A client needed a private, governed AI assistant across their company knowledge, without sending data to a third-party service. adBrain designed and built one for them, on open-source foundations, and deployed it inside their own infrastructure. It is proof of our enterprise AI platform engineering expertise, applied to a real organisation and handed over for the client to run and change.
The challenge
The client’s teams were losing time searching across scattered systems, and public AI tools were both a productivity dead-end (they know nothing about the company) and a data-leak risk. The client needed enterprise AI that knew their business, stayed inside their network, and that they could ultimately operate and change themselves.
What we built for the client
Starting from mature, permissively-licensed open-source components, we engineered and integrated the enterprise capabilities a production deployment requires, sized to the client’s needs:
- Enterprise sign-on and access control. Single sign-on ran through the client’s identity provider, with fine-grained, attribute-aware permissions and automatic role synchronisation, so access mirrored the client’s existing systems.
- Audit and accountability. A durable, tamper-evident record of activity for the client’s compliance needs.
- Central administration. One place to manage assistants, models, and settings, with the ability to route across multiple model providers.
- Cost visibility and control. Per-team usage budgets and spend reporting.
- Secure deployment. Containerised services in a private, network-gated production setup inside the client’s own environment.
Every one of these was built on the open-source base and configured specifically for this client.
The outcome: the client owns it
The client got more than a running system. They received the source code with the free right to modify it. They can audit it, extend it, and operate it independently of adBrain. No black box, no lock-in, no dependence on a vendor’s roadmap.
Why it matters
This is adBrain’s deployment expertise in practice: take strong open-source foundations, engineer the enterprise capabilities a real deployment demands, deliver it where the client’s data already lives, and hand over ownership. The same expertise is available to any organisation that wants private enterprise AI it genuinely controls, and it is the kind of environment into which our machine-learning decisioning models can also be deployed.
Enterprise AI platform engineering · Deployment & ownership