AI Services

AI Engineering
From AI Experiment to Production System

AI can demonstrate value quickly. Turning that demonstration into a secure, reliable, scalable production system requires thoughtful engineering. ArcusScale helps companies design, build, and integrate AI systems that solve real business problems.

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Capabilities

Production AI systems built to ship

AI is easy to demonstrate. Building systems that are reliable, secure, integrated with your business, and ready for production is much harder.

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.

AI Modernization

Take existing AI work, including prototypes, notebooks, and early integrations, and turn it into a reliable production system.

Our AI Approach

Identify, prove, then scale

Each stage earns the next. Nothing goes to production because it demoed well.

  1. 1

    Identify

  2. 2

    Prototype

  3. 3

    Pilot

  4. 4

    Production

  5. 5

    Improve

Our AI Stack

What we build AI systems with

Model choice on merit, open standards where they exist, and infrastructure you already run. These are the tools most engagements reach for.

Models

Frontier and open models, chosen per task on quality, cost, latency and where your data is allowed to go.

  • Claude
  • OpenAI
  • Google Gemini
  • Llama
  • Mistral AI
  • Amazon Bedrock
  • Azure OpenAI

Orchestration & Agents

Chains, stateful agents and tool access that connect models to your systems and keep a person in the loop.

  • LangChain
  • LangGraph
  • Model Context Protocol
  • n8n

Retrieval & Search

Ingestion, embeddings, vector and hybrid search for RAG that answers from your own documents.

  • Qdrant
  • pgvector
  • Elasticsearch
  • Hugging Face

Data & MLOps

The pipelines, experiment tracking and model registry that take work from a notebook to a service.

  • Python
  • PyTorch
  • MLflow
  • Databricks
  • Snowflake
  • Apache Airflow

Deployment

Serving, scaling and monitoring on your cloud, or self-hosted where data cannot leave.

  • AWS
  • Azure
  • Kubernetes
  • Docker
  • Terraform
  • Ollama
Common Questions

What people ask us about AI

How does an AI engagement with ArcusScale start?

Most start with a focused pilot. We identify a practical use case, build a working version of it, and you see a real system before committing to anything larger. You don't need a massive AI project to get started.

Can ArcusScale integrate AI into an existing product?

Yes. Most of our AI work involves integrating AI into existing software rather than building from scratch. We assess your current architecture and implement AI capabilities that work within the technology stack you already run.

Which AI models does ArcusScale work with?

We work with major LLM providers and open-source models, and we pick the one that fits your use case rather than defaulting to a favorite. Model choice is an engineering decision driven by your requirements, data, and operating constraints.

What is a RAG system and do I need one?

RAG stands for Retrieval-Augmented Generation. It lets an AI system retrieve and use your own data rather than relying only on what the model learned during training. RAG can be useful when you want an AI system to work with your documents, products, or internal knowledge.

What makes a production AI system different from a demo?

AI is easy to demonstrate. A production system has to be reliable, secure, integrated with your business, and operable, which means architecture, data pipelines, model integrations, monitoring, and the infrastructure to run it. That engineering is the difficult part, and it is the part we do.

What industries does ArcusScale build AI for?

Our AI work spans healthcare and life sciences, pharmaceutical, oil and gas and energy, financial services, SaaS and technology, legal, manufacturing, and clean technology. The engineering is largely industry-agnostic; what changes is the data, the constraints, and the compliance requirements.

Start With a Focused Use Case

ArcusScale can help identify a practical opportunity and build a focused pilot before committing to a larger implementation.

Start with a Pilot