Local-first AI agents.
You own the code.
We design and build multi-agent workflows that run inside your infrastructure. The engine is configurable, the source code is yours, and your data never leaves your network.
Two ways to get AI inside your company. Both are wrong.
You either rent a SaaS that ships your data abroad on every call, or you hire an AI engineer at €100K and wait nine months for a working prototype. Neither survives a real audit. Neither scales past the pilot.
- ✕ Your prompts and documents leave your network on every call.
- ✕ APIs deprecate, models change, prices triple — without your consent.
- ✕ Cloud Act, T&Cs drift, training-data leakage. You don't own the surface.
- ✕ You can't fork it. You can't audit it. You can't keep running it.
- ✕ Half a year before they ship anything that survives prod.
- ✕ One person away from "we have to start over".
- ✕ Every junior team rebuilds the same orchestrator from scratch.
- ✕ No published track record on the multi-agent stack.
- ✓ A multi-agent engine that already works — orchestrator, memory, tools, eval loop.
- ✓ Deployed on your infrastructure. Source code in your private remote.
- ✓ Open-source models by default. Frontier APIs only when you decide.
- ✓ Audit logs, permission boundaries, a rehearsed stop procedure. Designed to be inspected.
One engine. Many configurations.
One codebase underneath: orchestrator, agent runtime, memory, tools, evaluation loop. It ships in three shapes. Local agents that auto-evolve, tuning themselves on your real traffic. Chatbots that answer your customers on your own documents. Optimization loops that sharpen a system you already run. Your data stays inside your boundary unless you wire in a frontier API.
Three things we refuse to compromise.
Every page, every conversation, every line of code reinforces these. They're not features. They're the floor.
Privacy
Every byte stays on your hardware. Prompts, documents, vectors, logs — none of it crosses the boundary you set. Air-gap compatible. Audit-ready by default.
Ownership
You receive the source code. It lives in your private git remote. You can fork, audit, extend, and keep running it if Optetron disappears. No subscription holds it hostage.
Control
You choose the models, the hardware, the integrations. Open weights by default, frontier APIs only when you decide. Permission boundaries on every tool. A stop procedure your team has rehearsed.
It's software. It deploys in days.
Optetron isn't a quarter-long build — the engine already exists. Install it on your infrastructure, connect your data and tools, and it's live. The source sits in your private remote from the first day, and nothing runs anywhere but your hardware.
Install
Drop the engine onto your infrastructure — on-prem, your VPC, or air-gapped. No SaaS account, no data leaving your network.
Connect
Point it at your problem: your data, your tools, your models wired in. The engine is configurable, not bespoke — the graph already works.
Live
It answers on your real traffic in days. The source code is in your private git remote from the start — yours to fork, audit, and keep running.
Prefer we handle the integration for you? That's optional — a scoped setup or an ongoing retainer. See consulting →
Most software peaks the day you deploy it. This one doesn't have to.
To improve, ordinary software needs the vendor back in the loop — a new contract, a round-trip to their cloud, your data in their hands again. The engine you own works the other way.
Optetron auto-evolves. After deployment, the engine can keep optimizing its own behavior from your real traffic — on your hardware, inside your boundary. No vendor round-trips, no re-engagement fee. The capability to improve stays on your side of the line, not the vendor's.
Sovereignty intact. Nothing leaves the perimeter. You own the code — and any improvement stays inside it.
- Deploythe engine goes live on your infrastructure
- Real trafficyour users, your data — never leaving your boundary
- Auto-evolvethe engine can tune its own behavior on your hardware
- Better responsesresults feed back into the loop — on your side
Tell us where the data can't leave.
We'll bring the agents to it.
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