OFFERING · AI & ML PLATFORMS

Local AI &
Sovereign Inference

Models as a service, inside your walls.

The best AI lives in someone else's cloud, and for sensitive workloads a foreign cloud API is often not allowed at all. We deliver AI where the data already is: open-weight models on your GPUs, behind a governed gateway, consumed by your teams as a service. What you're choosing is where the AI runs and who controls it.

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Where the AI runs

Inside your perimeter

Open-weight models on your GPUs, behind a governed gateway, consumed by your teams as a service.

Governed gateway One gateway · one policy set · one audit trail

Your perimeter Illustrative local deployment · governed access to local serving

01
Teams, apps, agents Consume the endpoint as a service, with per-team budgets and throughput allocations.
02
Governed gateway Guardrails, security controls, and audit. Optional cloud routing uses the same gateway.
03
Local model serving Serves open-weight models from your catalog, evaluated and tuned for your workloads.
04
Model response The response returns through the governed endpoint to the consumer.

Only where data policy allows · routed strictly by data sensitivity · sensitive data anonymized on egress, fail-closed.

Cloud models Same gateway, same policy set, routed by data sensitivity.

Illustrative request path for local inference. Local serving runs on your GPUs, operated through MLOps. A cloud route is optional and policy-controlled; fully in-perimeter inference keeps model execution local.

Who it's for

The data can't leave.
The AI still has to arrive.

Data privacy first.

Client records, operational telemetry, regulated communications: the workloads that would benefit most from AI are usually the ones whose data cannot leave. That's the most common reason organizations come to us.

Control second.

No dependency on an external provider's release cadence, commercial terms, or continued goodwill for your AI capability to keep working.

And the cost shape changes.

From unpredictable per-token spend that grows with success to planned capacity you own and budget.

The models are good enough now

The gap closed
while everyone was arguing.

Two years ago, running AI locally meant accepting a visibly weaker model. That is no longer the trade. The distance between open-weight models and the closed frontier narrows with every release, and for the work that actually fills an enterprise (classification, extraction, summarization, code, retrieval, and the reasoning inside an agentic workflow) the best open models are now strong enough that the deciding factor is where they run, not how they score.

Smaller models, doing more.

A current sub-300B mixture-of-experts model performs close to the multi-trillion-parameter generation before it at a fraction of the cost to serve, because only a few experts activate per token. Below that, specialized models under 30B appear constantly, and a well-chosen small model on a narrow task routinely beats a large general one. Most organizations end up running several: a small model on the high-volume path, something larger for the hard cases, each pointed at the workloads it suits.

And a model larger than your hardware still fits.

Quantization, KV cache optimization, paged and shared attention, tensor and pipeline parallelism, continuous batching, prefix caching, speculative decoding: the serving recipe decides how much model a given GPU actually holds and how many concurrent users it serves. This is where most of our optimization work happens, and it is routinely the difference between needing twice the hardware and not.

The pace is the point.

New open-weight releases land every month. We evaluate them against your workloads, tune the serving recipe, and publish to your catalog, so the platform gets better without you rebuying anything.

What we deliver

Every inferencing service
an organization needs.

Models as a service.

A governed endpoint your teams, agents, and systems consume: guardrails, security controls, full audit, per-team budgets and throughput allocations. One gateway, one policy set, one audit trail; where policy allows, cloud models sit behind the same gateway, routed strictly by data sensitivity.

Model selection and evaluation.

Against your actual workloads, not benchmarks. We prove which model fits yours, at what quality and what cost per token served, before you commit to anything.

Optimization for your hardware.

Quantization, serving configuration, throughput and latency tuning, per workload.

Observability from day one.

The platform ships with its own observability stack on the LGTM foundation: dashboards, metrics, logs, and traces covering request rates, latency percentiles per model, token throughput, GPU utilization, queue depth, error and refusal rates, and consumption per team. Built as part of the platform, not a project that follows it.

Metering and internal billing.

Consumption metered per team, per model, per workload, turning into showback or chargeback at your rates, with budgets and rate limits enforced at the gateway. Part of the Models-as-a-Service build, not an add-on.

The serving stack.

vLLM and SGLang where throughput matters, Ollama where a small local footprint is what's wanted, LiteLLM as the gateway, OpenShift AI where enterprise support is required. Open source at every layer, chosen per workload.

Start today, sovereign tomorrow

From cloud to in-perimeter

A to D: each step increases sovereignty, and the platform stays the same.

Sovereignty is a spectrum Increasing sovereignty · A to D

Cloud Increasing sovereignty In-perimeter

A
Cloud, behind a governed gateway. Sensitive data anonymized on egress, fail-closed. No hardware wait.
B
Rented GPU. Evaluate open-weight models against your real workload before committing capital.
C
Owned hardware. Your GPUs, our MLOps: serving stack, optimization, capacity planning, lifecycle.
D
Fully in-perimeter inference. Models served entirely inside your data center. The sovereign end state.

Sovereignty increases from A to D, and the platform is the same at every step: no rebuild between steps.

The hardware question

Any hardware,
driven hard.

Local AI and ML inference on whatever accelerators you have. We make the most of the ones you already own, and if you're buying, we advise on the hardware against your use cases, projected growth, and the industry reality that models keep getting larger. Either way we apply the most advanced optimization configurations and recipes (quantization, parallelism, batching, caching, serving tuned per workload) to fit and run large models at scale as well as the hardware physically allows.

When inference is only the beginning: the Unified AI/ML Platform extends this same setup with training, computer vision, custom frameworks, real multitenancy, and full self-service. Same gateway, same governance, no rebuild.

Unified AI/ML Platform

Start today, sovereign tomorrow

Sovereignty is a spectrum.

Cloud, behind a governed gateway.

Sensitive data anonymized on egress, fail-closed. Start now, no hardware wait.

Rented GPU.

Evaluate open-weight models against your real workload before committing capital.

Owned hardware.

Your GPUs, our MLOps: serving stack, optimization, capacity planning, lifecycle.

Fully in-perimeter inference.

Models served entirely inside your data center. The sovereign end state.

Same platform at every step. No rebuild between steps.

How we deliver

Projects

Built with you.
Owned by you.

The inference platform designed and built in your environment, from hardware architecture to the governed gateway, with structured knowledge transfer.

Operations & Support

Operated as our own.

Day-2 operation of the platform and the models on it: serving, capacity, model lifecycle, under SLA.

Local AI &Sovereign Inference