LauraMac Mortgage Technology
Softmax Data partnered with LauraMac to turn labor-intensive due diligence into a governed, document-intelligence platform. End-to-end automation now prepares mortgage packages in seconds while preserving audit evidence and classification accuracy demanded by top due-diligence firms.
The Client
LauraMac provides technology and services for mortgage acquisition, due diligence, and surveillance. Serving 14 of the leading due diligence providers in the United States, the company delivers operational tooling that helps buyers, sellers, and investors complete deals with confidence.
Their platform supports more than 1,000 users across 100+ institutions and processes tens of thousands of loans. As the company scaled, the team needed AI-powered workflows to keep up with the volume and complexity of legacy loan tapes.
The Problem
Mortgage due-diligence teams received loan tapes that stretched 400–3,000 pages with 1003/1008 forms, appraisals, compliance attachments, and correspondence. Analysts were manually splitting and labeling documents, then re-keying information into downstream systems — a 40-minute task per loan even for experienced staff.
Rapid refinancing cycles amplified the pain: teams faced strict turn time guarantees and no margin for classification errors in regulated environments. LauraMac needed a secure, scalable AI solution to classify, extract, and audit every package with precision.
How Softmax Data Helped
We stood up a joint delivery pod with program leadership, senior ML engineers, and workflow specialists to reimagine LauraMac’s diligence pipeline. The team mapped every compliance checkpoint, instrumented existing manual steps, and designed a target operating model that kept analysts in control while the platform did the heavy lifting.
- Secure data pipelines move high-volume loan tapes into a compliant cloud environment with audit trails.
- Computer-vision processing denoises, rotates, crops, and enhances legacy scans so downstream models work with pristine inputs.
- Multimodal attention models classify, segment, and extract key fields across complex document types.
- Nightly self-training pipelines incorporate analyst feedback, maintaining and improving model quality automatically.
Softmax combined deep learning, document processing, and workflow automation so LauraMac’s analysts now review pre-organized packages while the system handles preparation, extraction, and evidence capture.
We also embedded change managers to document new procedures, built monitoring dashboards for executives, and established model risk management practices that satisfied customer audits.
Program Governance & Enablement
Human-in-the-Loop Controls
Analysts remain the final approvers. Every automated decision is surfaced with evidence, enabling rapid spot checks and preserving institutional knowledge.
Operational Runbooks
Softmax-authored playbooks describe ingestion, exception handling, and performance monitoring, making it easy for new team members to adopt the platform.
Executive Dashboards
Real-time reporting gives program sponsors visibility into accuracy, throughput, and savings so they can communicate value to customers and boards.
The Outcome
LauraMac transformed a manual bottleneck into a competitive differentiator. Analysts receive machine-prepared packages with transparent audit evidence and model-accuracy dashboards.
The organization redeployed diligence experts to higher-value exception work, accelerated client onboarding timelines, and now walks into renewal conversations armed with quantifiable proof of value.