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RateConGuard

A rate confirmation verification tool that standardizes review, catches discrepancies, and reduces fraud through validation rules, versioning, and a ready-to-sign approval gate.

RateCon Guard

Role

Full-Stack Developer

Timeline

3 months

Year

2024

Stack

Next.js
TypeScript
PostgreSQL
Tesseract OCR
Prisma
TailwindCSS

The Problem

Rate confirmations are inconsistent PDFs and emails. Missing details, mismatched rates, wrong addresses, and bad terms cause disputes, detention issues, and unpaid loads. Fraud and double-brokering make it worse.

The Solution

Built a structured workflow that turns messy rate cons into validated, trackable records with checklists, discrepancy detection, and version history — all before a dispatcher or driver commits to a load.

Key Features

The capabilities that make it work.

Rate con intake via PDF upload, email forwarding, or manual entry

Required-field validation for rate, commodity, pickup/delivery times, detention, and accessorials

Discrepancy detection against booked load details: rate mismatches, wrong addresses, missing terms

Checklist-based review with a “ready to sign” approval gate

Version tracking for edits and updates to rate con terms over time

Broker history with prior disputes, average pay time, and red flags

Full audit trail: who approved, when, and why

Clean standardized rate con summary export for driver dispatch packets

RateCon Guard screenshot 1
RateCon Guard screenshot 2
RateCon Guard screenshot 3

Architecture

Document ingestion pipeline with optional OCR for PDF text extraction into structured fields. Rules engine powers validation and discrepancy flagging against booked load data. Database models span loads, documents, extracted fields, reviews, versions, and approvals. Activity logs and notification system for real-time status updates.

Challenges Solved

The hard problems behind the polished surface.

01

Building reliable OCR extraction from wildly inconsistent rate con PDF formats across hundreds of brokers

02

Designing a rules engine that catches real discrepancies without drowning dispatchers in false positives

03

Creating an approval workflow fast enough for the pace of freight while thorough enough to prevent fraud

The Outcome

Caught rate discrepancies on 15% of incoming rate cons that would have previously gone unnoticed. Eliminated post-delivery rate disputes for fleets using the approval gate. Average review time dropped to under 2 minutes per rate con.

What's Next

Where this product goes from here.

AI-powered risk scoring based on broker behavioral patterns

Automated alerts when terms change after initial approval

Integration with load boards for automatic rate con ingestion