Accelerating a SaaS organization’s enterprise data accuracy through specialized AI automation

The Challenge

A private equity-backed SaaS organization faced a backlog of approximately 18,000 records requiring ownership validation to support core business operations. Inaccurate or incomplete ownership hierarchy data created direct financial risk by preventing the processing of required payment terms, while also increasing operational costs and raising data-sharing and security concerns. The scale of the backlog made manual verification impractical for the organization, as the process was slow, resource-intensive, and prone to human error. Highspring conducted an initial enterprise data assessment and confirmed that the existing approach could not meet business timelines or accuracy requirements.

The Solution

Highspring designed and delivered a custom agentic AI solution tailored specifically to the ownership validation workflow. Rather than relying on a general-purpose model, the solution used a network of specialized AI agents to independently evaluate each record, applying 10x redundancy to increase confidence in the results. Our team integrated the system directly into the client’s existing tools and workflows to support adoption without disrupting day-to-day operations. From initial design through deployment, Highspring implemented the solution within a 2.5-month timeframe to address the client’s immediate data readiness needs.

Our Impact

Highspring’s AI-enabled approach reduced the estimated time required to complete the ownership review from approximately 560 days of manual effort to 17 days, representing an 97% reduction in labor time. Automation significantly increased throughput, allowing the client to clear the backlog efficiently while improving data accuracy and consistency. Highspring oversaw internal adoption alongside agentic AI implementation, ensuring the company’s internal teams could prioritize core business processes while adapting to optimized data readiness best practices. The engagement demonstrated that specialized AI solutions can support large-scale, high-accuracy data readiness efforts that would otherwise be cost-prohibitive using manual methods, informing future data modernization initiatives.