About

Enterprise AI that does not stay a pilot.

DareData team in a workshop session
DareData team in the office
DareData team outdoors

Where GenOS comes from

Built from real deployments,
not from research.

GenOS is built by DareData Engineering — a data and AI engineering company that has been deploying production systems for European enterprises since 2019.

After years of building AI systems for clients across retail, telecoms, financial services, and logistics, a pattern became clear: the hard part of enterprise AI is never the model. It is governance, knowledge management, system integration, and keeping it working after go-live.

GenOS is the product that came out of solving those problems repeatedly. It is the operating system we wished had existed when we started — and the one we now deploy for every client.

How we work

The product and the team are the same thing.

Every GenOS deployment is run by DareData Field Deployment Engineers — the same engineers who built the platform. They sit with your team, map the workflows, build the integrations, and stay engaged in production.

You get a named engineer who owns your deployment. Not a support ticket queue. Not a CSM reading from a runbook. The person who built it.

01

Scope

We map your workflows, identify the highest-value automations, and define what production success looks like — before a line of code is written.

02

Build

Your FDE integrates GenOS with your systems, configures agents for your specific workflows, and sets up the governance and audit layer alongside your IT team.

03

Deploy

We run a supervised go-live. Every edge case is monitored, exceptions are handled before they become incidents, and the team is on-call through stabilisation.

04

Run

Your FDE stays on the deployment — owning production issues, running improvement cycles from real usage data, and expanding to new workflows as the system proves itself.

This is what separates a GenOS engagement from buying a software licence. When something breaks in production, the engineer who built it owns the fix — that loop does not exist in generic AI software.

How we work

Deployment over demos

We measure success by workflows in production, not pilots presented to a steering committee. If it is not live, it has not shipped.

Governance first

Access control, audit trails, and usage monitoring are built into the foundation — not bolted on after the fact. Enterprises need to trust the system before they scale it.

Improvement is the product

The system that goes live on day one is not the system that exists six months later. Human review loops, automated evaluations, and feedback cycles are part of every deployment.

See it in production.

Read how we deployed for Sonae Sierra, NOS, and Sogrape.

Success storiesdaredata.engineering →