Sandstone raises $30 million to streamline in-house legal workflows

Sandstone’s latest funding is a reminder that enterprise AI gets real when it disappears into the workflow. The startup raised $30 million in a Series A led by Lightspeed Venture Partners, betting that in-house legal teams will pay for software that routes messy intake, handles review and keeps approvals moving, instead of another general-purpose chatbot.

The company is focusing on a narrow, high-friction job: legal work that lands through Slack, email, Jira and other intake surfaces, then has to be triaged fast. Sandstone’s pitch is that AI should organize that flow, then build custom workflows around drafting, legal analysis and review. Its initial customer base is expected to be legal departments at small and mid-sized businesses, which is a useful signal for anyone building enterprise software at monday.com: buyers usually adopt AI when it removes a specific bottleneck they already feel every day.

Sandstone said the financing will expand access to its Legal Relationship Management platform for in-house legal teams. A separate report said the company’s revenue grew more than 40x in the prior 90 days and that it has already onboarded customers including Wayfair. The pace is striking, but the more durable lesson is simpler. Legal teams do not need more generic assistance. They need systems that understand intake, permissioning, documentation and the chain of review well enough to shorten the work without risking control.

That is why this story lands so cleanly for monday.com employees. monday.com says it serves more than 250,000 customers worldwide, and on May 6 it described itself as an AI work platform, with AI agents at the center of the product. Its legal product pages already market AI-powered automations for document review, research, task management, intake, case management and secure document handling. Sandstone’s rise reinforces that message: AI adoption is not happening at the chatbot layer, but at the workflow layer where process, context and compliance actually live.

For product managers, that means the winning feature is often not a flashy interface but a tighter fit between software and a specific job. For engineers, it means routing, permissions and data handling can matter as much as model quality. For sales teams, it is further proof that customers will fund AI when it cuts friction in a function they understand. Sandstone is not trying to be everywhere. It is trying to sit inside one of the most important workflows in the company, and that is exactly where enterprise AI is starting to win.