AI-Assisted Migration · 18 schedulers supported

Migrate any legacy batch scheduler to Apache Airflow.

BatchFoundry uses concept-mapping AI to convert Control-M, AutoSys, Automic, JAMS, Tidal — and 13 more — into production-grade Airflow DAGs. Zero rewrites. Full audit trail.

batchfoundry.ai — live migration preview

Control-M Automation API JSON

{
  "DAILY_ETL": {
    "Type": "Folder",
    "EXTRACT": {
      "Type": "Job:Command",
      "Command": "/etl/extract.sh",
      "eventsToAdd": {
        "Events": [{ "Event": "EXTRACT-OK" }]
      }
    },
    "LOAD": {
      "Type": "Job:Command",
      "Command": "/etl/load.sh",
      "eventsToWaitFor": {
        "Events": [{ "Event": "EXTRACT-OK" }]
      }
    }
  }
}

Apache Airflow DAG

from airflow.decorators import dag
from airflow.operators.bash import BashOperator
from pendulum import datetime

@dag(
    schedule="0 1 * * 1-5",
    start_date=datetime(2025, 1, 1),
    catchup=False,
)
def daily_etl():
    extract = BashOperator(
        task_id="extract",
        bash_command="/opt/etl/extract.sh",
    )
    load = BashOperator(
        task_id="load",
        bash_command="/opt/etl/load.sh",
    )
    extract >> load

daily_etl()

500B+

jobs analyzed

40+

Fortune 500 references

94%

avg migration accuracy, first pass

How we work

  • Deployment-neutral — Airflow on MWAA, Cloud Composer, Astronomer, or self-hosted
  • Full workload inventory before a single DAG is written
  • Concept-for-concept migration, not a generic rewrite
  • Parallel run period with rollback safety net
  • Knowledge transfer so your team owns the result

Not sure where to start?

We'll start with a free workload inventory call to scope the migration before any commitment.

Book a Free Scoping Call