How RevOS AI Empowers Data Engineers and Analysts
Hand the SQL grind to an AI coding agent that works against your real schema — dbt models, the Cube semantic layer, and ingestion, all on your data lake. You curate lineage, semantics, and policy; every change ships through your own Git and CI/CD.
An AI coding agent on your stack
Claude Code (or the agent of your choice) explores your lakehouse and writes dbt models and the semantic layer against your real schema — grounded, not guessing.
Your data stack as code
Sources, connections, and models are declarative YAML, reconciled with revos apply / pull / diff — versioned and reviewed like the rest of your application code.
Validated before it ships
dbt tests, primary-key/unique checks, and data contracts run in CI/CD. The agent works in an isolated environment and never touches production directly.
Free to start — 1 GB, no credit card. Or see how agentic data engineering works.
Direct the work. The agent builds it.
Stop hand-writing every dbt model and Cube overlay. RevOS gives an AI coding agent the harness it needs — your lineage, semantics, and policies — so it builds production-grade models against your real schema while you review the result.
- A dbt medallion project and a Cube semantic layer, scaffolded by
revos initand ready from minute one - Sources, connections, and models as declarative YAML —
revos apply/pull/diff, the same mental model as Terraform - Managed ingestion (ETL/ELT) so the agent works with live data, not a blank file
- Validation loops, data contracts, and CI/CD so a wrong model is caught at review, not in production
See the full agentic data engineering workflow
From revos init to a scheduled, production-grade data product — the deep dive on how the agent builds, validates, and ships, with a 60-second demo.
What stood out to me most was the focus on combining AI with a well-defined data framework rather than just code generation. That feels like a meaningful differentiator. For teams that need a unified data layer but don't yet have a dedicated data engineer, this could significantly accelerate implementation and reduce complexity while still allowing for human oversight where needed.
Deploy AI on a foundation you can trust
RevOS gives your AI and ML initiatives the clean, governed data they need to succeed — built by the agent, validated in CI/CD, and grounded in a semantic layer your tools and AI agents can query directly.
- Pre-built models for lead scoring, churn prediction, and deal forecasting
- A governed semantic layer your BI tools and AI agents can query without guessing
- Built-in monitoring, versioning, and governance for production data and AI
Ready to put an AI agent on your data stack?
Start free in an afternoon, or book a walkthrough with the team.
Free to start — 1 GB, no credit card. Built on your data lake, dbt, and Cube.
Frequently asked questions
- RevOS scaffolds a dbt + Cube project on your data lake and gives an AI coding agent like Claude Code the skills to operate it — it explores your lakehouse, writes the models and semantic layer against your real schema, and opens the change as a Git diff you review. See the full workflow on our agentic data engineering page.
- The agent works in an isolated development environment and never touches production directly. Changes reach production only through your CI/CD pipeline, where dbt tests, primary-key/unique checks, and data contracts run first — and production mutations keep a human in the loop.
- RevOS connects to major CRM systems (Salesforce, HubSpot), ERP platforms (NetSuite, SAP), marketing automation (Marketo, Pardot), customer success tools (Gainsight, ChurnZero), support systems (Zendesk, Intercom), product analytics (Mixpanel, Amplitude), and more. We also support custom integrations via API and webhooks.
- RevOS provides a semantic modeling layer that automatically handles data transformation, normalization, and enrichment. Data engineers can define custom transformations, business logic, and calculated fields that are consistently applied across all data sources. The platform supports both SQL-based transformations and visual data modeling.
- Yes, RevOS integrates seamlessly with major data warehouses (Snowflake, BigQuery, Redshift) and BI platforms (Power BI, Tableau, Looker). You can use RevOS as your semantic layer while continuing to leverage your existing analytics infrastructure. Data can be synced in real-time or on scheduled intervals.
- RevOS provides pre-built ML models for common revenue use cases (lead scoring, churn prediction, deal scoring) that you can deploy immediately. Data scientists can also build custom models using our API and Python SDK, leveraging the clean, unified data foundation. The platform handles feature engineering, model deployment, and inference at scale.
- RevOS includes automated data quality checks, validation rules, anomaly detection, and lineage tracking. You can set up data governance policies, access controls, and audit logs. The platform monitors data freshness, completeness, and accuracy, alerting your team to any issues before they impact downstream analytics or AI models.