SBA Loans HQ: The AI-Powered Engine for High-Velocity Lending
Architecting a centralized FinTech ecosystem that drove 25% revenue growth and slashed operational overhead by 60%.
Role: Full-Stack Developer & Team Lead
Role: Full-Stack Developer & Team Lead
Industry: FinTech / SBA Loan Consulting
Location: United States
Engagement Type: End-to-end product development
The Mission
SBA Loans HQ is a premier U.S. consulting firm specializing in business acquisition financing. The goal was to dismantle a fragmented, email-reliant workflow and replace it with a production-grade, multi-role platform. As Team Lead, I spearheaded the design and delivery of this system, transforming a manual "broker-and-spreadsheets" operation into a scalable, AI-driven FinTech powerhouse.
The Roadblocks
The high-stakes world of SBA lending was being throttled by legacy processes:
The Email Trap: Brokers were buried in fragmented threads, leading to inconsistent document submissions and security risks.
Operational Blind Spots: There was zero centralized visibility into the deal pipeline or loan progress.
Manual Bottlenecks: Constant follow-ups and manual data entry created massive delays in the loan lifecycle.
Tool Fatigue: The business was over-reliant on expensive, disconnected third-party SaaS tools (Zapier, Copper, etc.) that didn't talk to each other.
The "Intelligence" Layer: AI & Automation
To move beyond a simple CRUD app, I integrated a sophisticated AI layer to handle the heavy lifting of document processing and lead qualification:
AI Integration: Implemented AI-driven document summarization, automated file renaming, and intelligent lead scoring to help brokers prioritize high-value deals instantly.
AWS Textract OCR: Engineered automated data extraction from complex financial PDFs, turning raw images into structured, actionable data for faster underwriting.
Event-Driven Automation: Replaced third-party connectors with a custom-built backend automation engine that triggers stage-based document bundles and outbound communication via Postmark.
Core Platform Features
We delivered a multi-tenant, white-label-ready architecture that supports four distinct user experiences (Partner, Borrower, Lender, and External Requestee).
Intelligent Deal Pipeline: A dynamic Kanban and table-based system that enforces structured workflows across every stage of the SBA loan lifecycle.
Automated Document Vault: A secure, S3-backed management system where document requirements are automatically bundled and requested based on the current loan stage.
Integrated E-Signatures: Full DocuSeal integration allowing borrowers to sign critical documents directly within the portal, optimized for both desktop and mobile.
Real-Time Deal Chat: A threaded, per-deal messaging system that keeps brokers, borrowers, and lenders aligned without a single email.
Advanced Analytics Dashboard: A high-level operational view for "Super Admins" featuring ApexCharts for tracking lead distribution, partner performance, and bottleneck identification.
The Solution & Leadership Impact
Beyond the code, I led the transition from service-based chaos to product-focused reliability.
Architectural Strategy: Migrated all document storage and process automation away from third-party dependencies into a native, secure AWS infrastructure.
Team Orchestration: Coordinated between stakeholders and engineering teams to ensure business logic was perfectly translated into technical features.
Observability: Built a custom database-backed error logging system that allows the team to debug production issues with full user context and metadata.
The Finish Line: Business Outcomes
The launch of the SBA Loans HQ Portal fundamentally changed the company's trajectory:
25% Increase in annual revenue growth.
60% Reduction in operational overhead through native automation.
30% Faster loan processing times.
100% Visibility across all document workflows and deal pipelines.
Architected an AI-powered FinTech platform that automated SBA loan workflows, leveraging AI workflows to slash overhead by 60% and drive a 25% boost in revenue.