Question 1

Why not just use Claude with connectors?

Accepted Answer

Connectors give AI access to your data. Sigma goes further by unifying and transforming data across systems, resolving inconsistencies, and maintaining the foundation needed for reliable AI-powered workflows. Rather than starting from disconnected sources each time, you build on a foundation designed for reuse.

Question 2

How is Sigma different from Snowflake or Databricks?

Accepted Answer

Snowflake and Databricks provide powerful data infrastructure. Sigma handles the engineering needed to put that infrastructure to work, from ingestion and transformation to ongoing maintenance and business workflows.

Question 3

What can I actually build with Sigma?

Accepted Answer

From customer intelligence and predictive models to operational automations and AI-powered workflows, Sigma helps turn complex data projects into working business solutions.

Question 4

Do I need a data engineering team?

Accepted Answer

No. Sigma handles the engineering behind your data workflows, so you can focus on what you want to accomplish rather than building and maintaining the infrastructure yourself.

Question 5

Does Sigma work with my existing infrastructure?

Accepted Answer

Yes. Sigma can work with your existing data infrastructure, including Snowflake and Databricks, or provide a fully managed lakehouse if you’re starting from scratch.

Question 6

Does Sigma get smarter over time?

Accepted Answer

Sigma learns from your business rules, corrections, and decisions while reusing the work it’s already completed. Over time, this helps it handle more complexity and deliver new workflows with less manual effort.

See Sigma on your data.

A short call with the founders. We’ll walk through Sigma, look at how your data is set up today, and map out where to start.