The Best Products Save You...

The Best Products Save You...

I was scrolling X on Sunday night and came across a post on Ramp 's account.

If you can't see, it is a picture from the book My Life and Work, written by Henry Ford in 1922. The main quote that's important to pull is the following:

Built to save you time and consequent money.

If you've ever been to a startup pitch night, you'll hear at least one investor say the famous line: "You either need to help companies make money or save money." In the cloud era, you save money with faster and easier-to-use technology, increasing team productivity.

So the saying shifted to describing how it's best to save companies time or money. Products need to make your work or process 10x faster or 10x cheaper.

But the products that do both? They have an impact.

At Artemis we want to build this type of product because we want data teams to see its value immediately.

How we save time

Automated inefficiency analysis: Artemis automatically scans your entire data stack and builds a knowledge graph of your warehouse, dbt models, BI models and more to identify optimization opportunities, saving you from manually reviewing each model.

Automated Resolution:?You can delegate tasks to Artemis, which will resolve the issue so you can quickly review, approve, and implement them. This allows you to focus on higher-value work rather than delegate engineering time.

Pull request generation: The platform creates pull requests for approved optimizations, eliminating the need for your team to code and submit changes manually.

How we save money

Optimizing inefficient models: The platform identifies and helps fix inefficient models, reducing runtime and associated costs. For example, one model's runtime was reduced from about 15 minutes to 4 minutes.

Simplify BI vs dbt models: Artemis identifies which models should be kept within your BI and which should be rewritten in dbt. Most teams duplicate models in both layers and increase costs and sources of truth.

We monitor your stack, find issues, and provide clear, actionable solutions to improve their data stack efficiency.











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