Write pipelines 95% faster.
deepgreen AI researches the source, writes the code, and validates the pipeline.
A complete data engineering workspace.
Build, run, and maintain production pipelines with deepgreen AI.
No infrastructure to manage.
Build production pipelines in minutes.
Deliver reliable data for analytics and AI, without spending weeks building and fixing pipelines.
Built around the team’s conventions.
It applies engineering skills, reusable methods for how pipelines get built, and the team’s own instructions to write and validate Python and SQL.
Ready to run.
Deploy and schedule the pipeline in the same workspace. deepgreen handles credentials, compute, and execution.
700+ built-in connectors.
Connect existing systems and manage syncs alongside custom pipelines.
Applications
NetSuite 
Shopify 
Salesforce 
Stripe 
HubSpot 
Google Ads Databases
PostgreSQL MySQL SQL Server Files
Google Sheets CSV files PDF documents Events
Webhooks 
Kafka 
Segment
Kafka Segment Search all 700+ connectors
All connectors, grouped by source type.
Reliable data, with less maintenance.
deepgreen AI watches pipeline runs and SQL models, investigates failures, and prepares fixes for engineers to review.
Catch failures early.
deepgreen monitors pipeline runs and SQL models, so failures surface as they happen.
Investigate and repair.
deepgreen AI traces the cause through logs, data, and code, then prepares a fix in the pipeline or SQL model.
Review before applying.
Engineers review the proposed changes and decide what gets applied.
A unified stack for data engineering.
Ingest, transform, and orchestrate data in one platform.
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Less integration work.
Reduce the glue code and configuration needed to connect and maintain separate tools.
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End-to-end visibility.
Trace data from source to table and see which downstream steps depend on it.
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Shared context and permissions.
Engineers and deepgreen AI work with the same schemas, code, and access model.
Built to scale. Efficient to run.
Run daily syncs, large backfills, and demanding transformations efficiently as data grows.
Compute matched to the workload.
Scale resources to fit the size and complexity of each job.
Process only what changed.
Load new and updated records without reprocessing the full dataset.
No per-row ingestion fees.
Ingest large datasets without Monthly Active Row (MAR) charges.