For individuals
AI & GenAI Training
Applied training in machine learning and generative AI engineering — building and evaluating real systems, not prompt tips.
Service detailAI, Data & Engineering
Data, AI & Analytics
AI projects fail on data quality far more often than on model choice. We start by making the data trustworthy — pipelines that are tested, lineage that is traceable, definitions that are agreed — and then build analytics and AI on top of a foundation that will hold.
Start a conversationFor enterprises
- Starting point
- Data quality, always
- Evaluation
- Built before the feature
- Governance
- Lineage and access by default
- Deployment
- Production, not pilot
What's included
What this engagement covers
Data platform engineering
Warehouses and lakehouses with tested transformations, documented models and lineage — built so an analyst can trust a number without tracing it by hand.
Pipeline development
Batch and streaming ingestion with data quality tests, schema-change handling and alerting that fires before the dashboard is wrong.
Business intelligence
Semantic layers and dashboards built on agreed definitions, so two teams asking the same question get the same answer.
Machine learning in production
Forecasting, classification, recommendation and anomaly detection — deployed with monitoring, retraining and drift detection rather than left in a notebook.
Generative AI systems
Retrieval-augmented assistants, document processing and content workflows, each with an evaluation harness and a defined human review path.
Data governance
Catalogues, lineage, access control, retention and privacy controls that satisfy audit without making the data unusable.
Process
How we approach data work
Written down so you can hold us to it.
Data and use-case assessment
What data exists, what condition it is in, and which questions the business actually needs answered. Frequently the highest-value finding is that a proposed AI use case needs data you do not yet collect.
Foundations first
Ingestion, modelling and quality testing for the domains in scope. Unglamorous, and the single largest determinant of whether everything downstream works.
Analytics layer
A semantic layer with agreed definitions, then dashboards and self-service access built on it rather than on ad-hoc queries.
Models and AI features
Baseline first — often a simple model or a rule — then increase sophistication only where it measurably beats the baseline.
Productionise and monitor
Deployment with drift detection, quality monitoring, cost tracking and retraining paths, plus a defined human escalation route for AI-assisted decisions.
Outcomes
What you get
- A tested, documented data platform with traceable lineage
- Agreed metric definitions in a semantic layer, not scattered across reports
- Data quality monitored with alerting ahead of consumers
- Models deployed with monitoring, drift detection and retraining paths
- AI features with evaluation suites that run on every change
- Governance that satisfies audit without blocking legitimate access
Detail
Platforms and tooling
Platforms
- Snowflake
- Databricks
- BigQuery
- Redshift
- Microsoft Fabric
- PostgreSQL
Pipelines
- dbt
- Airflow
- Dagster
- Kafka
- Spark
- Change data capture
- Great Expectations
Analytics
- Power BI
- Tableau
- Looker
- Semantic layers
- Metric stores
ML & AI
- Python
- scikit-learn
- PyTorch
- MLflow
- Vector databases
- Anthropic Claude
- Model evaluation
Questions
What people ask about this service
Including the ones with answers you might not want.
We want to start with an AI feature. Do we really need a data platform first?
Not always a full platform — but you do need the specific data the feature depends on to be reliable, accessible and understood. We scope the minimum foundation the use case actually requires rather than defaulting to a two-year platform programme. Sometimes that minimum is genuinely small.
How do you decide between classical ML and generative AI?
By the shape of the problem. Structured prediction on tabular data — churn, demand, fraud, pricing — is usually better served by gradient boosting: cheaper, faster, more accurate and far easier to explain. Generative models earn their place on unstructured language and content tasks. Choosing by problem rather than by trend is most of the value here.
How do you handle AI accuracy and hallucination?
With evaluation suites built before the feature ships, retrieval grounding with citation back to source, confidence thresholds that route uncertain cases to a human, and monitoring in production. We also refuse use cases where the error cost is high and no human review step is acceptable — that is a design constraint, not a technology gap.
Can you work with our existing warehouse?
Yes. Most engagements build on what is already there. Recommending a platform migration is a serious call and we make it only when the current platform genuinely cannot meet the requirement, not as a matter of preference.
Still unresolved? Ask us directly.
Related
Services that usually accompany this one
For enterprises
Cloud & Infrastructure
Cloud migration, platform engineering and cost optimisation — infrastructure that is reproducible, observable and affordable.
Service detailFor enterprises
Enterprise Software Development
Custom platforms and applications built to be maintained — tested, documented and handed over to your team.
Service detail
Next step
Talk about your data
Whether you are starting a platform or trying to get an AI pilot into production, we will give you a realistic read.