Make your data AI-ready out of the box.

Pipeline agents build 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.

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How pipeline agents go from raw data to production.

Discover

Discover your source data

Your ERP database has thousands of tables and cryptic column names. Agents take the real business examples from your spec (built with Spec Agents) and use them as anchors to locate the source tables behind every business entity in your data model. For ERPs like SAP, Oracle, D365, and NAV/BC, agents draw on a growing knowledge library of known system patterns, confirming what's expected and isolating only what's unique to your setup.

Transform

Transform into a universal format

Agents restructure your source data into a single universal format that works across ERP instances, finance systems, and operational tools. Multiple SAP implementations across departments, a mix of SAP and Oracle, standalone finance and ops tools alongside your ERP, all land in the same consistent structure. Every downstream step applies once, universally, instead of requiring per-system logic.

Harmonize

Harmonize and apply your business rules

Agents reconcile entities, products, and transactions across your source systems into a single harmonized view, then derive business events and compute metrics on the clean result. Every business rule is versioned and ordered, preserving full auditability as your logic evolves. Harmonize first, compute on clean data.

Prove

Prove it's correct

Your verified spec includes real business questions with known answers. Agents validate the pipeline's output against those known facts end to end. Every question gets a clear result: confirmed, failed with a recommendation for correction, or not yet confirmable with the sample data provided.

Discover your source data

Production-ready star schemas, data marts, and dbt code 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.

Ship your first agentic data pipeline.

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