Ship self-verifying enterprise data pipelines
Autopilot Pipeline builds a production-grade dbt project that transforms your source systems into star schemas and data marts shaped for analytics and AI, tested against business facts before you ship.
Trusted by leading organizations globally
Turning raw data into production dbt pipelines
Production-ready star schemas, data marts, and dbt project you can inspect, test, and deploy
Production-grade dbt code
Star schemas and marts at any granularity, in clean SQL you can read line by line. Finally, AI that shows its work.
Tests at every layer
Every pipeline ships with schema, business-rule, and reconciliation tests baked in from day one, not bolted on as an afterthought. Tests are generated alongside every model the agents produce.
Self-verifying agents
Hundreds of agents write, test, and cross-validate each other in real time. If the numbers don't match, agents flag exactly where the pipeline diverges and iterate until they do.
Yours to own, yours to deploy
Your pipeline lives as a standard dbt project in your repo with no proprietary runtime. Deploy on Snowflake, Databricks, Microsoft Fabric, or any dbt-compatible environment.
Universal format from day one
Your source data is transformed into a universal format before any business logic is applied. One pattern that works everywhere, not per-system spaghetti code.
A growing knowledge library
Agents get smarter with every ERP they encounter. Discovery patterns for SAP, Oracle, D365, NAV/BC, and others are encoded and reused, so each new pipeline starts from known patterns instead of a blank slate.
AI-readiness without the layer trap
Most enterprises chasing AI-readiness end up in the layer trap, stacking solutions on top of solutions:
- RAG for context
- An ontology for relationships
- A semantic layer for metrics
- A knowledge graph, a vector store, governance, orchestration, reconciliation…
One data model, four dimensions: business context, time, dimensions, and facts. Every row carries its own meaning, lineage, and relationships by design, consumable by BI dashboards and AI agents with zero translation layers. Built by pipeline agents, owned by you.
All your complex systems in one universal model
SAP, Oracle, Salesforce, Workday, or any enterprise system you're running. Maxa unifies them into a single model, and generates the star schemas and marts your team needs, at any level of granularity.
Data modeling code you can inspect
Trace data lineage and transformations at every level of granularity. As business and harmonization rules evolve, every change is captured; fully auditable, fully documented, traceable to the source.
We love the BI tools you love
Tableau, Power BI, Looker, Sigma... You can point your favorite BI tools at the same harmonized foundation. The dbt marts are the BI-ready layer.
- Star schemas, ready to chart. Clean dimensions and facts, no extra modeling layer to maintain.
- One definition of every metric. Revenue means the same thing in the dashboard, in the AI answer, and in the audit trail.
- No rip-and-replace. Your existing dashboards keep working.
Months of work become days
Embrace forever changing business needs and requests. Maxa Autopilot deploys AI agents on your behalf to model, engineer, and document production-grade data pipelines.
Ship your first agentic data pipeline
Frequently Asked Questions
Maxa extracts concrete figures from your inputs, like net revenue from the investor deck or customer count from the CFO transcript. Those numbers become the acceptance criteria. The pipeline isn't done until it returns them exactly: Q3 net revenue at $847M, intercompany rows excluded, fiscal period boundaries matched. "Correct" means the numbers match, not that the SQL ran without errors. Every model also ships with generated tests: row-count parity, key integrity, event structure, and source traceability. If something breaks silently, a test catches it.
No. You export your schemas and a sample of rows — up to 10,000 per table — into a local database inside your Maxa environment. All agent work runs against that copy. Maxa never connects to your warehouse, never queries production. Read-only is enforced at the database level. When the build is done, you download the dbt project and deploy it yourself, in your own warehouse.
Yes, with the right honesty about boundaries.
For standard modules (SAP FI/SD, Oracle Financials, D365 Finance), the knowledge library already knows where invoices live and which joins are safe. Discovery confirms what it expects; it doesn't search blind. For your customizations, discovery searches your actual data using the concrete entities from your spec, such as your customer IDs, your invoice numbers. It finds the right tables regardless of what they're named. Where it needs you: when a customization changes the logic, not just the table names. Maxa surfaces those moments for review rather than guessing.
Yes. It's a standard dbt project: models, tests, sources. Download it, put it in version control, run it yourself. No dependency on Maxa's infrastructure to execute. Add models. Modify existing ones. ref Maxa-generated models the same way you'd ref anything else. Readable SQL you can open, understand, and change.
An LLM writes plausible SQL. It has no way to know if the output is correct.
Maxa runs the model against your data, checks the result against the numbers from your spec, and iterates until the gap closes. It finds the $16M discrepancy, fixes the FX rule, runs again until the number is right, rather than generate-and-hope. The other difference is structure. An LLM writes you a query. Maxa builds a layered pipeline where every business rule is versioned and auditable. The difference between SQL that works today and a pipeline that's still maintainable in a year.
Companies merge, product lines get retired, the CFO changes how FX is calculated. It happens! Harmonization rules in your pipeline are versioned. Update a rule, and Maxa regenerates the affected layer while preserving the previous version with a timestamp for auditability. Re-run validation against your original business facts to confirm the rest of the pipeline still holds.
The spec phase (requirements extraction, stakeholder alignment) compresses to a day or two once you have the materials. A single-source mart: in production within a week. Multi-ERP harmonization across SAP, Salesforce, and Workday: longer, but weeks, not months. The first project is always slower. The second is much faster since the library already knows your systems.
Yes. Maxa's models live in a separate folder inside your dbt project and your existing models ref them like anything else. If you have models that overlap with what Maxa generates, migrate gradually, keep them in parallel, or leave them alone. Maxa is a harmonized foundation layer your existing work builds on top of.