Announcing Recce 1.0 with Cloud beta

🎉 Recce 1.0 is live. Help us spread the word!

Broken Metrics Break Trust

When data teams break metrics, they break trust. If stakeholders can’t trust the numbers, why would they trust the people behind them? These aren’t simple SQL mistakes, these are errors that mislead business decisions and create confusion and frustration among stakeholders.

Currently, data teams spend countless days—sometimes weeks—manually crafting complex SQL, spot-checking query results, and preparing one-off screenshots or spreadsheets for stakeholder validation. This manual effort is mirrored by stakeholders who must also spend extensive time verifying changes.

These ad-hoc investigations are exhausting and inefficient, leaving data teams little time to proactively improve their foundations and workflows or implement safeguards to prevent future issues.

Backed to Build the Future

We’re also thrilled to share that Recce has raised $4 million pre-seed round led by Heavybit, with participation from Vertex Ventures US, Hive Ventures, and angels Visionary, SVT Angels, Brighter Capital, Ventek Ventures, Scott Breitenother and Tim Chen of Essence VC.

This funding enables us to accelerate our mission: helping data teams ship with confidence by making validation transparent, explainable, and repeatable.

Recce: Visibility, Verifiability, Velocity

Recce gives data teams a faster, more reliable way to understand, review, and ship changes without all the guesswork or manual overhead.

Visibility

Recce helps data teams quickly understand the context of changes. Through intuitive column-level lineage visualization, single-environment explorations, and interactive custom queries, teams can rapidly explore impacts, experiment confidently, and validate hypotheses clearly before merging.

Verifiability

Once a change is clear, Recce ensures its impact is precisely understood. Breaking change analysis identifies exactly which downstream models are affected. Tools like Profile Diff, Value Diff, and Top-K Diff remove guesswork and manual checks. During exploration, the author validates their own work, capturing critical evidence into a structured checklist. Reviewers effortlessly follow the author’s logic through a single shared link, eliminating cumbersome screenshots and spreadsheets.

This checklist becomes a powerful artifact for collaboration, clearly distinguishing which aspects can be automated as tests and which require deeper human insight.

Velocity

When data work is visible and easy to verify, teams can move faster with confidence. Instead of manually re-checking the same work, they turn trusted insights into automated tests and checks for key business metrics. For edge cases or special requests, one-off checks are easy to create and follow.

At Recce, we believe data validation should work like good software development: organized, consistent, and thorough.

We know data isn’t like traditional software. Business context matters, and people are a key part of the process. That’s why Recce is built around three core pillars: Visibility, Verifiability, and Velocity. Together, they help data teams build trust and collaborate more effectively with stakeholders, all while delivering reliable, business-ready data.

Give Recce a try today, and transform how your team validates and communicates data changes!

Recce 1.0 Introduces Powerful Features and Recce Cloud beta

In Recce 1.0, we introduced Breaking Change Analysis and Column-Level Lineage to help you detect real changes and avoid false downstream impact, so you can see what truly matters. With the launch of Recce Cloud beta, you can now share the full validation context including lineage, queries, and checklists with anyone, making stakeholder collaboration effortless.

Recce 1.0 key features

• Breaking change analysis: Instantly identify precise downstream effects.
• Column-level lineage: Clearly visualize changes and their downstream impacts.
• Profile, value, & top-K diff to the column: Effortlessly highlight changes in data values, distributions, and segments.
• Interactive custom queries: Intuitive query interface for ad-hoc validation without complex SQL setups.
• Structured checklists & evidence collection: Clearly capture exploration insights into shareable validation checklists.

👉 Get Started

Introducing Recce Cloud (beta)

Built on top of Recce, our standalone SaaS platform, Recce Cloud, enables frictionless sharing and collaboration for data validation workflows.

• Share complete validation contexts instantly
  • Share your entire data-validation context including lineage diffs, custom query results, and structured checklists through one simple, interactive link.
  • Stakeholders no longer need screenshots, spreadsheets, or separate explanations. Every visual is clear and interactive.
• Flexible plans for every team
  • Community (Free): Ideal for quick collaboration or small teams exploring Recce, easily accessible, offering limited viewing time.
  • Team Plan (SaaS subscription): Perfect for regular collaboration, increased share duration, and extended features.
  • Enterprise Plan (Custom Pricing): Tailored for organizations with advanced needs, contact sales to customize.

👉 Learn more

See Recce in action: explore the Jaffle Shop story

👀 Explore How Jaffle Shop Rebuilt Stakeholder Confidence with Recce.

If you want to play with the live story, you can find it on Recce Cloud here.

Where we going

Recce 1.0 is just the beginning.

We started with dbt because it’s where data teams are already embracing software development practices—but the future of data delivery goes far beyond model tests and CI logs. As teams adopt AI-generated code, LLM pipelines, and layered semantic definitions, the need for a proof of correctness will only grow. And with it, the cost of a bad merge into production will only rise.

Recce was built to meet that moment.

Just as version control transformed how developers collaborate, we believe Recce will redefine how teams validate, communicate, and ship trustworthy changes on data and AI. That means:

• Expanding beyond dbt to support a broader range of data systems and ecosystems.
• Smarter automation that not only validates metrics, but understands logic, distinguishing what can be automated, what needs human judgment, and how to codify that knowledge over time.
• Human-in-the-loop reviews that combine business intuition with technical checks, making correctness explainable to stakeholders, not just engineers.
• Confidence as a service for the next era of software, where code and data are deeply intertwined, and every change must be understood in context.

We’re building for that future—where every team working with data and AI can move fast and be trusted.