How EnrouteAI found its market by selling the outcome first

Transportation had interested Neil since childhood. He later studied it during his master’s program and worked with an MIT professor, where he saw the operational problems faced by transportation companies firsthand.

He understood the mathematics behind route planning, loading, capacity, and delivery economics. The opportunity appeared to be bringing capabilities used by companies such as Amazon to smaller operators that could not build large optimization teams.

Neil’s first product helped plan package-delivery routes and determine how a truck should be loaded. It was technically connected to a real industry problem—but that did not automatically make it the right business.

The initial product became a starting point rather than the final destination. The underlying optimization capability was valuable; the market still had to reveal where that capability was most urgently needed.

Fleet operators already had processes, spreadsheets, deadlines, and people responsible for pricing. Asking them to adopt a new product created resistance even when the technology was strong.

Neil realized that a general pain point is not the same as demand. Customers experience many problems, but they act only when important work is blocked and time is running out.

Truckload carriers must submit prices for their capacity whether EnrouteAI exists or not. That pricing project is the demand. The stronger offer was not ‘buy our pricing software’; it was ‘we will price this bid before your deadline.’

That distinction changed both the message and the way Neil evaluated opportunities.

Neil replaced abstract personas with a concrete hypothesis: a specific pricing leader at a specific trucking company. From there, he worked backward to find where those buyers gathered.

Specialized trucking conferences and cold calling helped him reach early prospects. Instead of waiting for a finished product, he recommended showing a mock-up or completing the work manually to learn what customers would actually pay to solve.

The original delivery product evolved as customers exposed a stronger opportunity. If route optimization could reveal the cost of serving a load, the same foundation could help determine what that truck capacity should be sold for.

EnrouteAI became a pricing engine for full-truckload fleets. The product direction came from repeated market evidence—not from protecting the original idea.

Neil said EnrouteAI now has a team of six. Its customers range from a business with roughly $1 million in revenue to a major U.S. furniture manufacturer with approximately $6–7 billion in revenue.

The talk did not present a predetermined five-year expansion plan. Neil’s position was that the next market—whether LTL, ocean freight, air freight, or something else—should be decided by demonstrated customer pull.