Service
Data & AI
Decision-grade data foundations and AI deployments that hold up in regulated environments.
The problem we are hired for
Most enterprises are not short of dashboards. They are short of numbers anyone trusts, and their AI ambitions are stuck in a queue behind model-risk review.
We build the data foundations that make one number mean one thing everywhere, then deploy AI where it returns money and clears compliance.
How we approach it
Data foundations
Governed models and pipelines with lineage from source to decision.
Applied AI
Use cases picked for measurable return and a clear path through model-risk review.
Governance built in
Access, audit, and explainability designed into the first pipeline, not retrofitted.
How the work runs
Four movements with a decision gate after each. You always know where the engagement stands and what it has returned so far.
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Audit
Trace the numbers leadership already argues about back to their sources. Fix the definitions first.
Decision gate: one agreed definition for every number leadership uses.
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Model
A governed semantic layer and the pipelines to feed it, built on your existing stack.
Decision gate: a governed semantic layer live on your stack.
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Deploy
AI use cases shipped into real workflows with owners, controls, and rollback.
Decision gate: the first use case in production with an owner.
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Govern
Model monitoring and data quality gates that keep regulators and auditors satisfied.
Decision gate: monitoring in place that satisfies model-risk review.
Tooling we deploy
Snowflake
Databricks
Python
PostgreSQL
Apache Spark
TensorFlow
What it returns
- 19
- AI use cases in production
- 44%
- Faster monthly close, on average
- 0
- Open model-risk findings across clients
Related case study
Northgate Energy: field operations analytics
Unplanned outages down 27% in the first year of rollout.