Intelligent Freight Discovery

Arrive Logistics is a technology-driven freight brokerage that connects shippers (companies moving goods) with carriers (truck drivers and fleets). Much of its success depends on how efficiently both sides can find, match, and book freight in real time.

Problem: Manual freight matching slowed booking speed, limited scalability, and reduced carrier engagement.

25%

Higher booking conversion on loads surfaced through matches compared to manual search

15%

Faster load coverage across key markets due to improved routing suggestions

40%

Higher repeat bookings among carriers using matches at least 3 times monthly

Overview

An iterative redesign of Arrive’s Carrier Portal homepage to surface high-probability matches through ML-driven recommendations, reducing search time, increasing instant bookings, and aligning the portal with modern 3PL technology trends.

Discovery

Carriers relied on reps manually using Arrive’s proprietary XLR8 matching engine to broker freight. The engine applied predictive algorithms considering historical carrier performance, lane preferences, equipment availability, and dynamic pricing models. While powerful, these insights were siloed and inaccessible at scale in the portal.

I collaborated with Product, Engineering, and Data Science to map algorithm outputs to carrier needs. Understanding the input features, probability scoring, and constraint validation helped define which signals mattered most to carriers and how they should be surfaced without overwhelming them.

ML application painpoints

Product owners initially pushed to surface matches on Find Loads, but real-time search behavior on that page meant dynamically pulled results could be displaced by recommended matches, creating friction and cognitive load. Surfacing high-probability matches on the homepage allowed controlled experimentation, data capture, and iteration without impacting core workflows.

Collaborative analysis and discussion

Ideation

Here’s what we learned and drew from when shaping the technical side of the matching engine. Machine learning became the core lever. Our algorithms score the booking likelihood of every carrier–load pair by looking at past acceptance patterns, seasonal demand, lane density, and carrier constraints. Real-time validation keeps the list clean by surfacing only the loads a carrier actually qualifies for based on insurance, equipment, and permissions.

The ranking model balances dynamic pricing, margin, and pickup-to-delivery efficiency so carriers see the most valuable options while brokers capture revenue. Incremental updates feed new data without reprocessing the entire database, which keeps latency low even as volume grows. ML outputs flow directly into the interface, mapping to UI components in a way that keeps relevance obvious and performance aligned with the system’s legacy requirements.

The concepts below guided us to what mattered most.

Benchmarking of ML applications

Hackweek Winning Concept - Carriers discover real-time visibility on volume, hot markets, lane price comparison assistance, reloads visibility, all while improving radius search limitations.

I considered the interplay of ML-powered confidence scores, carrier workflow psychology, and interface clarity. “Book It Now” buttons made sense during active search sessions, but homepage recommendations were meant for discovery. My customer-focused goal was balancing actionable visibility with minimal cognitive load, ensuring carriers could review detailed constraints, equipment requirements, and lane metrics before booking.

Design Execution

The first iteration of Matches tested whether Carriers would notice and engage with potential loads, moving beyond legacy table patterns that crowded the interface and created cognitive overload. I wasn’t thrilled with the use of the Oreo effect to draw attention, feeling the dark surfaces were overused and diluted hierarchy, but Product won the argument and we used it to test engagement.

Based on these insights, the second iteration refined the card experience to highlight both load and lane interest while maintaining a clear action hierarchy. This balance of visibility and discoverability allowed carriers to explore opportunities without feeling overwhelmed and created a structured foundation for data-driven decision-making in future iterations.

What we delivered

Solution

Looking ahead, the next step on the portal roadmap could have been a homepage experience designed to support informed decision-making. The concept surfaced value upfront, encouraging carriers to explore and understand opportunities before booking. Ideas like integrating the market map and grouping recommendations by state or equipment filters illustrated how we might have tested ML-driven recommendations in a controlled way without disrupting core workflows.