Technician routing, customer ETAs, and service-area tools built for home services.

Multi-stop dispatch, drive-time service areas, residential address precision, and customer-facing ETAs , built for HVAC, plumbing, electrical, pest, lawn, and the full home-services trade.

Isometric illustration of a service depot and a ladder-rack service van on a multi-stop residential route, with ETA chips and a service-area overlay.

Trusted by home-services brands and franchise networks across North America.

Built around how home services actually run

Routing that holds up across a tech's day.

Multi-stop sequencing for HVAC, plumbing, electrical, and recurring service routes, built to absorb the emergency call that always comes in at 10am.

Service areas grounded in drive-time, not zip codes.

Drive-time isochrones around each branch, depot, or technician home base, so the quotes you accept are quotes you can actually run.

ETAs the customer can actually plan around.

Live tech tracking and ETA refreshes built on real road-network data, so 'between 1 and 3' becomes a real arrival time, not a window.

Geocoding that lands at the right house.

Apartment numbers, gated communities, rural addresses, and shared driveways, geocoded with the precision residential service work depends on.

Why home-services teams choose MapQuest

Pricing built for the volume home services actually runs at.

Multi-tech dispatch, customer tracking, and service-area lookups don't run a few API calls, they run hundreds per technician per day, and millions across a national franchise network. Per-call pricing breaks the math at scale. Our pricing is built for it.

Routing built for the way trades actually work.

A consumer router doesn't know that the 8am no-show pushes the 9am into a re-sequence. Routing tuned for the realities of a service day, emergency add-ins, parts runs, and same-day recovery.

A real partner, not a portal.

Home-services teams get dedicated technical support and account management. When dispatch breaks at 6am during a heat-wave call surge, there's a human on the other end.

What we've learned working with home-services teams

The day doesn't survive the morning plan.

Home-services dispatch built at 6am rarely survives the 9am emergency call. The teams we work with that re-sequence mid-day instead of holding the morning plan finish more jobs and run fewer overtime hours.

Zip-code service areas leave money on the table.

Service areas defined by zip codes accept jobs the truck can't reach by 11am and decline jobs that are 12 minutes from the depot. The teams that move to drive-time isochrones price what they can actually run.

The window is the experience.

A four-hour window is what the customer remembers, not the technician's tools. The teams that quote tighter, ETA-backed windows get fewer reschedules, fewer cancels, and better reviews, and most of the gain is just better routing data.

Want the deeper technical view? Read our home-services routing and ETA guide →

Common questions from home-services teams

How does pricing compare to per-call providers like Google Maps Platform or HERE?

Home-services workloads, multi-tech dispatch, day-of re-routing, customer-tracking maps, and service-area lookups, break per-call pricing fast at scale. Our model is volume-based, designed for the call patterns home-services operations actually generate. Talk to sales for a per-tech or per-branch TCO comparison against your current provider.

How does this integrate with our field service management (FSM) software?

REST-first APIs that drop into the major FSM platforms used in home services. Most customers go live inside their existing FSM workflow rather than running a separate routing portal.

Can we run a branded service-area or 'find a pro' locator on your platform?

Yes. Custom-styled basemaps, branded markers, and proximity-plus-attribute search are first-class. Most home-services brands run a fully branded locator without giving up performance.

How quickly can we get started?

API keys same day. Most home-services teams have routing, geocoding, and a service-area definition running against a sample data set within two weeks, with production integration in four to six.