Alpha7x, a US-based fintech provider specialising in mortgage operations automation, has secured provisional patent status for an AI-powered orchestration platform designed to handle high-cost, manual tasks in the residential mortgage sector. The system, now recognised as patent-pending by the US Patent and Trademark Office, promises to deliver significant efficiencies for mortgage lenders and servicers by […]
Alpha7x, a US-based fintech provider specialising in mortgage operations automation, has secured provisional patent status for an AI-powered orchestration platform designed to handle high-cost, manual tasks in the residential mortgage sector.
The system, now recognised as patent-pending by the US Patent and Trademark Office, promises to deliver significant efficiencies for mortgage lenders and servicers by executing compliance-critical tasks that existing systems cannot handle without manual intervention.
The patent application, System and Method for Data and Document Orchestration in Residential Mortgage Operations, centres on Alpha7x’s multi-agent execution engine—a stateless, domain-specific technology that automates processes across origination, servicing, and post-close functions.
The platform claims to act as a digital workforce, operating without the need for integration, data persistence or changes to existing IT systems.
Alpha7x, based in Florida, counts mortgage technology firms such as AppraisalVision among its partners.
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The platform has been piloted by several top-tier lenders and mortgage servicers in the US, though specific client names remain undisclosed. According to Alpha7x, these early adopters have reported a 70% reduction in manual labour per role and cost savings of 15–25% in selected workflows.
According to Alpha7x founder and CEO Jim Cutillo, what distinguishes the platform is not just the automation, but the architectural shift it represents.
Its AI agents are configured without code and require no system access, making them easy to deploy and compliant with major regulatory standards, including SOC 2, GLBA, FCRA and CCPA.
“Our clients don’t need to overhaul their infrastructure,” Cutillo added. “Alpha7x delivers plug-in automation that works across the tech stack, enhancing what’s there, not replacing it.”
Notably, each agent executes specific tasks such as post-close quality control or collateral review in sub-second timeframes, complete with full audit trails.
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Delivered as a managed service, Alpha7x says it charges clients on an outcome basis—only billing when a task is completed, verified, and compliant. For instance, a post-close QC task that traditionally costs £23.50 ($30) per loan is executed by Alpha7x for around £5.90 ($7.50), with the lender retaining the savings.
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“You’re not paying for software, you’re paying for execution,” Cutillo confirms “We only charge when tasks are completed, verified, and compliant.”
With general release expected in Q3 2025, Alpha7x is expanding its agent library and commercial footprint.
“This patent confirms that Alpha7x isn’t just automation, it’s a new execution layer. Our AI agents replace the human effort still required to bridge the gaps between POS (Point of Sale), LOS (Loan Origination System), servicing, and vendor systems, and they do it faster, cheaper, and more securely,” Cutillo said.
While Alpha7x’s approach appears to offer significant efficiencies, the increasing reliance on AI-driven automation in financial services also raises questions around transparency, explainability, and regulatory scrutiny, especially in areas such as data handling and decision-making.
Similar platforms, such as Candor Technology’s Loan Engineering System, have also introduced AI to streamline underwriting and mortgage workflows, highlighting a broader industry trend.
However, the long-term performance and auditability of these systems remain under close watch by both institutions and regulators.
For instance, the UK’s Financial Conduct Authority (FCA) and the US Consumer Financial Protection Bureau (CFPB) have both issued warnings about the use of opaque AI models in credit decisions, stressing the importance of fairness, accountability, and the ability to explain outcomes in automated lending processes.
