▣ Capability
AI & Data Analytics Platforms
The heavy lifting: lakehouses and AI platforms built to last.
We design and build enterprise data and AI platforms on Databricks, Snowflake, Microsoft Fabric, Google BigQuery / Vertex AI or AWS-native services: ingestion, lakehouse storage, governance catalogues, BI, feature and vector stores, model serving and GenAI, sized for many teams and years of growth. See “Platforms & cloud” below for how to choose.
Typical use cases
Challenge. Data lives in dozens of operational systems and spreadsheets; every AI project starts with months of data wrangling.
What we build. A governed lakehouse with medallion layers, a unified catalogue, CI/CD for pipelines and self-service access for analytics and AI teams.
Challenge. An on-prem warehouse or Hadoop cluster is expensive, slow and blocks GenAI use.
What we build. Phased migration to a cloud platform with automated code conversion, reconciliation testing and parallel runs until cut-over.
Challenge. BI, data science and GenAI teams each run their own stack with duplicated data.
What we build. One platform where the same governed data feeds dashboards, ML models, vector search and LLM applications.
Examples
Use cases with Data & AI Platforms.
Next step
Let's talk about your AI system.
A free 30-minute call with an Engagement Lead or AI Architect. You'll leave with a clearer view of options, risks and cost, whether or not we work together. Your case doesn't need to fit any box on this site; just tell us what you're facing.
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