India’s blue- and grey-collar workforce, the backbone of our nation’s economy, comprises millions of workers facing hurdles ranging from low digital literacy and device constraints to regional language barriers and trust deficits. With smartphone penetration accelerating yet uneven across urban and rural areas, creating technology that truly resonates with these segments requires more than just translating interfaces; it demands a fundamental rethinking of design, outreach, and support models. In our recent conversation, Moiz Arsiwala, CTO and Co-Founder of WorkIndia, outlines the critical components of building highly scalable platforms for a demographic as vast and varied as India’s working class. He discusses how hyper-localized user experiences, powered by adaptive AI and real-time analytics, can bridge the gap between opportunity and capability, ensuring no worker is left behind.
TAM: Can you elaborate on the unique challenges and considerations when designing scalable tech solutions that cater to the diverse needs of India’s blue- and grey-collar workforce?
Moiz Arsiwala: Designing for India’s blue- and grey-collar segment requires a fundamentally different approach than building for the white-collar market. We are catering to millions of candidates and employers across an incredibly diverse demographic, spanning language, digital literacy, geography, and socio-economic contexts. The challenges are multi-dimensional:
- Device and Data Constraints
A significant portion of our users access the internet on low-end smartphones, often with limited data plans. Our app and platform need to be extremely lightweight, optimized for low bandwidth, and intuitive to use even on 3G networks. - Language Diversity
India has 22 scheduled languages and dozens of regional dialects. We must go beyond mere translation to localize experiences contextually, making them relatable and trustworthy. Our mobile application is available in 10 regional languages, covering over 85% of the languages spoken across the nation. - Trust and Behavior Patterns
Many blue-collar workers are first-generation internet users. They have different trust thresholds, often preferring voice and video over text and valuing human assurance. This highlights the need for strong verification systems to ensure authenticity in candidate-employer matching. - Job Discovery vs. Job Search
Many of our users aren’t proactively “searching” -they’re “discovering” jobs. This requires building recommendation engines that are intuitive and engaging, like content discovery on social media platforms. - UX Challenges for Non-Tech-Savvy Users
Our users often require human assistance to onboard employers and gather requirements for ideal candidates. The user’s experience must be seamless, helping candidates communicate their qualifications and preferences effectively to find the right job.
TAM: What are the most critical factors to consider when designing solutions for underserved populations in a country as diverse as India?
Moiz Arsiwala: There are three non-negotiables:
- Empathy in Design
We must design with deep empathy – not for the average user, but for the most digitally inexperienced user in the remotest part of the country. Every flow must be frictionless and inclusive. - Trust, Safety, and Transparency
Digital trust are a major barrier in underserved populations. With job fraud being rampant, solutions must be secure, transparent, and supported by rigorous verification of job listings and recruiters. - Hyper localization at Scale
Technology must dynamically adapt to user behavior and location. This includes auto-switching language preferences, tailoring job recommendations based on hyperlocal demand, and providing culturally relevant support.
TAM: AI is increasingly shaping hiring processes. What are the potential risks of bias in AI-driven recruitment, and how can companies ensure ethical implementation while scaling these technologies?
Moiz Arsiwala: Risks of Bias in AI-Driven Recruitment
AI is a powerful tool, but it’s only as unbiased as the data it learns from. In India’s fragmented employment landscape, historical hiring data may reflect deep-seated socio-economic biases -including regional, linguistic, and gender-based preferences.
Risks include:
- Exclusion of qualified candidates due to non-standard resumes
- Amplification of caste, gender, or regional biases
- Over-reliance on proxy indicators such as smartphone brand or typing speed
Our Ethical Approach at WorkIndia
- Bias Audits: We regularly test our models for discriminatory patterns and retrain them with representative datasets.
- Human-in-the-Loop Systems: Every layer -from user verification to job filtering and candidate-employer matching -includes human oversight.
- Explainable AI: We build transparency into our systems, helping both candidates and employers understand how matches are made, thereby enhancing trust and accountability.
TAM: The Relevancy Platform is a key innovation at WorkIndia. How does it enhance the job-seeking experience for candidates and ensure employers connect with the right talent?
Moiz Arsiwala: The Relevancy Platform is our proprietary matching engine -the beating heart of WorkIndia.
Deep Candidate Profiling
We map not just skills, but intent, preferences, availability, location, and soft skills like communication style.
Smart Job Curation
The platform ensures candidates only see genuinely relevant jobs, using real-time supply-demand mapping and behavioral data.
Fraud Filtering and Quality Control
We filter out irrelevant or fraudulent listings, maintaining a high-trust environment.
For employers, this drastically reduces noise. They are connected only to verified candidates who are available and aligned with the role a game-changer in India’s high-volume job market.
TAM: With India’s workforce evolving rapidly, how do you see technology redefining employment dynamics in the next five years, and what areas of innovation excite you the most?
Moiz Arsiwala: Over the next five years, we will see technology bridge systemic employment gaps like never before. Key areas of innovation include:
Voice and Vernacular Interfaces
The future is voice-first. Regional-language voice assistants will guide users through job discovery, application, and even interviews.
Real-Time Credentialing
Instant access to verified work history, skills, and assessments will reduce hiring lead times from day to day.
Financial Inclusion through Employment Data
Employment Histories will contribute to credit scoring, giving workers access to loans, insurance, and financial tools simply by having a digital work trail.
Predictive Labor Market Insights
AI will forecast local demand trends, helping workers with upskills in advance and allowing employers to plan hiring strategies more effectively.
What excites us most is the empowerment this brings -millions of people gaining access to real, dignified work opportunities they may have never discovered otherwise. That’s the true power of technology -not just solving problems but transforming lives.
