How AI and ML actually match crews to projects
4 min read
For decades, finding the right sub meant cold calls, word of mouth and a lot of luck. A matching engine replaces that guesswork with data: it reads the project's requirements and the sub's profile, then surfaces the best fit in seconds — and gets sharper every time a match succeeds.
From guesswork to data
For decades, finding the right sub meant cold calls, a stack of business cards and a lot of luck. A matching engine replaces that guesswork with data. It reads the project's requirements — trade, location, timeframe, license class — and compares them against every sub's profile, then surfaces the best fits in seconds instead of the days it takes to work a phone list.
Those four core parameters — location, trade, timeframe and license — do most of the work, and extra filters refine from there. The result isn't a random list; it's a ranked shortlist of crews who can actually do this job, on these dates, in this place.
It gets smarter with every match
A real-time bidding layer lets subs compete transparently for listed projects, and the same matching works identically on the web dashboard and the mobile app — so you can respond from the office or the truck. Every confirmed, successful match feeds the model, which means the recommendations sharpen over time not just for you, but for the whole network.
Key takeaways
- Location, trade, timeframe and license are the core parameters — extra filters refine from there.
- A real-time bidding system lets subs compete for listed projects, transparently.
- Matching works the same on the web dashboard and the mobile app — pick whatever's convenient.
- Every confirmed match feeds the model, so recommendations improve for the whole network.