RL Environment for Data Teams

Make Data Agents Faster, Cheaper & More Accurate.

  • you wrote the skills
  • you wired the MCPs
  • your agents still guess

2 attempts/hour on prod100+ on Zingle

0%

lower compute cost

0x

faster data team

0%

lower TCO

0

models with drift

Trusted by 100+ data teams

  • Cisco
  • Ford
  • Xpressbees
  • PwC
  • ElevenLabs
  • BlackRock
  • Nextdoor

[ backed by ]

Accel · Nexus

[ from the backers of ]

  • Anthropic
  • Scale AI
  • Cursor
  • MinIO
  • Hasura
  • Postman

[ how it works ]

If the numbers don’t match, it goes again.

“I don’t want to use your agent. I want to use your agent in my agent.”

Fair. You bring the agent and the warehouse. We bring the environment and the verdict.

You bring the agent and the warehouse. We bring the environment and the verdict. didn’t match → goes again. matched → back to your agent.

you bring · the agent

Claude Code

agent

Codex

agent

Cursor

agent

Your own harness

harness

4.20%

whole customers

Snowflake

warehouse

Databricks

lakehouse

BigQuery

warehouse

Redshift

warehouse

Postgres

database

Spark

engine

dbt

transform

Airflow

orchestrator

you bring · the warehouse

Faster

100+ attempts an hour, not 2. The agent gets its reps in before lunch.

Cheaper

Every attempt runs in a sandbox: a 4.20% scale model of your warehouse, whole customers, joins intact. Never on prod.

More accurate

Row counts, sums, nulls and distributions, checked on every attempt. Nothing ships until they match.

2 attempts/hour on prod100+ on Zingle

[ how it changes the work ]

The agent does the reps.
Your team gets the bigger job.

Same people, same stack. What changes is what each of them gets to own.

Data engineers

You built the harness.
Now it has somewhere to run.

dbt, Airflow, GitHub, Spark.
Same stack. The loop is what changes.

  • Checking every agent PR by handReviewing the proof that comes attached
  • Your agent proposes. You verify. Every time.Your agent proves. You merge.
  • Two attempts an hour against prodA hundred, before lunch
  • Re-prompting until the SQL looks rightRe-running until the numbers match
  • The last check on every changeYou own the system it runs inside

[ proof ]

What the loop has already shipped.

8

days

Snowflake → Iceberg on OSS Spark

400 models, zero drift

Read

35%

compute removed

Compute removed, on a cron

110 PRs, zero drift

Read

31

days 1 PR

Event-level feature sets for ML

Eleven attempts, four hours, zero drift

Read

[ security ]

It runs in your VPC, and your keys stay yours.

SOC 2 Type II

VPC deploy

Allowlist scoping

Policy reproduction

Erasure propagation

Session traces

Your AI gateway

VAPT