AI Architecture & Infrastructure Built to Be Operated, Not Just Launched
A demo needs a model and an API key. A production system needs architecture, data pipelines, security, monitoring, cost control, and somewhere reliable to run. That engineering is the difficult part.
When you need this
The prototype works, and nobody can say what it will cost at volume, how it will fail, or who gets paged when it does.
Design the architecture, data pipelines, model integrations, security, monitoring, and infrastructure required to operate AI systems reliably.
Let's Get Started- Reference architecture
How models, data, applications, and infrastructure fit together, documented so your team can own it.
- Data pipelines
The ingestion, transformation, and storage that feed the system, built to run unattended.
- Security and access
Key management, tenant isolation, data-handling boundaries, and audit trails designed in rather than added afterwards.
- Monitoring and cost
Latency, quality, failure, and spend all measured, because with AI systems the token bill is an operating cost.
What we work in
The tools this work runs on, and what each one is here for.
AWSInference, queues and storage inside your own account.
AzureThe same, where your organisation runs on Microsoft.
KubernetesModel serving that scales with load and back down again.
TerraformEvery environment reproducible from version control.
MLflowModel registry and versioning, so a rollback is one step.
OllamaSelf-hosted open models where data cannot leave.
The rest of what we build
Most engagements touch more than one of these. Start wherever the problem is.
LLM Integration & AI Copilots
Integrate LLMs into existing applications and workflows to give teams and customers intelligent capabilities.
AI Workflow Automation
Automate repetitive business processes using AI, APIs, agents, and existing business systems.
RAG & Enterprise Knowledge
Build AI systems that securely retrieve and use your company's data, documents, and knowledge.
Custom AI Applications
Design and build AI-powered applications around your specific business requirements, data, workflows, and users.
AI Modernization
Take existing AI work, including prototypes, notebooks, and early integrations, and turn it into a reliable production system.
Start With a Focused Use Case
ArcusScale can help identify a practical opportunity and build a focused pilot before committing to a larger implementation.