From Doom-Scrolling To Deal-Closing: How Two Founders Turned Addictive Algorithms Into A Logistics Sales Engine

The U.S. logistics industry has no shortage of software promising full automation of time-consuming tasks, dashboards, and efficiency gains. Yet for many companies, the real bottleneck comes in accurately finding and targeting potential customers, with many sales teams stuck doing endless cold calls with no guidance.

Albert Lie and Shan Wu saw this reality emerge quickly after launching their first logistics platform, breadd.ai. Customers appreciated the time saved on back-office tasks, but they kept signaling that the real challenge was finding and closing the right deals.

That insight drove them to pivot the platform from operations automation to a sales recommendation engine that aims to learn from past successes to point sales teams toward promising leads, making the prospecting process smoother and more targeted than before.

The Builders Behind The Vision

The careers of Albert Lie and Shan Wu began in the world of startups. Albert, a self-taught engineer from Borneo who became VP of engineering for a large expense management platform, brought deep expertise in systems, algorithms, and AI search. Shan, who started his career cold-calling donors, understood the pressure and complexity of closing sales better than most.

When they got together to develop breadd.ai, the first version of the platform set out to automate administrative work for freight brokers and carriers. It streamlined time-consuming tasks like data entry, scheduling, and paperwork, letting software handle it instead of staff.

But while the tech was solid, customer feedback quickly cut through the founders’ early optimism: operational efficiency didn’t matter if it didn’t drive new revenue. As one customer put it, “It’s nice you freed my staff from their ops, but now I pay them the same wage to do less work. Whenever you can help me sell, call me back.”

That blunt response revealed the real problem facing logistics companies. They didn’t need more automation; they needed growth tools.

Targeting A Deeper Problem In Logistics

As breadd.ai gained traction, its limits became clear. The platform was reducing manual work, but it wasn’t solving core challenges that companies faced. Sales teams were drowning in outdated CRMs and spreadsheets, chasing long lists of random prospects with no sense of which ones were worth their time. In a sluggish freight market, that inefficiency turned into a major drag on growth.

Albert and Shan began exploring a pivot. They realized companies needed to find the right buyers faster, yet sales reps were left guessing, burning through hours of cold calls that rarely turned into deals. It was during this period of frustration, marked by long nights marked by burnout and doomscrolling through social media, that the founders connected two seemingly unrelated dots.

The recommendation engine of media-driven platforms never asks what anyone wants to see; rather, it learns from every click, pause, and replay to deliver exactly the right content people are looking for. “The smartest people in Silicon Valley are working hard to rot our brains,” Albert said. “That’s sad. But it also shows how powerful these AI algorithms can be.”

If an algorithm could predict what would keep millions of people glued to their screens, Albert and Shan realized, the same behavioral learning could help freight brokers predict the next shippers most likely to close. “We figured out we could use the same approach to find the best match for a freight brokerage or carrier, and keep the sales rep in the flow and focus on selling,” Shan explained.

That realization set the stage for breadd.ai’s pivot: turning machine learning loose not to capture attention, but to focus it on the leads most likely to bring in revenue.

Reengineering The Scroll For Sales

The pivot moved breadd.ai away from automating routine back-office tasks and toward helping sales teams identify the prospects most likely to convert. The platform works by continuously ingesting data from every shipper a company wins: industry sector, commodity category, shipment volume, trailer type, facility location, and more.

Using this historical data, breadd.ai trains a recommendation engine designed to detect correlations too complex or subtle for humans to track on their own. Over time, the system identifies patterns that tend to show up in successful deals specific to each sales rep, and highlights new shippers that share similar characteristics.

The result is a streamlined, continuously updated view of prospects that helps sales teams cut through the noise of bloated CRMs and endless spreadsheets. Instead of guessing where to start, they can focus their time on opportunities with clearer signs of potential.

Early signs with customers were promising. The founders recall, for example, one early customer who admitted their skepticism upfront. Two weeks later, after closing a deal directly from breadd.ai’s recommendations, their view changed. “Instead of throwing a thousand darts in the dark,” they said, “we focused on ten laser-targeted accounts — and it worked.”

AI That Works For Humans

At its core, breadd.ai is built on a belief that technology should create connection, not distraction. Albert and Shan want the smartest algorithms to help people unlock their full potential at work.

With breadd.ai, the mission is clear: reclaim AI from the attention economy and put it back in service of human ambition. As the founders themselves put it, “We envision a world where every sales rep is equipped to find their perfect match every morning, and connect as human beings. When that happens, we’ll see less spam, more connections, and every sales rep winning in their niche.”

Albert Lie and Shan Wu are making that future possible, designing technology that makes sales brainlessly easy, and connections genuinely human.

BDG Media newsroom and editorial staff were not involved in the creation of this content.