Booking, staffing, a trace of each visit, and dispatch you can defend to the client
The Ministry of Commerce counts more than 30 million people in China’s domestic-service workforce, a market above one trillion yuan, growing more than 20% a year. Urbanization, an older population, and two-income households keep demand rising. The old model still runs on scattered workers, uneven quality, thin trust, and slow dispatch.
The platform is built on IoT, big data, and mobile internet. It covers cleaning, postpartum care, nannies, caregivers, and appliance repair: online booking, matched dispatch, check-in at the door, a record of the visit, the client’s review, and skills certification. GPS, a geofence, and time-on-site make the visit something a manager can check, which is what lifts satisfaction and repeat orders.
① Matching is a phone call. WeChat groups and memory hold the schedule, the skill, and the location. Idle hours and waiting clients sit side by side, and labor utilization stays under 60%.
② The visit is hard to see. False check-ins, shortened jobs, and attitude complaints are common. Repeat business is only about 35%.
③ Credentials are a story. Health certificates, skills, and training records are hard to verify, so clients choose by rumor.
④ The credit file is now a requirement. The Guiding Opinions on Building a Credit System for Domestic Services ask firms to keep a traceable record of each worker.
From the booking to the credit score
Clients book cleaning, postpartum care, caregiving, and the other lines online. The match uses skill, location, and the open slot, which cuts idle time. Rush jobs, monthly packages, and custom work sit in the same queue.
The worker checks in on the app or mini program at the client’s address. Time, duration, and GPS are stored. A geofence rejects a check-in that is not actually at the site.
Each profile holds the health certificate, skill certificates, training, and past reviews. Background checks, skill grades, and a star rating make the qualification visible before the client books.
After the visit, the client scores quality, manner, and skill. Those scores become a credit figure: higher-rated workers are offered first, and a low score raises a warning.
Homes and offices, booked by the visit or by the month
Care for the mother, the newborn, and ongoing nanny work
Companionship for older adults, patient care, and help with rehabilitation
Cleaning, repair, and installation at the address
Two apps. Workers check in, accept jobs, and navigate. Clients book, pay, and review. 4G, 5G, and Wi-Fi keep the apps usable where coverage is thin.
Proof of arrival. GPS and BeiDou plus a geofence confirm the worker is at the address. Bluetooth beacons help indoors.
The home, if the client allows it. The same stack can talk to the IoT platform so a lock, a camera, or a device state is available during the visit.
Dispatch gets more than 40% faster because matching fills idle slots. Repeat orders move to 55% or higher once the visit can be traced. Complaints fall by about 50% when reviews have consequences. Output per worker rises about 30%, so the same team covers more clients. Leading firms typically recover the spend in 12 to 18 months.
Booking, dispatch, and check-in first. Reviews, training, and the credit file come after those three are stable. Smaller firms can start on SaaS. Larger ones often want a private deployment.
The worker app has to be worth opening. Training stipends and a bonus for accepted jobs get check-in and location data that you can trust. Associations and training schools are the practical place to attach skill certificates and the credit file.
Addresses and live locations are personal data. Store them encrypted under the Personal Information Protection Law, and connect to the Ministry of Commerce domestic-service credit platform where the regulator expects it.
Tell us how many workers and service lines you run, and we will shape booking, check-in, and the credit file around that