Data pipelines & integration
Connect applications, databases, APIs and files through batch and streaming pipelines, with validation, retries and monitoring built into the flow.
Data Engineering and Analytics Services
Turn your data into an intelligence layer for advanced AI, real time business intelligence and self service analytics
From source to usable dataQuality checks enabled
Business systemsApplications & databases
Events & filesStreams, APIs & uploads
✓IngestCollect & reconcile
✓TransformClean & standardise
✓ValidateCheck & publish
Curated data platformWarehouse & lakehouse
Business-ready datasetsReportingAnalyticsAI applications
What we can build for you
From a single reporting pipeline to a shared enterprise data platform, we shape the work around your sources, users and business priorities.
Data warehouses & lakehouses
Design and build cloud data platforms on Snowflake, Databricks and AWS, with models organised around your reporting, analytics and AI needs.
Business intelligence & reporting
Bring business metrics, automated reports and interactive dashboards together so teams can work from consistent definitions and traceable data.
Real-time & self-service analytics
Make current data available through event streams, curated datasets and self-service tools, with access shaped around the people using it.
Data platform modernisation
Move legacy databases, reporting jobs and ETL workloads onto modern platforms, with reconciliation and a phased transition that protects business continuity.
Data quality & governance
Build quality checks, lineage, access controls and ownership into the platform so your teams understand where data comes from and how it can be used.
How we work
We start with the decisions your data needs to support. Our engineers use ART AI Stack™ to support defined development and validation tasks, with review and data controls throughout delivery.
Discovery & assessment
Map your sources, users and reporting needs. Assess data quality, access, dependencies and the workflows the platform needs to support.
Architecture & modelling
Agree the warehouse or lakehouse architecture, data models and processing patterns, including security, ownership and operating costs.
Pipeline development & validation
Build ingestion and transformation pipelines. Test completeness, accuracy and performance, then reconcile outputs against the agreed source data.
Deployment & operational handover
Release in controlled stages, configure monitoring and deliver reporting tools. Prepare documentation and training, with ongoing support agreed around your team.
Your business environment Connected by design
ApplicationsDataKnowledge
ART AI Stack™
Specialised agents · Coordinated workflows
PermissionsHuman approvalsAudit evidence
Explore ART AI Stack™
Bring specialised agents into pipeline development and validation, with shared context, engineering review and controls around your data.
Explore AI Accelerated EngineeringFrequently asked questions
Planning your data platform, delivery approach and ongoing ownership.
Can you work with our existing systems and data sources?
Yes. We start with the systems you already run, including applications, databases, APIs, files and existing reporting tools. We assess integration options, data quality and access constraints before recommending changes, so the work fits your environment and business priorities.
Do we need a new data platform, or can you improve the one we have?
You do not need to replace everything to make progress. We can improve individual pipelines, stabilise reporting or extend an existing warehouse. Where a broader change is needed, we agree a phased approach that addresses the most useful workloads first and manages dependencies along the way.
How do you choose between batch and real-time processing?
We look at how quickly the business needs the data and what that freshness is worth. Scheduled processing is often suitable for recurring reports, while operational decisions may need event-driven updates. The design balances freshness, reliability, complexity and cost for each use case.
How do you protect data quality and sensitive information?
We define validation rules, reconciliation checks and ownership with your team. Access controls, encryption and monitoring are designed around the data in scope, while lineage and documentation help people trace how it has changed. Security and retention requirements are agreed as part of the architecture.
How does ART AI Stack support data engineering?
ART AI Stack can support selected engineering tasks such as documenting sources, drafting transformations and preparing validation checks. Our engineers review the outputs and own the architecture and release decisions. Agent access and the use of sensitive data are configured around your policies and the agreed scope.
What happens after the platform goes live?
We plan operational ownership during delivery. Handover can include pipeline documentation, monitoring, runbooks and training for your team. Where continued support is needed, we agree responsibilities for failures, data quality issues, performance tuning and future changes before the service moves into operation.
Often paired with
What should your data
help you do?
Tell us about your systems, reporting needs and the decisions you want to support.