Trusted, governed data the business and the model can both consume
Cycle-time reductions of 40 to 70 percent on analytics requests
A platform finance can defend at audit
We open with a partner-led discovery - one week of interviews, data-room reads and stakeholder mapping. The output is a one-page thesis, not a deck.
Three to four weeks of solution design: architecture, control catalogue, build plan, success metrics, change posture. Reviewed with your model-risk, security and finance leads.
Joint Moweb-and-client pods deliver in 2-week iterations, with hard exit criteria for each release. Audit-pack evidence accumulates with every PR.
We co-run the system through the first three quarters. Hand-over is a transfer of practice, not just code.
Both, increasingly. The reference architecture we ship most often is Databricks lakehouse for raw / curated / consumption with Snowflake or Fabric serving the BI workload. The choice is driven by client landing zones, BI vendor and existing licensing.
Yes. Every data product we ship has an explicit contract, schema registry entry, SLA, owner and consumer registry. Breakages produce alerts, not surprises.
Translate ambition into an executable AI roadmap, scored against board-grade economics.
Migration, replatforming and FinOps for AI-ready cloud estates on AWS, Azure and GCP.