For the complete documentation index, see llms.txt. This page is also available as Markdown.
Introduction
Make Kernel responsible for data quality. Grow confidently with data you trust.
Data accuracy is the foundation for growth. Territory planning, AI automations, and cross-functional workflows between go-to-market and finance all depend on one thing: knowing exactly which entities are in your market, how they relate to each other, and whether your systems reflect that accurately.
Before Kernel, our customers used to buy enrichment data, hire humans to fix it, and maybe try to plug the gaps with AI. The result was inaccurate data, stressed humans, and missed opportunities. Kernel solves that.
Most Account records have one or more data quality issues
What Kernel is
How it works:
Kernel is entity data guaranteed to be accurate. Kernel resolves every record in your system of record to a unique and persistent KERN ID. It then enriches the records with hierarchical linkages and firmographic data - sourced and verified by agents trained on hard edge cases. If you find an error, Kernel handles errors end to end, ensuring your systems are accurate at all times.
What that means in practice:
Hierarchies and firmographic data that reflects how you think about your market, not overly granular legal entities or overly simplified domain-based accounts.
Duplicates resolved and parent-child relationships corrected at scale.
Every change is risk-scored, auditable, and applied on your terms.
Why Kernel is different:
Unlike traditional data providers, Kernel combines the consistency and coverage of a database with the data accuracy from agentic research. Delivered in the systems where you work with enterprise-grade APIs, change management tools, and accuracy SLAs.
Imagine you had expert humans continuously fix and enrich every record. That's Kernel.
