Ontology-Powered AI Agents
for unprecedented accuracy and reliability

Unlock unprecedented intelligence from your enterprise data ecosystem with Chicory's native understanding of your data models.

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

data mapping
pipeline optimization
feature engineering
data debugging
data engineering
data cataloging

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

Enterprise Use Cases

See how leading organizations transform their data operations with intelligentand deep analytics.

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