AI Platforms

The foundation your AI scales on

We design and implement AI platform stacks across infrastructure, data, model serving, governance and self-service AI components.

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The business case for a real AI platform

AI is the fastest-moving field in IT history and a growing driver of the economy, but it is not a single solution a company adopts. It is a sprawling ecosystem of models, tools and touchpoints, and adoption is rarely governed as a whole. As usage rises, infrastructure demand rises faster, and cost rises with it, in most companies uncontrolled, quietly becoming a threat to profitability. Much of the token spend and much of the ROI lands in the userspace, where subsidised SaaS vendors settle in and create lock-in.

Without a platform strategy spanning the entire stack, from where models run, through routing, governance and developer tooling, to the data layer and the userspace, every new AI use case adds cost and dependency instead of advantage.

Left unaddressed, this pattern doesn’t stabilise, it compounds.

Every quarter without a platform strategy adds more cost, more lock-in and more use cases you’ll eventually have to retrofit.

The Challenge

AI adoption happens by accumulation, not by design

Most companies do not decide to adopt AI as a platform. They accumulate it. One team stands up a model, another wires in a vector store, a third signs a SaaS contract, and governance gets consulted only once production is already close. Each step is reasonable on its own. Together they produce a fragmented landscape with no shared foundation, and that fragmentation is where cost and speed both quietly break down.

Fragmented by design

Different teams end up moving independently and governance only steps in once things are already near production.

Cost grows, ownership doesn’t

Usage rises, infrastructure demand rises faster, and spend follows with no clear owner. Subsidised SaaS vendors capture much of the token spend and the ROI in the userspace, turning early convenience into lock-in.

Every use case rebuilds the basics

Without a governed data foundation, a shared model registry and a common deployment path, each initiative rebuilds its own plumbing. Teams spend their time on integration instead of outcomes, right when the business expects speed.

The market won’t wait

AI is the fastest-moving field in the history of IT, and new frontier models appear constantly. Without a coherent platform, adopting each shift means touching every use case individually, or falling behind.

The good news: you don’t have to rebuild everything

A platform foundation can absorb the use cases you already have and scale with the next ten. You just need the right layers, built in the right order.

Our Solution

One target architecture for every AI layer

We start with a structured assessment of your use case pipeline, data foundations, infrastructure landscape, regulatory environment and operating model, not a vendor reference architecture.

We clarify which platform capabilities your organisation actually needs, which deployment model fits your constraints, and whether the right path is managed cloud services, a lakehouse-centric platform, a private AI foundation or a hybrid architecture.

From there, we design the platform across the relevant layers:

  • GPU and cloud infrastructure
  • Kubernetes and compute orchestration
  • model-serving infrastructure
  • inference gateways
  • vector and feature stores
  • MLOps and LLMOps pipelines
  • observability for AI workloads
  • audit logging
  • PII filtering, guardrails
  • MCP gateways and self-service AI components

The goal is not to build every capability at once, but a platform foundation that supports current use cases and grows with the AI portfolio. Need a sizing for your on-premise AI infrastructure to escape the cost trap? We’ve got you covered.

We also clarify responsibilities across AI, data platform and cloud infrastructure teams: ownership of platform layers, architecture review processes, deployment standards, incident escalation paths and shared KPIs such as adoption, latency, cost per request, GPU utilisation and self-service usage.

We work across Microsoft Azure, Databricks lakehouse architectures and Broadcom VMware Private AI Foundation with NVIDIA. Where a dedicated platform isn’t yet justified, we recommend a managed-service setup first, designed to evolve as your AI portfolio grows.

Benefits

  • Assessment before architecture, always
  • One platform across every layer
  • Clear ownership across AI, data, cloud
Tech Deep Dive

How the platform layers fit together

An AI platform is a full-stack foundation for scalable AI delivery. Business-facing use cases, from Modern Workplace productivity AI and AI-assisted development to agentic AI, RAG, AIOps and conversational agents, depend on several technical layers working together.

At the core are the AI platform and data platform layers. The AI platform provides reusable capabilities such as model serving, MLOps, LLMOps, inference gateways, observability and self-service AI components. The data platform provides lakehouse, streaming, ETL/ELT and vector-store capabilities so models and applications can access governed, production-ready data.

These capabilities need a reliable compute foundation underneath. Containerisation with Kubernetes, Tanzu Kubernetes Grid, GPU scheduling and Run turns infrastructure into usable capacity for AI workloads. Virtualisation with VMware vSphere, vGPU or MIG profiles and NVIDIA AI Enterprise enables controlled GPU sharing, workload isolation and enterprise-grade AI runtime capabilities.

For private and sovereign AI platforms, Broadcom VMware Private AI Foundation with NVIDIA provides a reference pattern across these lower layers, combining VMware infrastructure, Kubernetes, GPU resources and NVIDIA AI Enterprise to support private model deployment inside the customer environment. This is particularly important when data residency, sovereignty, IP protection, or regulatory requirements prevent the use of public AI endpoints.

For hybrid AI platforms, the goal is to combine the cost efficiency of self-hosted models with the flexibility of the public cloud, essential in a field moving so fast that no one can reliably predict a platform’s requirements a few years out. Open-weight models running securely on-premises at no per-token cost are becoming increasingly capable, matching or beating frontier models on individual benchmarks. At sufficient utilisation, running these models on owned infrastructure delivers real savings, while public cloud endpoints stay available for peak demand, specialised tasks and the newest frontier models. The key is right-sizing: matching model size, hardware and software ecosystem to real workloads without overprovisioning compute that erodes utilisation and drives up cost.

Consulting runs across the stack: use case pipeline, target architecture, technology selection, capability roadmap and operating model. Security and governance span every layer too, including access control, tenant isolation, audit logging, cost transparency, model lifecycle controls and operational standards. The result is not a collection of tools, but a coherent platform foundation that lets AI use cases scale without every team rebuilding the same capabilities.

evoila's AI platform stack from infrastructure to consulting, framed by security and governance
Technical Advantages

What you get when the platform does the work

One target architecture

Infrastructure, Kubernetes, data services, model serving and observability are designed together, not stitched together later.

Reusable platform capabilities

Model serving, inference gateways, vector stores and guardrails become shared services, not one-off project components. CTA:

Reliable MLOps and LLMOps

Model registries, versioning, evaluation harnesses and rollback patterns move AI from notebooks into governed production.

Controlled GPU and inference costs

Quotas, routing and usage attribution make capacity and cost visible before they become a programme blocker.

Governance built in, not bolted on

Tenant isolation, audit logging, PII filtering and model lineage sit inside the platform layer from day one.

Cost efficiency without losing flexibility

Self-hosted open-weight models and public frontier endpoints are combined and right-sized against real utilisation.

Your partner of choice

Full-stack platform engineering meets applied AI

evoila understands the layers below AI, cloud infrastructure, VCF, GPUs, Kubernetes, networking, storage and operations, and the layers above it: Data platforms, MLOps, LLMOps, agents, RAG, governance and business-facing applications. As a Microsoft, Databricks and Broadcom Partner, we support cloud-native, hybrid and private AI platform patterns, including regulated and data-residency-sensitive environments. Our teams define the platform roadmap, build the technical foundation, clarify responsibilities between AI, data platform and cloud infrastructure teams, and support operating the platform after go-live.

Full-stack expertise

From GPUs and Kubernetes to MLOps and RAG, one team covers every layer of the stack.

Three-way partner status

Microsoft, Databricks and Broadcom Partner status covers cloud-native, hybrid and private AI patterns.

Built for regulated environments

Private and sovereign AI patterns for data-residency and IP-sensitive environments, not just theory.

From roadmap to operations

We define the platform roadmap, build it, and can operate it after go-live.

Technologies & Partner

The technology stack behind the platform

Microsoft Partner ecosystem
As a Microsoft Partner, evoila delivers AI platform solutions using Azure AI Foundry, Azure OpenAI, Azure ML, Azure Kubernetes Service, Azure Monitor, Microsoft security services and related Azure platform capabilities. Partner co-funding options may be available for qualifying engagements.

Broadcom Partner ecosystem
As a Broadcom Partner, evoila supports private and sovereign AI deployments with Broadcom VMware Private AI Foundation with NVIDIA. This includes VCF, vSphere, Tanzu Kubernetes Grid, GPU-enabled infrastructure, Aria Operations and private AI platform patterns for regulated or data-residency-sensitive environments.

Databricks Partner ecosystem
As a Databricks Partner, evoila designs and implements lakehouse-centric AI platforms using Databricks Mosaic AI, Agent Bricks, MLflow and Unity Catalog, supporting data-intensive AI workloads, model lifecycle management, evaluation and governance where the lakehouse is the centre of gravity.

NVIDIA and Dell AI infrastructure ecosystem
We work with NVIDIA GPU infrastructure and NVIDIA AI Enterprise components where private or high-performance AI workloads require GPU acceleration, model-serving performance and controlled resource partitioning through vGPU or MIG-style patterns. Paired with Dell’s validated AI infrastructure, servers, storage and reference designs engineered for GPU-dense workloads, this provides a proven hardware foundation for on-premises and hybrid AI platforms.

Model serving and inference infrastructure
vLLM, TGI, Triton Inference Server, Ray Serve, managed model endpoints, inference gateways, LLM routing and KV-cache management support efficient, governed and scalable model consumption across AI applications.

MLOps, LLMOps and orchestration
MLflow, Kubeflow, Airflow, CI/CD pipelines, model registries, evaluation harnesses, prompt versioning, regression testing and deployment automation turn AI workloads into maintainable production services.

Entry Points

Five ways to start, one platform to grow into

AI platform work can start small, but it should be planned against the full stack. We begin by understanding your use case pipeline, current infrastructure, data platform, operational maturity, regulatory constraints and internal team boundaries. From there, we define the right next step.

1 | AI platform assessment and target architecture

We start with a focused assessment of use cases, platform maturity, cloud versus private trade-offs, data foundations, GPU and inference requirements, operating model and governance needs. Where hybrid is on the table, we right-size on-premises capacity against expected utilisation and weigh open-weight against frontier models. The outcome is a target architecture and phased platform roadmap.

2 | Platform foundation build

Implementation of core platform capabilities: VCF and Kubernetes-based compute, model serving, vector and embedding services, inference gateways, MLOps and LLMOps pipelines, observability, audit logging and self-service templates.

3 | Private AI platform implementation

Design and build of sovereign or on-premise AI platform patterns using Broadcom VMware Private AI Foundation with NVIDIA, for environments where data residency, security or IP protection rule out public endpoints.

4 | MLOps and LLMOps enablement

Setup of model registries, deployment pipelines, evaluation harnesses, prompt and model versioning, drift detection, retraining workflows, LLM routing, guardrails and production observability.

5 | Hybrid platform foundation build

Implementation of core platform capabilities across on-premises and cloud: GPU-accelerated compute for self-hosted open-weight models, model serving, vector and embedding services, an inference gateway routing between local and frontier models, unified cost attribution and governance, MLOps and LLMOps pipelines, observability, audit logging and self-service templates.

References

Platforms we’ve already built

Financial services cooperative
AI platform enablement and roadmap

For a cooperative financial services network, evoila delivered an enablement programme covering MLOps, AI platforming, large language models and RAG architectures. The engagement created a shared understanding of platform capabilities, use case requirements and implementation prerequisites, forming the foundation for roadmap decisions on business-process transformation and platform investment.

Critical infrastructure
private AI platform for mission-critical operations

For environments where operational data cannot leave the premises, evoila delivered AI platform capabilities around Broadcom VCF infrastructure, GPU resources and AI workloads built on Broadcom VMware Private AI Foundation, supporting private AI operations, first-level incident response and audit-ready monitoring for mission-critical, data-residency-sensitive environments.

Industry
Graph-RAG knowledge platform on Azure

For an industrial customer, evoila provided strategic consulting, solution design and architecture for a Graph-RAG-based knowledge platform on Microsoft Azure, covering proof of concept, cost optimisation, technical design and production rollout planning. The customer’s internal wiki became an AI-powered knowledge base accessible through natural-language queries.

Stop patching. Start building a platform.

Move from fragmented AI stacks to a platform foundation your teams can trust, reuse and scale.

Let’s get in touch

Talk to someone who builds AI platforms, not slide deck.

Reach our Data & AI platform lead directly, no account managers in between.

Dominic Petrak

Dominic Petrak

Unit Lead – AI

FAQs

Commonly asked questions about AI Platforms & AI Strategy Workshop

An AI platform is the shared foundation for building, deploying and operating AI workloads. It includes compute, data services, model serving, MLOps, LLMOps, observability, governance, self-service components and the interfaces AI applications consume.

The decision depends on data residency, latency, integration depth, cost predictability, operating skills and regulatory constraints. We compare managed cloud, lakehouse-centric, hybrid and private AI patterns before recommending a target architecture.

Yes. We design private AI platform patterns using Broadcom VMware Private AI Foundation with NVIDIA where data residency, sovereignty or IP sensitivity rule out public endpoints, relevant for regulated and security-sensitive environments.

Not always. A single use case may be better served with managed services or a focused build. We assess whether a platform investment is justified now, or whether the architecture should simply keep future scaling options open.

We define clear boundaries between AI, data platform and cloud infrastructure teams, covering ownership of GPU infrastructure, model serving, vector stores, pipelines, use case design, governance requirements, incident response and internal platform SLAs.

MLOps and LLMOps make AI workloads maintainable after go-live: model and prompt versioning, deployment pipelines, evaluation, monitoring, drift detection, routing, guardrails and rollback patterns for production AI systems.

Yes. evoila can operate AI platform components after go-live, including Kubernetes, GPU resources, data services, model-serving infrastructure and AI workload observability, with the operating model depending on criticality, internal ownership and SLA needs.

Governance becomes a platform capability. Audit logging, access control, model lineage, PII filters, guardrails, risk classification hooks and incident evidence get built into shared services instead of added separately per AI application.