definity Agentic Data Engineering Platform

definity is a Chicago-based data pipeline operations company led by co-founder and CEO Roy Daniel. It targets the cost, reliability and troubleshooting problems of Spark and lakehouse pipelines at enterprise scale, and announced a $12 million Series A in April 2026.

The platform embeds agents inside Spark or dbt drivers so they can act during a pipeline run instead of after it. Modules cover cost optimisation, in-motion observability, agentic root-cause analysis with lineage, and validation of code changes in CI. It supports Databricks, AWS EMR and GCP Dataproc, in cloud or on-premises.

• Cost optimisation: finds waste across compute, jobs, clusters and pipeline code with auto-tuning.
• In-motion observability: AI anomaly detection on data quality and pipeline health.
• Run pre-emption: can stop a run before issues spread downstream.
• Agentic troubleshooting: root-cause analysis using data and job lineage.
• Code change validation: simulates runs in staging within CI.
• Flexible deployment: cloud or on-premises with zero code changes.

A Vietnamese telco or bank running Spark batch jobs can cut cluster cost and catch stale inputs before reports are produced. A retailer on Databricks can validate pipeline changes in CI before production upgrades.

Site-reported results include 44% Spark cost reduction at Nexxen and 58% EMR cost reduction at a top-3 TV provider. Problems can be stopped before bad data spreads downstream. Installation is a one-time central setup with zero code changes and data stays in the customer environment.

As agentic AI systems depend on pipeline output, enterprises need data failures caught during execution and Spark compute costs under control. Vietnamese banks, telcos and retailers running Databricks or Spark face the same cost and reliability issues.

Chicago-based; $12 million Series A led by GreatPoint Ventures with Dynatrace, StageOne Ventures and Hyde Park Venture Partners participating (April 2026). SOC badge shown on the site.