Your pipelines, built and run by agents. In your warehouse, in your name.
Connect a source and the agents take it from there. They read the schema, write the transformation logic, add quality checks, deploy the pipeline, and suggest the KPIs. What they build lands in your warehouse as ordinary tables and ordinary code you can read, change, or throw away.
Twenty-plus sources today. If yours is not here, ask — connectors are added on request during a pilot.
What the first day looks like.
You connect the source. Within minutes the schema, keys, and table relationships are back. Nobody has mapped anything by hand.
Agents propose the model: which tables, which joins, which columns are derived. You confirm or correct it. This is where a human catches the column that means something different from its name.
The pipeline is written, tested against the source, and deployed. Quality checks run on the first load. KPIs are suggested from the resulting tables.
It is scheduled, running, and under watch. Ask it a question, or hand the tables to your BI tool.
This is the typical day for one relational source or warehouse. Odd sources, legacy formats, and models that join five systems take longer. We will tell you which yours is before the pilot starts.
KPIs that fit your data and your industry.
Pick a vertical to see the kind of metrics the platform suggests from a real schema. Outcomes, not a fixed template.
Suggestions, not a template — every KPI is yours to keep, edit, or replace.
Only schema reaches the AI. Everything else is blocked.
The agents work from the shape of your data: column names, types, and structure. That is the only thing ever sent to a language model. Personal data, card data, and raw values never leave your perimeter. If a filter cannot confirm a request is safe, the request is dropped, not passed through.
No PII or PCI to a model
Personal and payment card data is filtered at the source boundary, before any request is formed.
Schema only
Column names, data types, and table structure are all the AI receives. No sample rows, no values.
Deny by default
Anything beyond schema metadata needs an explicit allow. Nothing slips through because someone forgot to block it.
From a connection string to a KPI list.
Discovery
Connect
Schema, keys, and relationships read straight from the source. Nothing hand-mapped.
Pipelines
Build
Transformation logic, quality checks, and scheduling, generated from the schema and deployed to your warehouse.
KPIs
Insight
Suggested from your actual tables and your industry. Yours to edit, rename, or delete.
Changes
After launch
Describe what should be different. The agent updates the pipeline and shows you the diff before anything runs.
The pipeline does not leave your sight when it goes live.
Monitoring attaches at deploy. Every pipeline built here shows up in one list with its last run, its current state, and the stages that ran. When the scheduled run does not happen, when output goes stale, or when a column changes at the source, it says so and names the stage.
Attached at deploy
No agent to install and nothing to wire up. A pipeline built here is watched from its first run.
The run that never ran
A missed schedule is caught on its own. Nothing else notices a job that simply did not start.
Named, not alerted at
Each incident names the stage that failed and what changed, and marks the KPIs downstream that are now stale.
See it on one of your sources.
Pick a database. We will connect it during the pilot and you will watch the pipeline get built.