nothing records who reached what.Ch. 2 · Security → “Every team runs the flow its own way.”Flows live in personal scripts, not a shared, measured pipeline.Ch. 4 · Flows →

$80–150K

Per engineer, per year

On EDA tools, IP and verification — after salaries, a chip startup's largest cost line.

The infrastructure underneath decides how much of it turns into design work. We keep licences and compute busy with real jobs, and size both to what the chip needs at each stage.

Source: SemiAnalysis, EDA market primer, May 2026.

The story

A chip programme, from first engineer to tape-out — with us alongside.

Seven chapters, in the order you live them. Each says what runs in production today, and what we're building next.

Chapter 1

Designing on day one

You getEvery engineer on the same working environment, on infrastructure you didn’t buy for its peak.

You faceA new programme usually means months building a compute farm before anyone runs a simulation.

TodayA small, complete environment from our blueprint, defined in code — remote Linux desktops behind the VPN, a Slurm cluster, shared storage, your EDA tools as versioned modules, and one login everywhere. Then we onboard every engineer, hands-on.

Chapter 2

IP protected from the first login

You getAnswers to a customer’s security questions, backed by how the environment is actually built.

You faceFoundry agreements and customer NDAs demand isolated design data and traceable access.

TodayDesign hosts are reachable only through the VPN, with two factors at sign-in. One company identity on every Linux node, and separate admin accounts, so a daily login can’t change the system.

NextPer-project access groups on shared storage, hardened images and recorded admin sessions.

Secured by IIP →

Chapter 3

See where the compute and licences go

You getDecisions on licences, compute and schedule made from data, not guesses.

You faceLicences, compute, coverage and security live in different tools, or nowhere.

TodayEvery job is recorded by user and queue. Queue limits stop one person filling the cluster. Licence checkouts can be queried, and failed checkouts diagnosed.

NextDashboards for every lead — licences, compute, verification, security — with alerts before anything fills, fails or expires.

Chapter 4

One flow, run the same way

You getRuns that behave the same for every engineer, so problems are the design’s, not the setup’s.

You faceEvery team runs its flow its own way, by hand, on whatever machine is free.

TodayShared tool launchers and queues, so every engineer runs the same tool the same way. Adding a tool class is a line of configuration, not a new script.

NextML on your own job history — failure clustering, runtime prediction — running inside your environment.

See AI & ML Ops →

Chapter 5

Tape-out doesn’t wait for hardware

You getCapacity that grows with the chip by changing code — not by buying servers.

You faceCompute need peaks in the final weeks — exactly when buying servers is impossible.

TodayWhen the work outgrew the compute nodes, we gave them four times the cores in place, from code — no rebuild, and the cluster picked up the new size.

NextCloud burst for sign-off, DRC/LVS and final regressions: nodes added when jobs queue and removed when they finish, with limits set in code.

Chapter 6

Spend follows the chip, not the worst case

You getPlans and renewals based on measured use.

You faceNobody can say which project, team or idle seat is consuming licences and compute.

TodayStart small and size compute to real use. Job records show who used what.

NextEvery licence and node-hour attributed to a chip and team, with budget guardrails in code.

Chapter 7

Yours, on any platform

You getMoving platforms is a decision about cost — and you can run it without us.

You faceInfrastructure built by hand for one cloud ties you to that provider, and to the people who built it.

TodayOne definition in Terraform and Ansible that runs on AWS, Azure, GCP, colo or your own servers. You own every repository and runbook, and we train your team to run it.

See Engineering →

What you get

Engineers designing, not waiting. Spend you can explain.

No infrastructure lead timeA working environment from our blueprint, grown in code as the team grows.

The same flow for everyoneShared, versioned tools and fair queues for every team.

Spend under controlCompute sized to real use, and licence use you can see.

Ways to start

Starting a chip? Have engineers designing on day one.

Tell us your programme and team size. We'll propose day-one engineering infrastructure and how it grows to tape-out.