The First AI Agent Platform for Enterprise Data Teams

Chicory brings rigorous engineering and unprecedented accuracy to Agentic DataOps

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Chicory is the fastest way for enterprise data teams to create, deploy, and scale AI agents for

data cataloging

pipeline optimization

feature engineering

data debugging

data engineering

data mapping

Chicory is the fastest way for enterprise data teams to create, deploy, and scale AI agents for

data cataloging

pipeline optimization

feature engineering

data debugging

data engineering

data mapping

Features

Adaptive Data Understanding

Unify your enterprise knowledge by automatically surfacing deep relationships between code, data assets, and documents.

Obey Your Playbook

Design domain-specific agents that follow your runbooks, enforce policy, and automate complex workflows while maintaining consistency.

Tackle Hard Problems

Explore open ended questions deeply and over long horizons with multi-agent systems that dynamically plan, iterate with tools, persist memory, enforce guardrails, and self-monitor

Boost Data Discovery

Create rich contextual metadata that knows your business.

Improve Data Quality

Complete missing information and supply sample data.

Build AI Readiness

Understand and summarize your data models.

Enterprise Use Cases

See how leading organizations transform their data operations with integrated agents.

Data Harmonization

Automatically align schemas, formats and business definitions at ingest—so datasets are join-ready in hours, not days.

Solves:

  • Inconsistent data formats across sources
  • Schema drift causing pipeline failures
  • Extensive manual column mapping

Pipeline Optimization

Analyze your full DAG to surface cost and latency hotspots, then recommend configuration tweaks that free up budget.

Solves:

  • Slow-running, unoptimized queries
  • Resource-intensive pipeline operations
  • Expensive, inefficient DAG structures

Business Intelligence

Trace any KPI shift through data, code and dashboards—getting you root-cause answers in minutes.

Solves:

  • Delayed responses to business questions
  • Limited self-service analytics adoption
  • Complex queries requiring expert SQL

Feature Engineering

Scan your data estate to discover, score and prioritize the highest-impact variables for every ML model.

Solves:

  • Missed predictive relationships
  • Time-consuming feature validation
  • Manual feature identification bottlenecks

Data Debugging

Trigger runbooks on pipeline errors, gather logs, pinpoint failures, and suggest fixes in seconds.

Solves:

  • Recurring data quality issues
  • Reactive problem detection
  • Lengthy root cause analysis

Data Understanding

Ask “Where did this metric come from?” or “What does this column mean?” and get instant, auto-generated lineage and documentation.

Solves:

  • Difficulty locating relevant datasets
  • Time lost understanding data context
  • Poor documentation of data assets

Focus on Automations, Not Developing Agents

Manage data pipelines exactly the way you already do.

No Context Engineering.
No LLM Orchestration.
No Manual Tuning.‍Just define inputs and outputs for your specific task.

Input

Jira

Jira Ticket

A Jira ticket is assigned to Chicory to create a data view for a new marketing campaign.

Input

Pagerduty

Pagerduty Alert

A PagerDuty alert triggers Chicory to investigate SLA breach of data freshness

Input

Airflow DAG

Airflow DAG Run ID

A scheduled batch invokes Chicory  to optimize selected Airflow DAG run IDs.

Chicory

Agent

Output

DBT Labs

DBT Model

A dbt model (.sql + .yml) is created and ready for code review.

Output

Incident Report

Root Cause Analysis

An incident report is created with RCA and mitigations.

Output

Document

Performance Tuning Report

An optimization report is created with bottlenecks and  recommendations.