Dayjob fits waste-management fleets that lose margin when routes, drivers and last-minute jobs move faster than a planner can update the schedule.
What it does
Dayjob’s current product is an AI scheduling agent for short-haul trucks. It connects to a job-booking system and telematics, schedules routes, adapts when the day changes, and factors in travel time, skip size, driver schedules and customer time windows. Dayjob homepage YC profile
The company’s origin story is specific. The founders say they watched waste-industry planners spend an hour dragging jobs onto trucks, only for the plan to become wrong within another hour. The current site frames the result in operational terms: more jobs, better time windows and less planner administration.
Dayjob’s published calculations include 11% revenue improvement, 50% more customer time windows and 25% less admin time. These figures are company models based on stated fleet assumptions, not an independent benchmark.
Why I’d look closer
The product is designed around exceptions. A route planner does not need another static dashboard if jobs arrive, drivers change and customer windows move during the day. Dayjob’s value is the re-planning loop.
The founders’ backgrounds fit the wedge. The YC profile describes George Postlethwaite and Fred Fooks as having previously built Gaea, with George bringing Deliveroo and Otta experience and Fred bringing engineering and data-science work.
What could make it the wrong choice
The product depends on a working connection to the existing booking system and telematics. A fleet should ask what data is required, how exceptions are approved, what happens when a driver rejects a route, and how performance is measured against the old schedule.
Pricing is not published in the checked sources. The revenue and efficiency figures should be rebuilt from the operator’s fleet, jobs, hours, service windows and current margin rather than copied from the homepage.
My editorial take
I would shortlist Dayjob for a waste operator with repeatable short-haul jobs and a planner who is already spending hours rebuilding the day. I would not start with the headline revenue estimate. Start with one depot, one fleet and one exception-heavy route, then measure whether the agent makes the plan easier to operate—not just faster to generate.
Quick facts
| Field | Sourced detail |
|---|---|
| Product | AI route scheduling and real-time re-planning |
| Buyer | Waste-management and short-haul fleet operators |
| Integrations | Job-booking systems and telematics are named |
| Pricing | Not published in the checked sources |
| Main fit question | Does the fleet have enough daily change to justify autonomous re-planning? |
Sources checked
Dayjob homepage, YC profile, launch material and privacy page were checked on 2026-09-19.
Cohort context
Dayjob is listed in Spring 2026. In our 2026-09-18 directory snapshot, 112 of 193 listed companies in that cohort have YC’s primary industry label B2B (58.0%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.
Public website snapshot
Observed 2026-09-19 16:16 UTC in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.
| Signal | Homepage observation |
|---|---|
| Product description metadata | Observed |
| Canonical link | Not observed in this response |
| H1 or H2 heading | Observed |
| Typed structured data | Not observed in this response |
| Docs/developer link | Not observed in this response |
| Pricing link | Not observed in this response |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |
Public observations · Collection method. Missing links here do not establish that a capability or file is absent elsewhere.
