Databricks work that reaches production.

Migrations to Databricks

For Data platform leads with a funded initiative

Off Snowflake, Synapse, Teradata and SQL Server, onto Lakehouse and Lakebase, with a cutover you can reverse.

Data engineering

For Data platform leads whose pipelines break more often than they ship

Pipelines that hold, tables people trust, and a bill that stops surprising you.

Data science & AI

For Leaders whose AI pilot works in a demo and nowhere else

Context stores, memory, retrieval and governed agents that survive production.

AI/BI & Genie dashboards

For Analytics leads whose stakeholders still export everything to a spreadsheet

Genie answers a business question in English, and the answer holds up when somebody checks it against finance.

Machine learning

For Data science leads whose best model is still in a notebook

Models that reach an endpoint, get retrained on a schedule, and can be rolled back by somebody who was not there.

Data & AI governance

For Platform and security leads who have to defend the setup to someone else

Unity Catalog designed so grants hold, lineage survives a refactor, and an agent inherits permissions instead of routing around them.

Forward-deployed teams

For CTOs and chief data officers with a problem nobody has scoped yet

Product, design and engineering people who sit inside your business, find the real problem, and ship it.

Product development

For Founders and product owners building a product

Full product delivery: multi-tenant architecture, operator consoles and the data layer under them. On Azure, AWS, Google Cloud or Cloudflare, and as a Databricks App where the product belongs next to the lakehouse.

APIs & durable systems

For Engineering leads with reliability pain

Long-running operations that survive restarts and partial failure. Temporal under the lakehouse jobs, agent runs and approvals that must not half-complete.