Do you deploy into our account or yours?

Yours, always. Code, pipelines and infrastructure configuration live in your repositories and your cloud account from the first commit, so nothing is held hostage and billing is visible to you.

We have several models live with no governance. Where do we start?

With a registry and scheduled evaluation, which usually takes 2 to 3 weeks and immediately shows which models have drifted. Shadow deployment, approvals and lineage follow in the same engagement.

Can you work with our existing DevOps team?

Yes. We work inside your GitHub or GitLab organisation, follow your review process and use your CI. Where none of that exists yet, we set it up in a form your team can take over.

Cloud and MLOps

Infrastructure that keeps 5 models honest, deployed into the cloud account you already own

Terraform from the first deployment, an evaluation gate in CI, shadow deploys before live traffic, drift detection against rolling baselines and lineage an auditor can follow. On AWS and GCP by default, Azure where you already run it.

Book a free scoping call See the work

5: Production models under 1 control plane
3x: Faster safe promotion via shadow testing
99.9%: Production uptime across monitored deployments
100%: Data residency inside a client VPC

What we build

Cloud and MLOps, delivered as a working production system

Scoping, architecture, build, evaluation, deployment, monitoring and handover under 1 engagement, delivered by the engineer who designs it.

Cloud architecture and deployment

Production-ready AI infrastructure on AWS, Azure and GCP, placed in the region your regulator names, with model endpoints chosen to match. Deployed into your account with billing you can see.

Infrastructure as code

Terraform and CI/CD from the first deployment, so the environment can be rebuilt rather than remembered, and every change is reviewed in a pull request.

LLMOps control plane

Versioned prompt and model registry, scheduled evaluations, statistical drift detection, shadow and A/B deployment, and approval workflows with SOC 2 compliant lineage.

Monitoring and alerting

Latency, cost, quality and failure rates on 1 dashboard, with alerts routed to on-call before a user notices. Live at launch, not in a phase 2.

Cost control

Per-feature cost attribution, spend caps, semantic caching and routing between models so the inference bill is predictable and attributable.

Private and self-hosted deployments

Open-weight models on client-owned GPU nodes with vLLM, self-hosted vector stores and egress blocked at the network layer for environments where nothing may leave.

Architecture

The control plane we ship by default

Every prompt and model version lives in a registry. CI runs the golden evaluation set and blocks anything below threshold. Candidates run in shadow against production traffic before promotion. Monitoring feeds drift and cost alerts, and every output can be traced back to the prompt version, model version and retrieved context that produced it.

Where these projects usually fail

The 4 mistakes we get called in to fix, and what we do differently

Models tracked in a spreadsheet

A regulated FinTech was doing this across 5 models. A registry with lineage and approvals replaced it and made the next audit a formality.

Promotion without a shadow run

A new model version goes live against real traffic in shadow first. Only when its scores match or beat the incumbent does it take over.

Monitoring as phase 2

Drift, latency and cost alerts are live on launch day, because the first incident always arrives before phase 2 is funded.

Infrastructure that lives in 1 engineer’s head

Terraform and a runbook mean your team can rebuild the environment without us. That is the intended end state of every engagement.

Proof

Delivered systems in this practice area

Every figure comes from a system running in production for a real client.

Financial ServicesUSA

One governance layer for 5 production models in a regulated FinTech

1. Problem. 5 AI models were running in production with no shared visibility, no drift detection and no governance. Model behavior was tracked by hand in spreadsheets. In a regulated financial environment, that is an audit failure waiting to happen.

2. Solution. A single LLMOps control plane across all 5 models: scheduled automated evaluations, statistical drift detection against rolling baselines, shadow deployment and A/B testing before any version reaches live traffic, and a full model registry with SOC 2 compliant lineage and approval workflows.

MLflowAWS SageMakerArize PhoenixSOC 2

3. Impact

5: Production models unified under one platform
Auto: Drift detection replacing manual review
SOC 2: Audit-ready model lineage for compliance
3x: Faster safe promotion via shadow testing

Regulated EnterpriseEU

A private LLM deployment where no request ever leaves the customer environment

1. Problem. A regulated enterprise wanted a modern language model over its internal documents, under a contractual ban on sending that content to any third-party processor. Even zero-retention API terms did not clear the bar: nothing could leave the environment at all.

2. Solution. A fully private deployment inside the customer VPC: open-weight models on client-owned GPU nodes, self-hosted vector store, orchestration and observability, with egress to external model APIs blocked at the network layer. The audit answer to "where did this data go" is the same as "where does it live".

Open weightsvLLMSelf-hosted VPCQdrant

The client's name, sector and jurisdiction are under NDA. The residency figure is the measurement they cleared us to publish.

3. Impact

100%: Data residency inside the client VPC
0: Requests leaving the environment
Open: Weights, on client-owned hardware
Full: Audit trail held internally

All 13 case studies

Stack

What we build on, and why it is current

Model names and tooling are kept up to date with what we are shipping this quarter. The selection logic is simple: the option that passes your benchmark at the lowest cost wins, and that is tested rather than assumed.

AWS

BedrockSageMakerEKSLambdaS3CloudWatch

GCP

Vertex AICloud RunGKEBigQueryPub/Sub

Azure

Azure OpenAIAzure MLAKSAzure Functions

Platform

TerraformKubernetesDockerGitHub ActionsvLLM

Observability

MLflowArize PhoenixLangfuseGrafanaOpenTelemetry

Questions

Before you book a call

Generative AI DevelopmentLLM applications, RAG systems and copilots with a real backend Agentic AI and AutomationAgents, MCP, orchestration and n8n workflows with approval gates AI/ML EngineeringModel development, fine-tuning, forecasting and computer vision

AWS and GCP carry most of our production deployments and are where our deepest operational experience sits. Azure is used where a client already runs on it. We hold professional certificates on all 3, so the choice is yours rather than ours.

Your AI system should work, not just demo well

Book a free 30-minute scoping call. You leave it with a recommended approach, a realistic timeline and a cost range, and you talk to the engineer who would design the system.

Book a free scoping call Send project details

Written scoping summaryyours after the call

1Recommended approachRAG with hybrid retrieval, or classical ML, or plain software. Including the option of not building.
2What the system ownsThe steps it resolves alone, and the steps that stay with a person.
3First metric and baselineThe number it has to beat, measured on your own data before any build.
4Timeline and cost rangePhased, with the first scored result inside 14 days.

Written by the engineer who would build itFree, no obligation, yours to take to any vendor

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