Your data optimizedfor AI use cases.

Moving from legacy systems to platforms like Snowflake, Databricks, BigQuery, or modern AWS/Azure data stacks is a major initiative, with strong focus on architecture, performance, and cost. We sequence the move so reporting never goes dark mid-migration, and size the platform so the bill tracks what you actually query rather than what you provisioned.

• —Snowflake, Databricks and BigQuery platforms
• —Cloud data stacks on AWS, Azure and GCP
• —Lakehouse and warehouse modernization
• —Workload performance and cost optimization

A practical plan for how data moves through your business: where it comes from, where it flows, who owns it, and what it has to support. We design the modelling, storage, and platform choices around the decisions and AI use cases they actually feed, so the architecture serves the roadmap rather than the other way round.

• —Data platform and warehouse architecture
• —Data maturity assessment and roadmap
• —Modelling, semantics and ownership
• —Data lake architecture modelling

Systems that get data from every product, database, and third-party tool into one reliable place. Batch or streaming, ingested incrementally, tested at each hop, and orchestrated so a failed run pages someone instead of silently poisoning tomorrow's dashboard.

• —ELT/ETL pipelines and orchestration
• —Streaming and change data capture
• —API, SaaS and legacy system integration
• —Migrations from legacy warehouses

Trustworthy data is a promise companies are built on and that simply can't fail. We put contracts, tests, and lineage around your critical tables, then wrap them in the access controls, retention rules, and audit trail your compliance obligations demand, so sensitive data stays protected and every number has a traceable origin.

• —Data contracts, testing and validation
• —Lineage, cataloguing and documentation
• —Access control, encryption and PII handling
• —GDPR, SOC 2 and regulatory readiness

The last mile: clean, modelled datasets your analysts can query in minutes, dashboards that answer a question at a glance, and the vector stores, embeddings, and feature pipelines your AI features rely on. Data your teams and your models can both consume without a translation layer in between.

• —Analytics models and semantic layers
• —Dashboards and self-serve reporting
• —Feature pipelines and vector stores
• —RAG-ready document and content pipelines