Skip to content
Example build · Scheduling & dispatch

FieldOps

Dispatch that accounts for skills, parts, and traffic — not just a map pin

Service contractors with 20–200 technicians14 weeks to first crew livefrom $145k
A dispatcher managing a scheduling board and map across three monitors
+22%
Jobs completed per tech per week
5% → 1.4%
Return visits for wrong parts
2 hr
Arrival window, down from all-day
The situation

What was actually happening

A dispatch and job-management application replacing a whiteboard, three spreadsheets, and a group text thread. Built for a mechanical contractor whose scheduling constraints no off-the-shelf FSM product could express.

A field technician holding a rugged tablet showing a job checklist
  • Jobs were assigned by a dispatcher's memory of who was good at what
  • Techs arrived without the right part roughly one visit in five
  • Emergency calls blew up the day's schedule with no re-optimization
  • Nobody could tell a customer a real arrival window
The build

How we solved it

01Constraint-aware scheduling

Assignment considers certifications, truck inventory, drive time, customer SLA, and technician preference — and re-optimizes when an emergency lands.

02Parts prediction at booking

The likely parts for a job type and equipment model are surfaced when the call is taken, so the truck is stocked before it leaves.

03A mobile app techs will actually use

Offline-first, four taps to close a job, photo capture, and voice-to-text notes that get structured into the work order automatically.

04Customer communication on autopilot

Real arrival windows, an on-the-way text with live ETA, and an after-visit summary — all generated from the job record.

The application

Screens that carry the workflow

Interface placeholders — the real product screens go here once client approval lands.

Dispatch board

Drag-to-reassign with live constraint warnings

Technician mobile

Offline-capable job view with photo and voice capture

Parts forecast

Predicted parts by job type and equipment model

SLA dashboard

On-time performance by crew, customer, and job type

AI inside

Where the models actually do work

The application works without any of this. The AI is what makes it fast — and every use below has a confidence threshold and a human path when it isn’t sure.

  • Parts prediction from historical job and equipment data
  • Voice-to-structured-work-order transcription in the field
  • Anomaly detection on job durations that flag training or estimate problems
Integrations
QuickBooksTwilioGoogle Maps PlatformSlackVerizon Connect
Time to production
14 weeks to first crew live
Typical investment
from $145k
Something like this?

Your version won’t look like this one.

That’s the point — the whole reason to build rather than buy is that your process is yours. Tell us what yours does and we’ll sketch the shape of it.

We use your details to reply to this request only. No sequences, no list.

Taking two new engagements this quarter.