LLMOps
Operating what the platform runs, once it is live.
LLMOpsEnterprise AI Platforms
One governed platform serving many use cases — instead of a dozen disconnected pilots nobody can secure or support.
The problem
The second-year problem
The second year of an AI programme looks nothing like the first. Six teams have built six systems on four different stacks. Security is reviewing each one separately, from scratch. Nobody can answer what AI costs the company or which data it touches.
Each new use case restarts the same argument about architecture, model choice, and data handling — and each one takes as long as the first did.
The fix is a platform: shared inference, shared retrieval, shared identity, shared observability, shared guardrails. Paved paths that let product teams ship AI features without re-litigating the foundations every time.
Governance then becomes a property of the platform rather than a review process bolted on afterwards — which is the only version that scales.
Scope
What we deliver
- Inference gateway — One entry point with routing, rate limiting, cost attribution, and policy enforcement. Model choice becomes configuration rather than an architectural commitment per team.
- Shared retrieval services — Ingestion, indexing, and identity-aware retrieval offered as a service, so each team is not building its own pipeline and its own permission bugs.
- Identity integration — One integration with your directory, enforced consistently — rather than six interpretations of what a user may see.
- Guardrails and policy — Input and output controls applied at the platform layer, so they cannot be forgotten by an individual team.
- Evaluation tooling — Shared harnesses and datasets so quality is measured the same way across the portfolio.
- Observability and cost attribution — Per-team, per-use-case visibility into quality, latency, and spend.
- Paved paths — SDKs, templates, and self-service onboarding that make the secure route the easy route.
Detail
Platform layers
| Layer | What sits here |
|---|---|
| Infrastructure | GPU compute, Kubernetes or OpenShift, storage, networking, and isolation boundaries. |
| Serving | Model serving, autoscaling, batching, quantization, and multi-model routing. |
| Gateway | Authentication, routing, rate limiting, guardrails, cost attribution, and audit logging. |
| Data & retrieval | Ingestion, indexing, entitlement-aware retrieval, and knowledge services. |
| Application | Product team code, built on SDKs and templates rather than raw infrastructure. |
| Governance | Inventory, risk classification, approval workflow, evaluation, and audit evidence — spanning every layer. |
Questions
Frequently asked
When is it too early for a platform?
Before the second or third use case. Building a platform for one workload is premature abstraction. The signal to start is when a team is about to rebuild something another team already built.
Can this run on our existing Kubernetes?
Usually, and that is the preferred path. AI workloads become a first-class citizen on infrastructure you already operate, rather than a parallel stack with its own on-call rotation.
How do we migrate existing pilots onto it?
Incrementally. Put the gateway in front of existing systems first for visibility and cost attribution, then migrate retrieval and serving as each team touches its code. A big-bang migration is unnecessary and rarely survives contact with roadmaps.
Does a platform slow teams down?
Only if it is built as a gate. Built as paved paths — with sensible defaults and self-service onboarding — it is faster than each team solving architecture and security independently, which is what it replaces.
Related
AI Security
Threat modeling and controls, designed into the platform layer.
AI SecurityStart with an assessment, not a proposal.
A structured evaluation of your data, infrastructure, security constraints, and candidate use cases — delivered as a prioritized roadmap you own.
Direct response from an engineer. Typically within one business day.