“Sovren does not chase every trend. We’re not trying to offer every type of recruiting and sourcing product for every type of situation and customer. We are laser focused on making the best parsing and semantic matching components, and making sure that those components are the leaders in every way.”

Our software runs in desktop applications, multi-tenant SaaS architectures, global server farms, and everything in between. It is fully multi-threaded and cost-effective to scale up and out.
The problem was that as bankers, Robert and Jeffrey had little experience in the staffing industry. They were stumbling along trying to use existing HR software products as the foundation for their business – but nothing worked well. Frustrated, they took a leap of faith and began developing custom software designed to address what they saw as a void in the market. This proved to be a fortuitous decision.
In 1997, Robert bought the business from Jeffrey and switched Sovren’s business model from a recruitment firm to an ATS provider.
The first version of the parser was based on optical character recognition (OCR), which would read paper resumes via fax machines and scanners. The product was well received, but as time and technology progressed, customers began moving away from the paper-based process.
As Sovren grew, Robert made a decision to focus on the features that made the first product a success. Rather than trying to be everything to every customer, Sovren decided to focus on what it did best: providing parsing and matching functionality. The decision was made to provide components rather than solutions such as applicant tracking systems.
Since that time, Sovren has not strayed from its roots. While the company’s technology continues to advance, its business model has not changed: “Be the premier provider of parsing and semantic matching components for use in recruitment worldwide.”
With such focus, it did not take long for the company to rise to the top. Today the best names in the industry are Sovren clients. In fact, Sovren’s Resume/CV Parser currently powers: