Aviocharter

AI Cargo Pricing Platform

Published 2024

Services

Product Architecture

Frontend Development

Backend Development

AI Model Integration

Custom Admin Dashboard

Timeline

3 months

About Aviocharter

Aviocharter is an air cargo broker that helps businesses move goods fast. They coordinate aircraft across a global network, connecting freight with private and commercial air operators for urgent, oversized, and mission-critical shipments.

The Problem

Cargo pricing isn’t simple. It depends on weight, volume, aircraft type, distance, and timing and until recently, quoting all of that was manual.

Aviocharter’s brokers were spending hours building quotes, most of which didn’t convert.

Low-intent leads were eating up time. Customers were dropping off due to long wait times. And brokers lacked the tools to respond with speed, accuracy, or confidence.

They needed a platform that could:

  • Deliver quote estimates instantly

  • Recommend viable aircraft options

  • Improve transparency around pricing

  • Help qualify leads earlier in the process

  • Keep human brokers involved where it mattered

Our Solution

An AI Cargo Pricing Platform

We built the Aviocharter AI Cargo Pricing Platform — a system that lets users generate real-time, data-backed quote estimates from a clean frontend while giving Aviocharter’s team full control over the data, logic, and lead flow in the backend.

What we built

1. Instant AI Pricing Interface

Users enter basic details: origin, destination, payload, and departure date. Within seconds, they get:

  • A list of aircraft options

  • Estimated pricing (based on past quotes and AI projections)

  • Smart labels like Recommended, Nearby, and AI Estimate

  • The option to connect with a broker instantly (email or WhatsApp)

This lets serious leads self-qualify and start the sales process without delay.

2. Aircraft Selection Engine

Behind each quote is a matching engine that checks:

  • Payload and volume constraints

  • Cargo hold and door dimensions

  • Route distance and airport compatibility


Some planes can handle the weight but not the size of the cargo. Our system filters those out automatically, so users only see aircraft that actually work for their shipment.

3. Authentication and payments

To make sure returning users could easily view or manage bookings, we set up a lightweight but secure authentication system using Twilio for SMS-based login. If the user already had a profile, the bot could then pull up past appointments and stored payment methods.


We added fallback login via password (for users who didn’t want SMS) and integrated the final step payment by redirecting to MassageBook’s existing payment gateway.


This created a seamless flow from question to confirmation in one uninterrupted conversation.

4. Internal Backoffice Dashboard

We gave Aviocharter a secure backend where their team can:

  • Upload historical quote data via CSV

  • Edit/delete records in real time

  • View incoming quote requests with full details

  • Track customer engagement and deal flow

This turns quoting into a living system — not just a form submission.

What made this project complex

Internal Backoffice Dashboard

Our system had to reason with multiple constraints: weight, volume, cargo bay dimensions, and door size.

Quote data is incomplete

Some routes don’t have much past data to go on, so the system uses smart estimates and always shows how those numbers were calculated.

Speed without shortcuts

We had to keep the UI light, while calculating and ranking real options behind the scenes.

Lead handoff matters

The tool couldn’t replace brokers, it had to get better-qualified leads to them faster.

Results and Impact

Since launching, the platform has delivered measurable results:

90% faster quote responses

The bot can handle the bulk of booking, rescheduling, and cancellation interactions — significantly reducing the need for manual support.

Improved client trust

Customers see realistic price ranges upfront, which builds credibility.

Reduced broker workload

The team now spends more time talking to serious leads and not wasting time on people who were never going to book.

Timeline

The platform was built and deployed in three months, including frontend development, aircraft logic, quote prediction, backoffice dashboard, and integrations.


The platform is still being tested and improved, with new updates rolling out as it learns from real usage.

Tech Specs

Frontend

  • Next.js – App directory support and server-side rendering

  • React – For building a responsive, component-based UI

  • Tailwind CSS – Utility-first styling with custom animations

Backend & Data

  • Node.js, Express, tRPC, Prisma

  • Custom aircraft constraint-matching logic

  • Quote database with CSV ingestion and editor

  • WhatsApp + email handoff automation

AI & Prediction

  • OpenAI (via LangChain) - for fallback estimation and natural-language queries

  • Internal logic - for distance scaling, payload adjustment, and margin buffers

  • Learn-as-you-go quote predictions, improving over time

Backoffice

  • Password-protected admin portal

  • Upload, manage, and track quotes and leads

  • Real-time request visibility for brokers

Built by Green Republic

Built by Green Republic

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Let’s build together

Tell us about your project or reach us at

Follow us on Socials.

Let’s build together

Tell us about your project or reach us at

Follow us on Socials.