Data pipelines are broken.
We're fixing them.
Epiphany is an AI-powered ETL platform that turns a plain-English description into a fully-deployed, production-grade data pipeline — in under three minutes.
The Problem
Building a data pipeline still takes days. It shouldn't.
A data analyst spots an opportunity. They need a pipeline: pull from Postgres, clean the records, load to Snowflake, run nightly. Simple enough to describe in one sentence.
But actually building it? A data engineer writes Python, configures AWS, wires up Airflow, manages credentials across three systems. That's days — sometimes weeks.
The bottleneck isn't intelligence. It's translation: turning the analyst's clear intent into working infrastructure.
Our Answer
Natural language → production pipeline.
Epiphany's four-agent AI crew — Planner, Ingestion, Transformation, Orchestration — works in parallel to plan your pipeline, write connector-aware Python, generate an Airflow DAG, and deploy it to AWS Fargate.
You get a running, scheduled, production pipeline with retry logic and secure credential management. No YAML. No Terraform. No waiting.
→ Pipeline deployed in 2m 41s.
How we think
What Epiphany stands for
Plain English, Real Infrastructure
You describe the pipeline. Epiphany's agents plan it, write the code, and deploy it to AWS Fargate — no YAML, no Terraform, no waiting on an engineer.
Built for Speed
A data analyst shouldn't wait two weeks for a data engineer. Epiphany closes that gap — you get a production pipeline in the time it takes to drink your coffee.
Production-Grade by Default
Every generated pipeline comes with scheduling via Apache Airflow (MWAA), automatic retries, and credentials managed securely in AWS Secrets Manager.
Connector-Aware Intelligence
The AI agents understand the specifics of Postgres, Snowflake, S3, BigQuery, and more — generating idiomatic, optimised code for each target, not generic boilerplate.
Ready to build your first pipeline?
Start free. No credit card required. Your first deployed pipeline in under 3 minutes.