Aviocharter
AI Cargo Pricing Platform
Published 2025
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
