DataAgentsFrom a fragmented stack
to one governed loop.
DataAgents replaces the workflow, not just the dashboard. Five moves take you from authenticating a source to operating the loop in production.
Connect a source.
OAuth Stripe, Shopify, Postgres, anything in the catalog. The platform inspects the schema, picks the right grain, and lands the first rows in a governed layer within minutes.
- 552 connectors live · weekly drops
- Schema-aware sync · idempotent backfills
- No warehouse to provision
→ grain := "day"
→ backfill(window="90d")
→ governed.layer.land(rows=1.4M)
Model, the platform's job.
DataAgents proposes definitions for revenue, margin, churn, AOV, and the rest. You review them in the visual studio, tweak SQL inline, and pin them as the team's source of truth.
- Auto-generated metric definitions
- Visual studio editor with lineage
- Two-reviewer publish flow
metric contribution_margin {
numerator: net_revenue − refunds − fees − cogs
denominator: net_revenue
grain: "day"
owners: ["finance", "growth"]
}
Ask in plain English.
Operators ask in Slack, Teams, WhatsApp, email, the web app, or the API. Same memory, same definitions, same answer with the SQL plan and confidence signal attached.
- Six channels · one conversation
- SQL plan visible on every answer
- Threaded follow-ups
← sources · stripe.fees, meta.spend, shopify.orders
← cause · summer25 promo stacked w/ free shipping
← confidence · high
Deliver where it matters.
Push the same governed answer to Slack, an email digest, the web app, your reverse-ETL pipe, or your own product through the API. No manual reconciliation later.
- Slack · Teams · WhatsApp · email
- Web app · API · webhooks
- Reverse-ETL to your tools
Operate, with proof.
Pin the metrics that matter. The platform monitors the underlying pipelines, the freshness of the rows, the drift in model outputs, and the daily anomaly behaviour and explains why anything moved.
- Pipeline & ML deployment monitoring
- Anomaly alerts with root cause
- Audit log on every published change
Not another BI layer.
An autonomous workflow.
Configure, don't build.
OAuth a source. The platform models, pipes, monitors, and explains. The team you would have hired is now the platform you switch on.
No warehouse to provision.
The governed layer ships with the product. Bring your own warehouse if you want to. It works either way, with the same definitions.
Definitions improve in place.
The platform learns from corrections, yours and the cohort's. Bad metrics are not buried in a dashboard. They are flagged and re-proposed.
See it run on your stack.
Bring a real question.
We will plug into a sandbox of your data, ask the question your team is stuck on, and show what the loop looks like at the end.