Podd is an autonomous AI engineer that connects to your raw databases, writes production-grade ETL pipelines, and maintains them. Zero headcount required.
Natively writes pipelines for
You don't need a team to spend weeks writing PySpark jobs. You just need an agent that understands your schema.
Give Podd a connection string. It automatically profiles your schema, detects foreign keys, maps nested JSON, and sets up the ingestion layer instantly.
# Connect your messy production DB
import podd
agent = podd.DataEngineer()
agent.ingest("postgres://prod-db:5432")
agent.map_topology()Tell Podd what metrics you need in plain English. It writes the exact SQL transformations, generates the `dbt` models, and pushes a PR to your repo.
If an upstream software engineer drops a column, Podd catches the downstream error, rewrites the SQL to fix the pipeline, and alerts you via Slack.
Your data never leaves your VPC. Podd agents deploy directly into your cloud environment via Docker, ensuring absolute security and compliance.
No drag-and-drop workflow builders. Just pure code execution. Podd gives you the power of a senior data engineer directly from your terminal.
Why we are building this
Data engineering is a solved problem. We just need to automate it.
Every modern company is forced to hire a data team. You pay multiple engineers massive salaries to perform the exact same tasks: extract data from an API, load it into a lakehouse, and write SQL to make a dashboard work.
It is tedious, repetitive, and entirely predictable. It is the perfect job for an AI agent.
Podd isn't a copilot that helps data engineers code faster. Podd is the engineer. We are building the infrastructure to make manual data engineering obsolete.