GENAI OS
Run GenAI as a managed capability.
GenAI stops being a collection of projects when it becomes something you can operate.
- Shared services every team consumes and no team rebuilds
- Governance built into the road, not bolted on as a review gate
- A paved road, so builders focus on the use case, not the plumbing
A GenAI OS gives teams one governed operating layer.
A GenAI OS makes GenAI predictable to run: a paved road where safety, evaluation, cost control and governance are standard.
When guardrails, audit trails and cost attribution are built into the path every team uses, governance stops being a review gate and becomes a property of the system. That is the shift from did we build an AI app to can our whole organisation ship AI safely by default. It is an operating question, and it needs an operating layer.
Make GenAI boring to run, and every team ships it safely by default.
Services every team consumes and no team rebuilds.
Together, these services replace **each team solving the same platform problems** with **a shared operating layer that already handles them.**
The operating layer, delivered.
Shared gateway and retrieval
A model gateway and retrieval services consumed across teams.
Centralised evaluation and observability
Prompt, evaluation, and observability so quality and cost are visible org-wide.
Org-wide guardrails and policy
Enforced consistently, aligned to Responsible AI.
A developer experience layer
SDKs, templates, and self-serve onboarding.
Governance and audit
Spanning every GenAI application in one place.
Stand up the core, then pave the road.
Map the estate
Find where teams duplicate services and where risk goes ungoverned.
Stand up the shared core
Gateway, retrieval, evaluation, and guardrails first.
Pave the road
SDKs and golden-path templates, so building the right way is the easy way.
Onboard teams
Migrate existing apps onto shared services and enable new ones.
Govern and evolve
Audit, cost attribution, and continuous improvement of the platform.
Three ways in, sized to where you are.
Stood up to a defined spec
You need the shared operating layer stood up against a defined architecture.
A team that owns and evolves it
A dedicated team owning and evolving the operating layer with you.
Scattered apps onto one layer
You have scattered GenAI apps and want them onto one governed layer.
The operating layer of the platform.
GenAI OS is the operating layer of Scaled GenAI and AI Platforms, built on the platform foundation.
AI Platform Design and Implementation
The infrastructure the operating layer sits on.
LLMOps
Evaluation, guardrails, and cost control, run as shared services.
AI Agent Builder
The composition layer the OS hosts for agentic workloads.
LLM Fine-Tuning
Adapters served through the shared model gateway.
MLOps
The discipline for classical and predictive models.
Dedicated Delivery Pod
A team to operate and evolve the GenAI OS with you.
What platform leads ask us first.
It is the operating layer on top of the platform. The AI platform provides the infrastructure; the GenAI OS provides the shared services, governance, and developer experience that make GenAI a capability teams consume.
With an estate assessment. We map duplication and ungoverned risk, then stand up the shared core, gateway, retrieval, evaluation, guardrails, and migrate existing apps onto it.
By making the responsible path the default path. Guardrails, audit, and cost attribution are built into shared services, so governance is enforced consistently rather than reviewed app by app.
A dozen projects, or one operating layer?
Let's map your GenAI estate and design the operating layer underneath it.