The App Fixes the Hour: How Platforms Are Reorganising Domestic Work in India
The promise of formality, dignity, and efficiency offered by on-demand apps for domestic workers mask the fact that they only reorganise old labour circuits and exacerbate its inequalities and exploitation
It is still early in the morning when Shanti’s* phone buzzes, even before she has left the house after wrapping up her own exhausting household “duties”. There is a notification from a domestic work platform on the screen: a two-hour cleaning job beginning at 9 am in Hiranandani Powai, one of Mumbai’s most affluent neighbourhoods. The app does not say what needs to be done–no details about the house, the number of rooms, or the kind of cleaning that is expected. For a moment, she hesitates, but then accepts. Too many rejections and the work stops coming.
Shanti has never visited the area but estimates the journey from her home in an informal settlement near Ghatkopar will take at least an hour. She walks the 5 km distance, though the trip takes longer than expected. Avoiding the congestion on the main road, she cuts across the small hill between Ghatkopar and Chandivali toward Powai, passing a labour naka-like gathering point where workers assemble each morning to report to a male team leader.
By the time Shanti arrives, she is exhausted. Inside, the job bears little resemblance to the app’s description of basic cleaning—“sweeping, mopping, dusting”. Dirty dishes are piled up in the sink, the bathroom needs scrubbing, and the floors are caked with grime. There is no way to renegotiate the task or extend the booking without jeopardising the next one. The time slot is fixed, even if the workload is not.
She works faster, keeping one eye on the clock. When she leaves, she does not know what rating she will receive, only that it matters. Before she reaches the gate, another job notification flashes on her phone.
Shanti, a mother of three school-going children in her 40s, joined a domestic work platform in April 2024 after a recruitment campaign for domestic workers in her chawl. The company promised earnings nearly three times higher than those in conventional domestic work. She was initially offered Rs 75 per hour. A year later, another worker I interviewed reported earning Rs 125 per hour on the same platform.
Shanti’s experience denotes a broader shift in how domestic work is organised and mediated today in Indian cities, through apps and algorithms.
When I began my PhD research on domestic work in 2023, fully developed on-demand platforms had yet to emerge. Beyond a few experimental start-ups, such as Beegle in Bengaluru and MaidHub in Pune, the platform sector remained largely organised through more conventional intermediaries – placement agencies, website-based services, and telephone networks. My research therefore focused on broadly conceived labour intermediaries. Historically, such intermediaries have done far more than just match workers with employers. They have played a central role in governing labour, shaping workers’ mobility, social reproduction, and everyday lives.
The year 2024 marked a turning point in the platformisation of domestic work. Backed by fresh venture capital, a new generation of on-demand platforms—including Snabbit, Urban Company’s InstaHelp, and later Pronto—entered the sector. Just six months into my fieldwork, the landscape had shifted dramatically. Major platform firms launched pilot projects in affluent Mumbai neighbourhoods such as Hiranandani, Powai. Yet their operations remained tentative, hampered by difficulties in recruiting workers and building a stable customer base.
The situation was different in Bengaluru. Earlier bootstrapped platforms such as Beegle and Broomies had already expanded beyond established neighbourhoods like Jayanagar and JP Nagar into newer growth corridors such as Electronic City and Varthur. Interviews with workers on these platforms painted a more nuanced picture than dominant critiques of platform labour suggest. Most spoke positively of the apps, citing significantly higher earnings. Although formal protections were minimal, features such as credit advances, uniforms, fixed schedules, and what workers described as “permanent jobs” offered a sense of stability, recognition, and dignity often absent in traditional domestic work.
By 2025, it was clear that platforms were becoming more than a new hiring mechanism. They were increasingly shaping how domestic workers moved through the city, organised their time, interacted with clients, and understood the possibilities of their work.
This account, however, remains partial. It is based on workers’ narratives and limited by what they chose—or were able—to share. Much of the labour that sustains these lives, especially the forms of care, negotiation, and social reproduction that occur beyond the platform interface, remains difficult to capture even through long-term ethnographic research.
Platform Modus Operandi
Recent entrants such as Urban Company’s InstaHelp and Snabbit mark a shift from platform-mediated matching to on-demand service delivery. Domestic work is no longer arranged through profiles, referrals, or phone calls but booked instantly through an app. Cleaners can arrive within 10–15 minutes, while tasks are standardised, priced, and packaged into discrete time slots. Domestic labour is thus increasingly transformed into a service unit that can be ordered, scheduled, and consumed on demand.
Despite this shift, recruitment remains largely conventional. There is currently no evidence of app-based self-onboarding, as seen in ride-hailing and delivery platforms, and my fieldwork found no formal engagement with location-based agencies. A former Urban Company employee noted that the company initially approached agencies informally with commission incentives, but the effort appears not to have succeeded.
Many workers recalled that, during the pilot phase, platforms recruited directly in slums and chawls, distributing pamphlets and advertising potential earnings of Rs 20,000–30,000 per month.
“During training, workers were told grand things. ‘We will make you a crorepati’”, recalls Shubhangi*, a domestic worker. They were not allowed many questions, she adds. Platform representatives also promised four days off (one per week on a preferred day), she said, which workers later discovered were unpaid.
Once platforms secured sufficient labour supply, recruitment shifted to referral-based growth, with workers incentivised to bring in women from their families and neighbourhoods. Some reported promised referral payments that were not always honoured, while others like Neelima* joined after seeing a poster in her neighbourhood bus stop.
Workers’ first three days on the platform are designated as “training,” covering manual onboarding, document capture, app navigation, and behavioural instruction. During this period, platforms collect Aadhaar, bank passbooks, and PAN cards, often retaining originals for several days, and require forms with personal details, including address and, in some cases, religion and caste category.
They also assessed workers’ preferences and constraints, as a Snabbit employee noted: “what work you can do, which houses you are willing or unwilling to go to, whether you will go to houses with dogs.” Beyond this, platforms imposed physical and age related norms including checks on height and weight. Snabbit reportedly enforced thresholds for both age and body size. Neelima recalled that “weight had to be below a limit”, adding that a woman weighing over 70 kg was turned away. Another worker remarked bitterly that the work itself was so intense that “after one or two months, workers automatically lose weight”.
Workers were instructed not to “irritate clients,” who were described as “our gods,” and to avoid confronting dissatisfaction. Yet, as many noted, the most consequential rules only became visible after entering work. Initial training focused on app use and basic conduct, while key dimensions of platform labour—penalties, ratings, client escalation, and the operational meaning of “flexibility”—were largely learned on the job.
The Fiction of Formalising Paid Domestic Work
Before app-based platforms, web-based services had already begun to “formalise” domestic work. Sites such as BookMyBai.com, KaamwaliBais.com, and Helper4U functioned mainly as listing platforms, enabling employers to filter workers by age, experience, location, and often religion and other personal details. In practice, however, the process remained partly offline: BookMyBai, for instance, also relied on phone-based matching, sharing workers’ photographs, addresses, and profiles with employers, and in some cases bringing workers in groups for in-person selection.
“They would make us stand there, and the employers would just look at us and choose. It felt very strange. It felt as if we were being chosen for a matchmaking process,” said Anubhuthi*, a worker I interviewed in Bengaluru.
These platforms also helped produce a discourse of mistrust around domestic workers. One BookMyBai.com advertisement claims that “3 out of 10 maids have done some form of crime,” offering background verification—Aadhaar checks, police records, and identity validation—as a solution. The worker is thus framed less as labour than as a potential risk to be managed.
Rather than eliminating informality, these platforms formalised selective aspects of recruitment while reproducing existing social biases. Historically, domestic work was organised through neighbourhood and kin networks. With urban growth, location-based agencies expanded, but worker circulation continued to depend on these informal circuits, especially among migrant women from marginalised backgrounds.
This is the context into which new age platforms enter. Despite promises of formality, dignity, and efficiency, they remain dependent on the same labour circuits they claim to modernise, reorganising rather than replacing informality and translating older practices of recruitment and control into a more systematised, app-mediated form that reproduces existing inequalities.
Domestic Work Becomes A Commodity
It is against this backdrop that the next phase of platformisation emerges. If the earlier phase was structured around mistrust, the current one is defined by hyper-commodified convenience. This shift is evident in platform advertising. Urban Company’s InstaHelp, for instance, frames domestic work as “chill pills,” casting household labour as a stress condition requiring relief. Cleaning, mopping, and washing are recast as symptoms of stress, resolved through on-demand service delivery. In a podcast, Snabbit founder Aayush Agarwal, citing his own difficulties finding reliable help and a perceived market gap, asked, “Why is it easier to buy a car online than to get someone to wash your dishes?”
Companies frame their model as offering workers “flexibility” and “autonomy,” while promising clients “reliability” and “convenience.” Workers are labelled “experts,” with claims of 50–100% higher earnings alongside flexible work arrangements.
In a promotional campaign, workers clean inside a glass-walled vehicle moving through the city, staging domestic labour as a public, performative display. Slogans such as “house help in 10 minutes” further compress embodied labour into promises of speed and instant availability. Domestic work is thus reframed as a market inefficiency to be “optimised,” rather than a site of labour exploitation requiring rights and minimum wages.
The Vulture Capital
This technological shift also carries important financial implications. Rapid platform expansion has been fuelled by venture capital, notably firms such as Elevation Capital, a major investor in start-ups that seek to digitise everyday practices such as AppsForBharat’s SriMandir app which enables a host of spiritual services. These firms invest across competing platforms, including Urban Company’s InstaHelp and Snabbit, a common pattern in start-up ecosystems, where investors hedge bets across companies in an emerging sector.
When the same capital backs multiple competitors, it raises a basic question: what is actually being financed? The focus appears less on service provision than on scale, market capture, data extraction, and user habituation.
Grace Blakeley describes this as “vulture capital” — funding that prioritises rapid expansion and competition over innovation. In early stages, workers may benefit from higher wages, sign-on bonuses, predictable earnings, and fixed shifts as firms build labour supply. But as companies scale and investor pressure grows, the model often shifts toward tighter control, lower unit costs, and increased worker availability.
This pressure is passed on to workers through penalties, unpaid waiting time, strict attendance rules, rating systems, commissions, compulsory purchases, ID blocking, and algorithmic monitoring. The funding model therefore shapes the labour model. Platforms compete not only to deliver domestic services but to turn a historically informal, feminised sector into a scalable market. Losses are tolerated if firms show growth, retention, data capture, and geographic expansion. But this drive for scale intensifies labour control, where claims of convenience and dignity coexist with surveillance, discipline, and wage suppression.
Platforms Reorganise Domestic Work
Domestic work in urban India has long been organised through informal but structured arrangements. Workers find jobs through neighbourhood networks, word of mouth, or intermediaries such as security guards and local agents. These relationships are ongoing and shaped by everyday negotiation, but are structured by deep caste, class, and gender inequalities that shape access to work and its conditions.
Tasks are rarely pre-defined but are negotiated within households, sometimes on an everyday basis. Workers constantly adjust to shifting expectations, often through routine accommodation. Pay is similarly flexible, shaped by the number and type of households, tasks performed, and the worker’s bargaining position.
For Saritha*, a domestic worker in Bengaluru, pay was lower than in platform work but more predictable. “We knew how many rooms to sweep, how many people were in the house and we negotiated our salaries accordingly,” she said. These employer–worker relationships, though not equitable in any way, are embedded in social ties that workers actively navigate, managing workloads and schedules through informal negotiation. Tasks are often adjusted across days to accommodate leave or early departures, with expectations coordinated directly with employers.
Platform companies entered into this already structured field with their new systems of booking, standardisation, and control layered onto longstanding hierarchies of labour. What changes, most visibly, is not the task itself but the conditions of its execution. Time is divided into fixed one, two or three hour slots, creating an appearance of predictability, but tasks remain undefined in advance. Workers may only discover on arrival whether they will do routine cleaning, deep scrubbing, or extra ad hoc work. With time fixed, they adjust their pace, skip breaks, or carry unfinished work into subsequent bookings.
“Customers always ask us for extra work,” says Rohini. “One day I finished sweeping and mopping before the log off time. But the customer made me clean the kitchen ‘kappas’ because 15–20 minutes were still left.”
Pranali describes a similar experience: “They notice that there are 2 or 5 minutes more and you cannot stop. They use you as a machine,” she says, detailing how every minute of labour is incrementally extracted.
The informal employer–employee dynamic is increasingly displaced by rating systems. Though framed as neutral feedback, workers describe ratings as structuring everyday household interactions. Small gestures—how one speaks, the speed of task completion, and willingness to absorb extra work—directly shape evaluations and, in turn, future access to work.
As Shubhangi puts it: “Customers usually rate us not on the quality of the job but on something else. We work for 6–8 hours a day. Our body is in pain. And they still expect us to be in a good mood and talk to them with a smile, and if we don’t do that, they will give lesser ratings.”
Maintaining a high rating is essential for securing future work and, in some cases, access to welfare benefits. The system is tiered into gold, silver, and bronze categories, primarily linked to differential health insurance coverage rather than wages—for instance, gold workers reportedly receive up to Rs 5 lakh coverage, while silver workers receive around Rs 3.5 lakh. Although the exact figures remain unclear, employees consistently reported unequal access to benefits based on ratings, with scores of 4 and above generally considered acceptable.
The Body Bears The Cost
The work, Sumitra says, is taxing and, without proper rest, untenable. “The phone keeps ringing. Even if you’re exhausted, you feel like you can’t let it go.”
Platform work intensifies already physically demanding labour, but workers emphasise that the difference lies in pace and continuity. Fixed time slots, back-to-back bookings, and strict time limits leave little room for rest or recovery. What was earlier staggered is now serialised, with informal breaks compressed into narrower intervals.
For Shanti, this intensity is both enabling and exhausting. Formerly an agricultural labourer in a dry-land village, she migrated to Mumbai after a family crisis, carrying debt and responsibility for three children. On the platform, she works nearly 12-hour shifts, earning about Rs 1,000 a day. Within months, she repaid her loan, brought her children to the city, enrolled them in school, and improved her rented home. But the costs are evident– significant weight loss and persistent joint pain that often requires medication to sleep.
Workers experience the platform’s promise of flexibility as a trade-off between income and physical survival. “We neither have washrooms nor a place to change pads. Our stomach gets bloated,” says Sunita, pointing towards how intimate bodily needs are rendered invisible within the system.
Fatigue is not incidental but built into the work design. Strain accumulates not only from tasks inside homes but also from travel, waiting, and constant adjustment to new spaces and expectations. The app fixes duration, tracks completion, and measures performance, but remains agnostic to fatigue, pain, and depletion, even though these shape how the work is actually done.
Navigating The System
Workers do not experience these conditions passively. Over time, they develop strategies to navigate the combined demands of platform and household, continuously adjusting routines, expectations, and tactics in response to shifting work conditions.
Across bookings, they learn to pace themselves—deciding what to prioritise, what to accelerate, and what may be left unfinished. “We see what is most important and finish that first,” one worker says, describing rapid judgments shaped by both time pressure and employer expectations.
Awareness of ratings further shapes conduct. Workers report being especially careful not only with tasks but with how they are perceived, often accommodating extra requests and avoiding confrontation to protect their scores. At the same time, access to work requires constant engagement with the app. Workers repeatedly check for bookings, reorganising their day around availability. Some cluster jobs geographically to minimise travel; others accept distant work to avoid income gaps.
These strategies do not remove uncertainty but manage it. They reflect an ongoing effort to absorb pressures of time, evaluation, and availability in order to keep work viable. What platforms frame as “flexibility” is, in practice, the continual labour of assembling a fragmented and unstable working day.
Platform-based domestic work is often presented as a shift towards greater organisation and efficiency, but as the everyday experiences of workers show, these systems do not fully reshape the conditions under which domestic work takes place. Instead, they sit alongside them, introducing new forms of coordination and control while relying on workers to manage the gaps that remain.
Underneath these debates, there is a broader question about what it means to ‘formalise’ work that is shaped by the intimate and unequal spaces of the household. If the app can fix time but not the task, measure completion but not effort, and track performance without accounting for its conditions, then much of what makes this kind of work possible remains outside its frame.
Domestic workers across Indian cities are navigating systems that extract maximum labour while offering minimum protection. The same inequalities of caste, class, and gender that structured domestic work before the apps arrived continue to structure it today, now encoded into ratings, penalties, and time slots. Demands for minimum wages, regulated hours, written contracts, and the right to organise, raised by domestic workers and their unions for decades, continue to evade them.
[*Names have been changed to protect the identities of women workers.]
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