Bengaluru-based Pronto is expanding its 'Verified' service, which uses worker-worn cameras to collect AI training data, to 1,300 devices by August. While the company aims for this data segment to generate 90% of its future revenue, the model faces scrutiny regarding privacy and scalability compared to industrial data collection methods.
Bengaluru-based home services startup Pronto is aggressively scaling its 'Verified' service, an initiative that captures video footage of home interiors via cameras worn by workers to train artificial intelligence for robotics. The company has reported a sharp rise in demand, with daily orders for the service climbing from 15 to approximately 700 within two weeks. To meet this demand, Pronto plans to increase its fleet of head-mounted recording devices from 130 to 1,300 across Bengaluru and the National Capital Region by August.
Business Pivot Toward Physical AI Data
Pronto is positioning itself as a provider of physical AI training data, a specialized field where companies collect real-world movement patterns to teach robots how to navigate complex environments. Founder Anjali Sardana has projected that this data-focused business could account for 90% of the company's total revenue within the next five years. Unlike competitors that often outsource data processing, Pronto intends to manage the entire cycle of collection, annotation, and sales internally. The company currently operates with gross margins between 20% and 70%, aiming for a stabilized range of 50% to 60% as its internal processing systems become more efficient.
Operational and Privacy Challenges
This strategy has drawn significant attention due to the sensitive nature of recording inside private residences. While Pronto maintains that the program is strictly opt-in and utilizes anonymized video, industry observers have questioned the long-term feasibility of such models. Bharat Chadha, an expert at Uniqus Consultech, has noted that collecting data in domestic settings presents greater scalability and privacy hurdles than in controlled industrial environments. Furthermore, competitors including Urban Company and Snabbit have publicly distanced themselves from such recording policies, opting not to implement similar data-gathering measures.
Investor Monitorables
The financial success of this pivot will depend on Pronto’s ability to manage high operational costs. The company is currently spending heavily on cloud computing and GPU processing power to handle the incoming data, which creates pressure on profit margins. Additionally, the company is actively recruiting machine learning and data science professionals to optimize its workflows. Investors and stakeholders should monitor how the company balances these capital-intensive technology investments with the ethical and privacy-related challenges inherent in its chosen business model. Future progress will hinge on the company's ability to maintain high service demand while navigating potential regulatory scrutiny regarding data privacy in domestic spaces.