AI Modernization From Notebook to Production System
Plenty of AI work already exists inside companies: prototypes, notebooks, and early integrations that proved the idea and then stalled. Modernization is the engineering that gets them running reliably.
When you need this
The proof of concept worked months ago, one person understands it, and it has never survived contact with real users.
Take existing AI work, including prototypes, notebooks, and early integrations, and turn it into a reliable production system.
Let's Get Started- An honest assessment
What is worth keeping, what needs rebuilding, and how far the existing work really is from production.
- Notebooks into services
Experimental code turned into deployable, testable, version-controlled software with an owner.
- Reliability and scale
Error handling, retries, rate limits, and the load testing that shows it holds under real use.
- Handover to your team
Documentation, runbooks, and the walkthrough that removes the single-person dependency.
What we work in
The tools this work runs on, and what each one is here for.
JupyterWhere the work usually starts, and what we turn into services.
PyTorchExisting models retrained, optimised and packaged.
Weights & BiasesExperiment history carried forward, not rebuilt.
DatabricksData and training pipelines on the platform you run.
GitHub ActionsTests and deployment for models, like any other code.
DockerOne reproducible image in place of a working laptop.
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 Architecture & Infrastructure
Design the architecture, data pipelines, model integrations, security, monitoring, and infrastructure required to operate AI systems reliably.
Start With a Focused Use Case
ArcusScale can help identify a practical opportunity and build a focused pilot before committing to a larger implementation.