Inference infrastructure for AI that actually runs in production.
Serving AI at scale is as much an infrastructure problem as it is a model problem. We design the compute, serving and deployment layer around the model, workload and environment — from efficient small-language-model inference to private GPU infrastructure.
Built for production AI, custom deployments and specialized workloads.
For CTOs, technical leads and AI teams — including healthcare organizations, laboratories and research institutions with private AI requirements.
We choose infrastructure around the workload.
We don't default to a fixed stack. The right infrastructure depends on the model, latency target, traffic pattern, memory requirements, serving framework and deployment environment — and we design around those specifics rather than whatever's familiar.
Four core capabilities
The engineering layer beneath production AI, Custom AI and Private AI.
Production inference
Serve language and multimodal models efficiently in production — model serving, batching and scheduling tuned for real concurrency, latency and throughput targets, with memory use treated as a first-class constraint rather than an afterthought.
GPU & accelerator optimization
Choose compute around the workload, not the other way around. We evaluate accelerator options on their merits — GPU memory, throughput, cost and framework compatibility — and select the appropriate hardware for a given workload, rather than assuming every workload belongs on the same platform.
Small language models
Use smaller models when the task doesn't require a larger one. SLMs are often the better choice when the task is narrow, latency and inference cost matter, volume is high, the model can be specialized, and predictable behavior is important — and serving them well is its own discipline.
Private & regional AI
Deploy AI where your organization needs it — on-premises, in a private cloud, on dedicated GPU capacity, or through regional cloud infrastructure. This is the same infrastructure expertise behind Custom Medical AI and Private Medical AI.
Private AI, without compromising the infrastructure.
Some workloads require more control over where models run, where data is processed and how infrastructure is managed. We design private deployments around the organization's security, privacy, data residency and operational requirements.
On-premises
Customer-controlled infrastructure.
Private cloud
Dedicated or isolated cloud environments.
Regional cloud
Infrastructure in the required geography — for example UK or India infrastructure, depending on the intended use case.
Dedicated GPU
Dedicated inference capacity for the organization.
Deployments are designed around organizational requirements and are subject to security and governance requirements — infrastructure location alone does not constitute compliance with any particular regulatory framework.
From model to production system.
A model that performs well in a notebook still has to survive real traffic, latency requirements, memory constraints and operational demands. We work across that gap.
A layered engineering problem.
Each layer is a separate decision — and each can be matched to the workload independently of the others.
User / application
API / model router
Inference layer
Model serving
vLLM · model routing
GPU / accelerator
Docker · Kubernetes · GPU scheduling
Storage / monitoring
Redis · observability
Any layer above can point at any of these deployment targets — the choice depends on the workload and the organization's requirements, not a fixed default.
"Not every workload needs the largest model or the most expensive GPU."
We match model size, accelerator, memory, throughput and serving strategy to the workload — to avoid paying for capacity that isn't actually needed.
Need a model adapted to your workflow?
We help organizations evaluate, customize and deploy AI models around their specific terminology, knowledge and workflow.
Powering specialized medical AI.
Our Medical AI platform brings specialized models into one workspace. For organizations with more specific requirements, the same infrastructure expertise supports customized and private deployments.
Infrastructure decisions should follow the workload.
Model fit
Choose compute based on model requirements.
Traffic fit
Design around actual throughput and concurrency.
Cost fit
Optimize infrastructure against expected usage.
Deployment fit
Match architecture to the organization's environment.
Region fit
Support regional infrastructure where requirements call for it.
Operational fit
Design for monitoring, scaling and reliability.
Have a model, workload or deployment requirement?
Tell us what you're trying to run. We'll help determine the appropriate model, serving architecture and infrastructure.