Expertise · AI-native
AI & Data consulting: from data to impact
We help organisations turn technological potential into real impact: making better use of their data, automating, making better decisions and developing new uses through responsible AI.
Our approach
Data is a strategic asset. To create value, it must be structured, made reliable and put to the right uses, with AI sized to the need.
We support business, IT and data leaders from strategy to execution: automation, personalisation, performance management, decision support, process optimisation and customer experience.
Our AI & Data expertise
01
Machine Learning
Use cases, data preparation, model selection and training, integration into your processes: reliable predictions and recommendations.
02
MLOps
Deployment, supervision, monitoring and governance: moving from experiments to production with robust, monitored models.
03
AI Product
Scoping, prioritisation, value proposition, adoption and impact measurement: useful, actionable AI-augmented products.
04
Data Engineering
Pipelines, quality, integration, governance and storage: reliable foundations for your data, AI and analytics projects.
Responsible
Useful, controlled, low-carbon
The goal is not to use AI everywhere, but where it creates real value, without unnecessary complexity.
Confidentiality & securityYour data stays under control, from design to operation.
Quality & explainabilityResults your teams can understand and verify.
Right-sizingThe right model for the right use, rather than the biggest one.
Digital sobrietyLeaner architectures and a measured footprint.
Our method
01
Understand
Business challenges, available data, technical constraints and goals.
02
Prioritise
The most relevant use cases by value, feasibility and impact.
03
Design
AI models, data pipelines, architectures, AI products or MLOps set-ups.
04
Industrialise
Release to production, monitoring, adoption and continuous improvement.
AI-native consultants
Technical expertise, business understanding and an advisory mindset across the whole lifecycle of your AI & Data projects.
Machine Learning Engineer
Data Engineer
MLOps Engineer
AI Product Manager
Data Analyst
AI Consultant
Data Product Owner
Data Architect
Turning data into better decisions, with just the right amount of computing.
Frequently asked questions
What is MLOps?
The set of practices for reliably deploying, monitoring and evolving AI models in production, just as you would for software.
Do you need a lot of data for an AI project?
Not necessarily. Relevant, good-quality data matters more than volume. Scoping lets us check what is available and sufficient.
How does an AI & Data project with Acrion get started?
With a conversation to understand your challenges, then a scoping phase that identifies and prioritises use cases before anything is built.
