Languages & Frameworks
Python
PyTorch
TensorFlow
scikit-learn
Services > Artificial Intelligence & Machine Learning
AI that goes into production, not into a slide deck.
We take ownership of day-2 operations under contracted SLAs, then quietly remove the recurring problems.
— Is this right for you?
Recognise any of these?
If your organisation is facing any of these challenges, artificial intelligence & machine learning is likely the right next step.
01
PoCs that never ship
Months of AI proofs-of-concept that never reach production.
02
Data can't leave the tenancy
Sensitive data that cannot sit in a public LLM.
03
Inference costs climbing
Inference cost rising faster than the value it returns.
04
No deployment governance
No clear governance for who can deploy which model, on which data.
05
Knowledge buried in documents
Knowledge buried inside thousands of documents that employees can't find quickly.
— how we work
How we deliver results
We separate AI hype from AI ROI, then design for production from day one.
01
Assessment / Discovery
We map your high-value use cases against your data reality, regulatory exposure and team capability — and tell you which to pursue first, which to delay and which to drop.
02
Strategy / Planning
Solution design covering data pipelines, model choice, infrastructure (cloud or on-prem), governance and the human workflow that surrounds the model.
03
Implementation / Delivery
Data engineering, model development, fine-tuning or retrieval-augmented generation, integration with your applications and the MLOps backbone to ship, monitor and roll back safely.
04
Optimization / Support
Drift monitoring, retraining cycles, evaluation against business KPIs, inference cost optimization and continuous improvement of prompts and retrieval.
— What we deliver
Common use cases
Real delivery patterns across industries and complexity levels — not theoretical capability.
Customer-support copilot grounded in internal knowledge bases.
Document understanding and automation for claims, KYC and contracts.
Computer vision for quality control on production lines.
Demand forecasting and dynamic pricing.
Fraud and anomaly detection on transaction streams.
Private GenAI assistants for enterprise data with a full audit trail.
— Free resource
Take our guide with you.
Download our guide for Finance Directors and Heads of ERP sizing an S/4HANA migration.
— FREE WHITEPAPER
The enterprise AI readiness checklist.
Covers data readiness, governance, private-deployment patterns and how to scope a PoC that actually reaches production.
- How to score AI use cases for ROI before funding them
- Private deployment patterns for regulated data
- A governance checklist for model deployment
- What 'good' MLOps looks like at mid-market scale
S/4HANA Migration Sizing Guide 2026
PDF · CUBE Enterprise
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The right tools for the job, not a fixed stack.
We select technology to fit your requirements and existing estate — every layer engineered for speed, built for trust and designed to adapt.
LLM Tooling
LangChain
LangGraph
Hugging Face
Pinecone
Weaviate
pgvector
Cloud AI
AWS Bedrock
SageMaker
Azure OpenAI
Vertex AI
Data Platforms
Databricks
Snowflake
BigQuery
MLOps
MLflow
Kubeflow
Airflow
— SEctor experience
Industries we serve
Real delivery experience across regulated and high-growth sectors — not first-time builders.
Banking & Financial Services
Insurance
Retail & E-commerce
Manufacturing
Healthcare & Life Sciences
— What CUBE deliversNo over-the-wall handoffs
Honest about the limits of AI
Why organizations choose us
One CUBE, eight specialist companies
The reach of a group with the focus of a specialist AI team.
Production experience in regulated industries
We have taken AI into production inside regulated industries — not just demos.
Public and private cloud, both
Comfortable with both private/on-prem and public-cloud LLM stacks.
Security built in from day one
Tight integration with our cybersecurity arm CyberGlobal for risk and data-exposure review.
No over-the-wall handoffs
Engineers and data scientists sit in the same team.
Honest about the limits of AI
We decline use cases that will not pay back.
8–14 wks
GenAI PoC to production-ready pilot
4–6 mo
Predictive ML including data work
4
CUBE companies with dedicated AI practices
— Client stories
What our clients say
Discover how our clients have transformed their ideas into successful projects through collaboration, creativity, and solutions tailored to their needs.
"We had three failed AI pilots before this. CUBE's team got a claims-automation model into production in ten weeks." - Head of Data, national insurer
Outcome: 40% reduction in manual claims review time
"They were honest that not every use case was worth funding — that saved us from a costly mistake."
CTO, retail analytics company
— FAQ
Frequently asked questions
What is enterprise AI and machine learning?
Enterprise AI and machine learning means applying models, data pipelines and automation to real business workflows as governed production systems with monitoring and retraining — not one-off proofs of concept.
How do we use AI without leaking enterprise data?
Private deployments, retrieval-augmented generation against your own vector store and strict data classification. We design for 'no data leaves your tenancy' by default where the use case demands it.
What is the minimum data we need to start?
Less than people think for GenAI retrieval use cases; much more for predictive ML. We assess your data in the first week and tell you straight.
How long from PoC to production?
Typical GenAI assistants: 8 to 14 weeks to a production-ready pilot. Predictive ML: four to six months including data work.
Will the model still work in a year?
We design retraining and evaluation into the operating model so accuracy does not drift silently.
How much does an AI or machine learning project cost?
Cost depends on scope: a focused GenAI pilot typically reaches production in 8 to 14 weeks, while predictive ML with heavier data work runs four to six months. We quote after an initial use-case assessment, with fixed fee for defined phases and T&M for ongoing work.
— Explore more
Related services
DATA
Data Science
The forecasting and analytics work that often sits right alongside AI.
Learn moreCLOUD
Cloud, DevOps & Infrastructure
The platform foundation your AI workloads need to run reliably.
Learn moreSECURITY
Cybersecurity & Compliance
Risk and data-exposure review for every model before it goes live.
Learn moreGot a project you want to talk about?
No matter how complex the challenge or specific the need, we have the expertise, scale and flexibility to deliver. Let's build something exceptional together.
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