Build · AI orchestration platforms and production ML
In a federated enterprise, teams take a build-it-and-they-will-come approach to AI platforms. Over-engineered technical showcases constrain the people meant to use them, while shadow AI sprawls around the edges, and models stall at proof of concept or reach production with no monitoring.
We build central AI platforms that internal teams choose rather than work around, with orchestration, guardrails and MLOps built in — and we drive adoption on real use cases.
THE PROBLEM
The first wave of enterprise AI was pilots. The second wave is platforms, and most of them are being built the wrong way round.
A technically impressive central platform that the business teams find constraining. So they build around it, and the platform becomes another system nobody uses.
Vendor contracts, identity, guardrails, logging, cost controls, security review, a DPIA. Each business unit builds them again before it ships a single use case.
AI is being treated as a third-party dependency. DORA makes every model provider a register entry with audit rights and an exit plan, and the EU AI Act requires high-risk systems to keep logs for six months. AWS, Google and Microsoft all ship that logging switched off.
Promising models sit in notebooks. The ones that ship have no monitoring, so drift goes unnoticed until something breaks in front of a customer.
THE PROCESS
Platform programmes run from three to six months to first production use cases, then continue as the platform grows. The Map step is as much about the consumers as the technology.
Map
We find the real use cases and the real consumers: which teams are building what, on which vendors, with which data, and what they need the platform to do for them. We baseline existing ML assets, deployment paths and monitoring, and the current spend across providers.
You hold: A use-case backlog, a consumer map, a cost and risk baseline, and a build-or-buy recommendation.
Design
An API-first, modular architecture: serverless compute, containerised execution, a model registry and an orchestration layer, so teams can swap between Anthropic, OpenAI, Bedrock and Azure OpenAI without rewriting application logic. Enterprise identity and role-based access are enforced across every agent interaction, and MLOps pipelines are designed for training, validation, packaging and deployment.
You hold: A platform design that is value-led and experience-led, with guardrails and MLOps specified.
Build
We build the platform alongside your first two or three real use cases, so adoption is designed in rather than hoped for. Governed model access and MCP connectors go live first, then platform-hosted agents. Raidar evaluates every agent before and after release, and cost is attributed to each team from day one.
You hold: A production platform with its first teams live, and an audit trail you can show.
Run
Model governance and version control for datasets, features and weights. Cost and token governance across providers. Your platform team owns the roadmap, with enablement for the consuming teams so the platform keeps earning adoption.
You hold: A central platform that stops sprawl, measured adoption, and models whose value is tracked in the business.
THE OUTCOME
What good looks like:
PARTNERS AND ECOSYSTEM
We are an Anthropic Preferred Services Partner, an OpenAI Select Partner and hold the AWS Generative AI competency, with Azure OpenAI and Google Cloud delivery at depth.
That breadth is the point of an orchestration platform: we have built against every provider the platform needs to abstract, and no single one has a stake in our architecture advice. Snowflake and Databricks partnerships cover the ML platform side.
Preferred Services Partner.
One of six in Europe, and 100+ certified practitioners. One of the providers the orchestration layer abstracts, which is why none of them gets to shape the architecture.
Select Partner.
More than 750 certifications across our people, so swapping a model for cost, latency or capability is a decision made by practitioners rather than a vendor.
Generative AI competency.
Bedrock alongside Azure OpenAI and Google Cloud, so the registry and the orchestration layer are built against every route your teams might take.
Alongside them we build against AWS Bedrock, Azure OpenAI and Google Cloud.
BUILT IN-HOUSE, PROVEN ON CLIENT WORK
Evaluation you can trust
Our evaluation harness tests AI systems against real-world cases before and after they go live, so quality is measured, not hoped for.
For this work: Raidar is built into the platform, so every agent and model a team deploys is evaluated against real-world cases before release and monitored after. Quality becomes a platform guarantee rather than a per-team hope.
Coordinates the agents
An agentic software delivery harness that coordinates coding agents, tools and engineering workflows, with automated quality gates, evaluations and traceability built into production delivery.
For this work: Fleet is the first workload most platforms host, and it proves the rails on real engineering work from week one: identity, cost attribution, limits and audit. It is built to grow, so the agents your teams need next run the same way.
LIVE IN BUSINESSES LIKE YOURS
OAG
Automated data and analytics pipelines on Azure and Snowflake, with real-time monitoring, synthetic user testing and observability.
Read the case study
Dechert
A regulatory intelligence monolith re-engineered into Azure microservices with a retrieval-augmented AI layer, prompt-injection stress testing and bias evaluation.
Read the case study
This is a partner who has already earned trust in the hardest environments to operate in.
James Allen
Partnerships Lead for UKI and Northern Europe, Anthropic
THE FIRST STEP
A two-to-three-week Map gives you a use-case backlog, a map of who is building what on which vendor, and a cost and risk baseline across providers. Most organisations are surprised by how much AI they are already running, and what it costs.
