Data & AI Infrastructure

Data Strategy and Architecture

Data strategy work translates business priorities into a concrete data architecture decision tree. Should you centralise in a warehouse, federate across a lakehouse, or run a data mesh? Which workloads belong in real-time pipelines vs nightly batch? How does your data layer support agentic AI workflows where models read AND write back? We design pragmatic architectures that suit your scale, team, and budget — not theoretical reference architectures from a vendor whitepaper. Our AI strategy work always feeds into the data architecture phase, ensuring every pipeline serves a business outcome rather than building data assets that nobody uses.

Pipelines and Integration

Your data lives across CRMs, ERPs, marketing platforms, finance systems, ticketing tools, and dozens of SaaS apps. We build the pipelines that bring it together reliably — using Fivetran or Airbyte for ingestion, dbt for transformation, and Airflow or Dagster for orchestration. Real-time pipelines stream events from Kafka or Kinesis into your warehouse and AI models. Reverse ETL pushes enriched data back into operational systems where teams already work. The AI automation we build relies on clean integrated data, and the agentic AI systems we deploy use the same pipelines as their connective tissue.

Analytics and Machine Learning

Analytics and ML capabilities turn data into decisions. We build dashboards in Looker, Tableau, Power BI, or Metabase tailored to the audience — exec dashboards for boards, operational reports for shift leaders, ad-hoc analysis tools for analysts. Machine learning models cover classification (customer segmentation, fraud scoring), regression (forecasting, pricing), clustering (cohort discovery), and recommendations. Every model ships with explainability built in, monitoring in production, and retraining pipelines so accuracy does not silently drift. The applied AI capability turns model outputs into specific business products like demand forecasts and predictive maintenance schedules.

Data Governance

Data governance is the unsexy work that determines whether your AI program survives an audit, a regulator visit, or a major incident. We help you establish clear data ownership, classification taxonomies, retention schedules, access policies, lineage tracking, and quality monitoring. UK GDPR, sector-specific rules (FCA SS1/23, NHS DSPT, ICO employment guidance), and the new EU AI Act all demand provable data governance. Our governance frameworks integrate with the wider AI governance work so model and data accountability live in the same operating model rather than being managed separately by different teams.

Common Data and AI Use Cases

Five clusters of work consistently deliver the strongest ROI for businesses building data foundations alongside AI capability.

• Customer data unification: identity resolution, CDP build-out, single customer view, GDPR-compliant first-party data activation.
• Operational analytics: real-time dashboards on inventory, throughput, SLAs, financial position, with alerting on threshold breaches.
• Predictive modelling: demand forecasting, attrition prediction, anomaly detection, propensity scoring across the customer base.
• Reverse ETL and activation: push warehouse-derived insights back to Salesforce, HubSpot, ad platforms, and customer service tools.
• ML platform and MLOps: feature stores, model registries, deployment pipelines, monitoring, retraining triggers.

Most clients get the strongest first win from customer unification or operational analytics — both create immediate business value while laying the foundation for downstream AI work. Our AI Readiness Assessment ranks these against your stack, team, and revenue priorities.