Building MortgageMind AI
Today was one of those development days where solving infrastructure issues mattered just as much as adding new features.
✅ Progress Completed
✔ Built the first version of the AI Underwriting Engine backend.
✔ Added a dedicated Underwriting API and service layer.
✔ Designed the Loan Summary caching architecture to reduce expensive AI calls.
✔ Implemented database persistence for AI-generated loan summaries.
✔ Updated LoanAIService to:
- Check PostgreSQL for an existing summary.
- Generate with Gemini only when needed.
- Cache the result for future use.
✔ Successfully integrated PDF viewing into the document workspace after resolving browser-related issues.
Biggest Lesson Today
During testing, I hit the Gemini API free-tier quota much sooner than expected.
Instead of simply increasing the quota, I redesigned the architecture.
Old approach
Every page refresh
↓
Call GeminiNew approach
Generate once
↓
Store in PostgreSQL
↓
Reuse cached AI resultsThis architecture dramatically reduces AI requests, improves response times, and lowers operating costs.
What’s Next
🔹 AI Underwriting Dashboard
🔹 Cached Risk Analysis
🔹 Persistent AI Executive Summary
🔹 Intelligent document verification
🔹 Mortgage underwriting workflow automation
Every sprint is moving MortgageMind AI closer to becoming a production-ready AI-powered mortgage assistant for lenders and loan officers.
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