Dagen is an intent‑driven data pipeline platform that lets users describe pipeline goals in natural language and automatically generates, deploys, and monitors the full data stack—from ingestion with 500+ connectors to dbt/Spark transformations and orchestration. Its specialist AI agents continuously detect schema drift, quality issues, and SLA violations, remediating problems autonomously while preserving institutional knowledge for future pipelines.
Funding
Funding not disclosed
Founders
Product
Problem
Building and maintaining data pipelines requires extensive manual effort, specialized engineering resources, and constant monitoring to handle schema changes, data quality issues, and infrastructure drift. Teams often face delays, errors, and loss of tribal knowledge when pipelines break or when engineers turnover.
Solution
Dagen provides an intent‑driven, agentic data pipeline platform that automates the full lifecycle from ingestion to business‑ready KPIs. Users declare the business purpose of a pipeline in natural language, and a hierarchy of specialist AI agents designs, builds, and deploys the required connectors, dbt/Spark transformations, data models, and orchestration logic. The platform continuously monitors schema drift, quality anomalies, SLA violations, and volume spikes, automatically remediating issues by rewriting code, adjusting ingestion settings, or reprocessing data without human intervention unless escalation is required. A tri‑layer memory system captures decisions, event logs, and institutional knowledge, enabling each new pipeline to benefit from prior learnings and preserving expertise across team changes. Dagen integrates with existing warehouses, Airflow, dbt, and major cloud services, allowing organizations to protect prior investments while moving toward fully autonomous data infrastructure.
Target Audience
Primary customers are data engineering and analytics teams in mid‑size to large enterprises that need to accelerate pipeline development, reduce manual maintenance, and ensure reliable, self‑healing data flows across multi‑cloud environments.
Features
- Intent declaration interface that translates plain‑language goals into pipeline architecture and execution plans
- Nine specialist AI agents covering ingestion (500+ Airbyte connectors), dbt model generation, metadata discovery, medallion architecture design, data cleansing, orchestration (Airflow integration), Spark job creation, synthetic test data generation, and external data enrichment
- Multi‑level autonomy (Guided, Semi‑Autonomous, Autonomous) that lets teams progress from human‑in‑the‑loop to fully self‑healing pipelines
- Autonomous monitoring for schema drift, data quality thresholds, SLA windows, and volume anomalies with automatic code remediation
- Tri‑layer memory: working memory for active tasks, episodic event log for lineage and root‑cause analysis, and persistent institutional knowledge base that compounds over time
- Full code transparency with versioned, auditable outputs stored in Git and compatible with existing CI/CD workflows
- Seamless integration with major data warehouses (Snowflake, BigQuery, Redshift, Databricks, etc.), orchestration tools, and BI platforms while keeping data within the customer’s infrastructure