Primary Residential Mortgage achieves 75% operational efficiency gain with Touchless AI mortgage automation

Primary Residential Mortgage, Inc. (PRMI) operates one of the largest retail mortgage origination networks in the United States, with more than 150 branches and over 1,200 employees processing a high volume of residential loans. In retail mortgage lending, loan cycle time directly affects borrower experience and competitive positioning — yet PRMI's underwriters were spending up to three hours per loan on routine document review tasks. This manual, linear workflow created compounding bottlenecks from application submission through clear-to-close, constraining throughput as loan volumes increased. The inability to scale capacity without proportional headcount growth posed a direct threat to profitability and branch expansion goals.

PRMI deployed Tavant's TOUCHLESS® AI Mortgage Automation Platform across all 150+ branches nationwide. The platform applies machine learning and predictive analytics to automate pre-underwriting document analysis, intelligently classifying and validating loan files in real time before they reach an underwriter's desk. Rather than replacing underwriters, the system shifts them to an exception-based review model — only loans falling outside established risk parameters require manual attention. AI-powered decision logging and compliance-aligned audit trails address GSE traceability requirements, ensuring every automated decision remains explainable and reviewable. Tavant acted as a dedicated team extension throughout the rollout, providing change management and adoption support that proved essential to consistent deployment across a geographically distributed branch network.

The implementation delivered a 75% operational efficiency gain in central processing and underwriting — a result that fundamentally restructured PRMI's cost per funded loan. Underwriting time dropped from three hours to under one hour per loan, freeing substantial underwriter capacity without additional hiring. Additional outcomes included:

• Higher funded loan throughput across all branches as origination pipeline bottlenecks were eliminated
• Shorter loan cycle times, improving borrower experience and competitive positioning in the retail mortgage market
• Expanded staff capability, with loan officers able to manage more complex and diverse loan products as volume scaled across the full branch network

• Exception-based underwriting powered by ML allows skilled staff to focus exclusively on high-risk or complex loans, multiplying effective capacity without proportional headcount increases.
• Full AI decision auditability is non-negotiable in mortgage origination — GSE compliance requires every automated determination to be traceable, explainable, and audit-ready.
• Distributed branch rollouts require dedicated vendor change management support; technology deployment alone is insufficient for consistent adoption at scale.
• Addressing document validation at the pre-underwriting stage removes root-cause inefficiency rather than treating downstream symptoms of a slow origination pipeline.