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Automating manual and repetitive data engineering tasks with AI
Datafold automates manual work in data engineering. We leverage agentic AI to automate both day-to-day tasks, such as testing and code reviews, and massive one-off projects, such as data platform code migrations. Companies from Perplexity to Disney use Datafold to unlock more value from their data by freeing up their data teams from manual work, accelerating developer velocity, and ensuring data quality.
Datafold offers an AI-powered platform to automate data engineering tasks, focusing on migrations, optimization, and data development. It provides an AI agents layer, a context/data knowledge graph, and data quality tooling to accelerate and validate data workflows.
The platform enables Migrate, AI-Driven Data Development, and Data Quality tooling. It uses specialized AI agents for migrations, optimization, and code reviews, backed by a Data Knowledge Graph that provides lineage, business logic, usage, ontology, and organizational knowledge. Data Diff and anomaly detection monitor data quality across sources, while single-tenant/VPC deployment and governed LLM inference ensure security. Pricing is contractually guaranteed based on objects, timeline, and quality, delivered as a service with translation, validation, human review, and automated data validation within pipelines.
Who it’s for: Data teams and data engineers responsible for migrations, data quality, and development in large-scale analytics environments
Hiring and growth mentions, active product development with beta/Data Knowledge Graph, case studies and customer quotes, pricing and demo requests indicate traction and ongoing commercialization
In the past five years, I’ve designed and built data platforms for three very different companies: Autodesk, Lyft & Phantom Auto, a mature public corporation, an exponentially growing marketplace, and a seed-stage startup at the time respectively. I've experienced first-hand the challenges of having poor data quality and observability in a data-driven environment and built multiple internal tools for data monitoring, data cataloging, and lineage.
AI agents for customer support.
AI Employees for the Enterprise
