An AI-native software company
Senior engineering,multiplied by agents.
We design and build software of every kind. Small senior teams direct AI agents working in parallel, and people make every decision that matters.
How it works
From a plain brief to production, in six steps.
Agents do the volume. A person makes every call, and you can see each step.
Brief
You describe the outcome, in plain language.
Plan
Agents draft the spec, the cost and the risks.
Approve
You approve it. A decision, not a ticket.
Build
Agents build in parallel, each in its own sandbox.
Verify
Tests, guards and review agents attack it.
Ship
A person opens the gate to production.
A person actsAgents act
Pace
Fast, because the work runs in parallel.
A traditional team works a queue. Our agents work side by side, so independent work never waits its turn.
One after another
Side by side
A schematic, not a measurement.
Planned before it is built
The thinking happens on paper, where change is cheap.
Checked on every change
Tests, guards and adversarial review, continuously.
Senior attention where it counts
People on judgement. Agents on volume.
What we build
If it is software, we build it.
Products
Consumer and member experiences.
Platforms
Accounts, payments and permissions.
Data systems
Sources of truth that explain every number.
AI and agents
Assistants that cite their sources, and ask first.
Internal tools
The software a company actually runs on.
Integrations
The services you already use, wired in properly.
Several products are in build right now, across music, entertainment, culture and business software. We will name them when our partners are ready to.
The standard
Agents work outside the boundary. People hold the keys.
Merging, deploying and touching production are impossible for an agent by construction, not merely disabled.
Evidence, or it does not ship
Every answer links to its proof.
No silent action
Agents propose. People confirm.
Impossible, not disabled
A refused attempt is still recorded.
Keys out of reach
Agent-written code can never read a credential.
Costed and capped
Every model call is metered against a budget.
Enforced by tests
If agent quality regresses, the build fails.
Tell us what you need built.
Describe the outcome in plain language. We will come back with how we would build it.