Full StackACTIVE DEVELOPMENT

SpendLens

// THE PROBLEM

What challenge did this address?

With the proliferation of SaaS tools and API consumption, teams struggle to monitor, categorize, and optimize their operations spend. Raw bank feeds or API logs are cryptic, leading to unnoticed subscription leaks, budget overruns, and manual accounting overhead.

// THE SOLUTION

How was it engineered?

SpendLens integrates raw financial feeds and translates them using natural language processing into organized, category-aware transactions. By applying anomaly detection algorithms and expense forecasting models, it flags sudden cost spikes and provides AI-recommended optimization actions.

// TECH STACK

Next.js

Python

Gemini AI

PostgreSQL

// KEY FEATURES

Core Implementation Details

↳Real-time transaction synchronization & automatic normalization
↳Gemini-powered contextual expense categorization and merchant matching
↳Anomaly detection engine highlighting unexpected spikes or double charges
↳Dynamic cost-forecasting graphs and automated savings reports

// SYSTEM OUTCOMES

Verifiable Performance Metrics

✓Identified an average of 18% savings in unnecessary or duplicate SaaS subscriptions
✓Reduced monthly bookkeeping tasks from hours of classification to a single click
✓Real-time detection of payment leaks and serverless billing anomalies