Data Validation Startup Recce Raises $4M for AI Code Reviews

The startup tackles a growing problem: As companies build more AI systems, checking the data that feeds them becomes crucial. One wrong dataset can throw off an entire AI model.

"Most code reviews will become data reviews as data correctness becomes key for success in AI," says CL Kao, Recce's founder and CEO. Kao knows what he's talking about - he built SVK, an early version control system that companies like Apple and Ubisoft used before Git came along.

Recce's tools plug into existing data workflows and let teams compare datasets, check results, and track changes. Think of it as quality control for data pipelines. The company's open-source version already sees 3,600 weekly downloads on GitHub.

The Philadelphia Inquirer uses Recce to check data across 50+ systems. "It's helped us move faster without compromising data integrity," says Brian Waligorski, their lead data engineer.

Today's launch adds new features to catch problems earlier:

• Analysis tools that show how changes affect downstream systems
• Side-by-side comparisons of production and test data
• Checklists teams can share and reuse

Heavybit, which led the funding round, sees this as perfect timing. "Data pipelines are the new secret sauce for companies building AI," says Jesse Robbins, Heavybit's general partner who joined Recce's board.

Other investors include Vertex Ventures US and Hive Ventures. Brian Behlendorf, who helped create Apache, also joined the board. He points out that AI adds randomness to software development, making data testing vital.

The money will help Recce build its cloud platform, which launches in private beta today. The platform lets teams:

• Share data validation results
• Link checks to GitHub workflows
• Use a free tier to start

This matters because AI systems are only as good as their data. Wrong data means wrong results, and catching errors early saves time and prevents problems.

Companies already use tools like dbt to handle data analytics. But as AI grows, they need better ways to check the data itself. Recce fills this gap by bringing standard software testing practices to data workflows.

The timing fits a broader trend. As AI becomes standard, unique datasets give companies an edge. But that only works if the data stays accurate and clean.

Kao will speak about these challenges at the Data Council conference in Oakland on April 24. The company also opened applications for early access to its cloud platform.

Why this matters:

• Data mistakes cost more in the AI era. When machines learn from bad data, they make bad decisions at scale. Tools like Recce help catch problems before they spread.
• We're moving from an era where code quality mattered most to one where data quality drives success. Companies that catch this shift early will have an edge.

Read on, my dear:

• Data Recce