We build the data and platform layer AI work depends on, including warehousing, pipelines, identity, and governance.
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With our AI data foundation offering, governance stays legible throughout; so you know who can read what, where it came from, and how long it’s kept.
We audit readiness case by case. Then we check API coverage, schema, freshness, lineage, and identity governance against the real source system, and build what’s missing.
You get:
• A ship/no-ship answer for the workflow you ask about
• The concrete blockers named and listed
• Missing pieces built afterward, from pipelines to the access layer
Metric definitions live in one version-controlled semantic layer, so a change to a definition is a reviewed change with an author attached. Reporting is built on top of that layer, and agents read the same definitions your analysts do.
You get:
• One shared number instead of three teams disputing revenue
• Changes to a metric’s definition are never anonymous
• No separate agent-only version of the numbers
We run data migrations and build ETL/ELT pipelines across AWS, GCP and Azure, or on-premise, moving your marketing, product, CRM and finance data into the warehouse model your AI and reporting depend on.
You get:
• Migrations and ETL/ELT pipelines built for your source systems
• New data sources folded into the same model as they come online
• Integration across AWS, GCP and Azure, or on-premise, on top of what you already run
We track token, pipeline and query consumption in one shared system of record, attribute cost by pipeline, model and consumer, and set partitioning and materialisation against how your queries actually run.
You get:
• Token, pipeline, and query consumption tracked in one shared system of record
• Cost broken down by pipeline, model, and consumer
• Storage and query setup tuned to how you actually query, reviewed on a set cadence
• Committed-use discounts negotiated where the spend justifies them