Anvil Raises $10M for Data-First Workflows
Anvil, a startup focused on document-centric automation, has secured a $10M Series A. The company provides services like e-signatures, OCR, and PDF management with a "data-first" philosophy. The funding signals a market trend towards building workflows where data quality and structure are foundational, not an afterthought.
The funding round was co-led by Craft Ventures and Gradient Ventures, Google's AI-focused venture fund. Founded in 2018 by Ben Ogle and Mang Git Ng, Anvil aims to shift companies from traditional paperwork to structured "datawork," unlocking information previously stuck in PDFs. Anvil's "data-first" philosophy centers on treating documents as containers for structured data that can be accessed via APIs. This approach contrasts with traditional workflow automation that simply moves a file from point A to point B, instead focusing on the data entities themselves for better traceability and integration. For enterprise and regulated industry use, the platform includes features like SSO, detailed audit trails, and embeddable workflow builders for integration into existing applications. Insurtech companies Ascend and Vouch have used Anvil's technology to accelerate the launch of new insurance products by embedding its workflows directly into their platforms. This move toward structured data is a core tenet of the modern data stack, which is consolidating around platform ownership rather than a sprawl of individual tools. The 2026 outlook for data engineering emphasizes a stable stack as the foundation for AI-driven data products and embedded governance. The availability of structured, API-accessible data is critical for the growing use of AI copilots in data workflows. These AI assistants accelerate tasks like SQL writing, data exploration, and debugging by leveraging large language models, but depend on high-quality, reliable data to function effectively. In healthcare, data observability is crucial for ensuring the quality and security of information across complex systems like electronic medical records and connected devices. This capability helps maintain compliance with regulations like HIPAA by providing real-time monitoring of data flows to detect anomalies and potential security threats. Modern healthcare data architecture is evolving to break down data silos through unified pipelines and lakehouse models. This shift is necessary to handle the increasing volume of real-time data from sensors and automated inputs, making the data ready for AI applications and advanced analytics.
