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Building the data
foundations that
make AI work.

We build and modernise data estates on Microsoft Fabric and Databricks - one governed foundation that powers Power BI reporting, machine learning and AI. We fix the data first, then train your people to use it.

Specialists in
Power BI Microsoft Fabric Databricks Automation AI & Machine Learning

AI doesn't fail on ambition. It fails on foundations.

Our Point of View
Where AI Stalls

Everyone's racing to adopt AI. Most are building on data that can't carry it.

Predictive models, ML pipelines and LLMs are only as good as the data beneath them - and that is exactly where most AI stalls.

Fragmented data

Siloed ERP, CRM and spreadsheets that never reconcile - no single version of the truth.

No governance

No lineage, no access control, no confidence in which number is actually right.

No AI-ready structure

Nothing modelled or connected for AI to run on - reports built by hand, models stuck in Excel.

The Athos Approach

We ready the data - then unlock the AI.

Most of the value is in getting the foundation right. Here is the path from raw source systems to production AI.

01 · Ingest
Connect every source

ERP, CRM, files and APIs brought into one place.

02 · Medallion
Bronze, silver, gold

Clean, structured, trustworthy layers you can rely on.

03 · Semantic Layer
Modelled for meaning

A star schema with fast, readable measures.

04 · Govern
Lineage and access

Purview and Unity Catalog - security and the right access.

05 · Enable AI
Intelligence on ready data

Predictive analytics, ML pipelines and LLMs. We don't just train your models - we train your people.

Fix the data firstThat's around 70% of the work, and where AI projects succeed or fail.
What We Do

Three areas we go deep on.

One governed foundation. Reporting your teams trust. AI they can actually use.

AI & Advanced Analytics

Once the data is ready, the intelligence follows.

This is what the foundation is for - models grounded in your own data, built to run in production, not just to demo.

Predictive Analytics

Forecast demand, spot risk and see what's coming - models grounded in your own clean, connected data.

ML Pipelines

Production machine-learning pipelines on Fabric or Databricks - trained, deployed and monitored, not just prototyped.

LLMs & Copilot

Knowledge-grounded LLM assistants and Copilot workloads that answer from your governed data - safely.

Enablement Built In

We don't just train your models. We train your people.

Every engagement includes hands-on enablement after implementation - coaching your team on AI best practices, effective prompting and responsible use, alongside full documentation and walk-throughs. You own the capability, not just the platform.

AI best practices Effective prompting Responsible use Documentation & walk-throughs
Our Principles

What we stand for.

01 · Precision
Every element earns its place.

We don't add complexity for its own sake. Every table, measure, and pipeline serves a clear purpose - because clean, precise data is the only kind worth trusting.

02 · Substance
Outcomes over optics.

We measure success by what actually changes in your business - time saved, better decisions made, problems genuinely solved. Not by how impressive the slide deck looked.

03 · Ownership
We build it so you keep it.

Documentation, knowledge transfer, and honest walk-throughs are non-negotiable. When the engagement ends, your team should feel capable - not dependent.

Power BIMicrosoft FabricDatabricksDelta LakePower AutomateData ModellingApache SparkLakehouseDAXUnity CatalogETL PipelinesMachine LearningPredictive AnalyticsLLMs & CopilotAI EnablementOneLakeMLflowPower QueryReal-time AnalyticsGovernancePower BIMicrosoft FabricDatabricksDelta LakePower AutomateData ModellingApache SparkLakehouseDAXUnity CatalogETL PipelinesMachine LearningPredictive AnalyticsLLMs & CopilotAI EnablementOneLakeMLflowPower QueryReal-time AnalyticsGovernance

Is your data ready for the AI you want to build?

A free 30-minute call to look at it together. No commitment - just a clear view of what the foundation would take.