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AI-based data solutions are characterized by computational accuracy and quick processing, allowing your business to operate faster and grow exponentially. Artificial intelligence chatbots and apps are used as well as machine learning models, neural networks and deep learning. iMerit is such an AI-based data solution company and Radha Basu is the founder and CEO. Analytics Insight has engaged in an exclusive interview with Radha Basu.
iMerit is the leader in providing AI data solutions for machine learning algorithms and artificial intelligence (AI) applications. iMerit takes unstructured data or mediums and annotates them to create highly accurate structured data for advancements in machine learning (ML) and AI. iMerit's aim is to provide high-quality, tech-enabled data services that leverage human intelligence to power algorithms in ML while creating positive social change through employment in the digital economy. iMerit aims to collaborate with clients and partners to deploy AI by unlocking the value hidden deep within large volumes of unstructured data.
iMerit provides end-to-end services to guide data scientists through all phases of their data annotation projects and architect a solution that meets their needs. Solution architects draw upon their deep expertise to define the requirements, training, workflow, feedback loops, and data validation required to achieve high-quality data. Once the annotation and delivery process begins, they use a dynamic improvement model by incorporating feedback through constant monitoring and edge case analysis to gain insights and further ensure data quality and accuracy. iMerit works with leading brands across the globe, with Fortune 500 companies in healthcare, autonomous mobility, government, GIS analytics, finance, insurance, retail, ecommerce, agricultural AI, legal, and others.
iMerit provides AI data solutions for all forms of content and mediums, including:
In 2015, iMerit raised US$3.5 million in Series A funding from Khosla Impact, the Michael & Susan Dell Foundation, and the Omidyar Network. In 2020, in Series B funding, it got US$20 million, led by CDC Group.
Today, it has a global presence with 10 delivery centers in India, Bhutan, and the US. It has become a tech-enabled data annotation powerhouse with over 5,000 employees. The mission of human-empowered computing resonates throughout the organization, be it in the skilling model or its operational structures. The model centers around providing employment in underserved regions, and harnessing latent talent through continuous and inclusive skilling.
iMerit takes a double-bottom-line approach, by which social and financial impact metrics are enshrined in the articles of association alongside normal business metrics and are tracked and reported quarterly.
Started in a single room, iMerit has now expanded to 10 centers with over 50% of the staff comprising women. It continued to increase the size of its workforce even during the COVID-19 pandemic.
A critical component of its success is bridging the gap between skilling and employment. By doing so, has opened opportunities across geographies and set employees on a path of professional growth and economic stability
The company's team of subject matter experts applies its knowledge to every phase of clients' process – which involves customized solutions. Experts-in-the-loop work with the client through the entire lifecycle:
Radha Basu is the founder and CEO of iMerit. She is a leading tech entrepreneur and pioneer in the Indian software business. Under her leadership, iMerit has employed hundreds of skilled and marginalized women and youth in digital and data services worldwide.
Previously, Radha was the SupportSoft chairwoman and CEO. She led the company through public offerings and built it into a worldwide market leader in support automation software. Earlier, she spent 20 years at Hewlett Packard, where she assisted the electronic software division business, founded operations in India, and set up the first software center of any multinational in Bangalore.
Radha founded Santa Clara University's Frugal Innovation Hub and co-founded the Anudip Foundation, iMerit's sister foundation. She also serves on the boards of NetHope, the Jhumki Basu Foundation, and the Miller Center for Social Entrepreneurship.
Radha Basu has a master's in computer science and biomedical engineering from the University of Southern California and has completed an executive management program in business administration and management, general from the Stanford University Graduate School of Business.
She has received numerous awards, including:
Radha Basu, through iMerit, aims to fill a void in the billion-dollar data annotation and labeling industry, for which the most important skills today are in the computer vision and NLP sectors. Many people are required to work with large amounts of data. For that, she has successfully created a workplace that provides steady employment and skills development in local communities in underserved areas.
During a period of extreme challenges, Radha led a transformation within iMerit that found it changing from a company that conducted 100% of its operations 'on premise' to 100% of its operations remotely. At a time when many companies were laying off and furloughing employees, iMerit was procuring laptops, working with broadband providers to lay cables in villages in India, upskilling its workforce, and hiring employees in India, Europe, and the US.
During the pandemic, Radha launched unique wellness programs to ensure employee health. Along with her teams, she ensured a strong connection between executive leadership and the global workforce through more than 100 virtual fireside chats with employees. From July to December 2020, iMerit added over 800 jobs. Radha has successfully led iMerit's metamorphosis, from vibrant center-based culture to more than 2,500 people working from home in rural and remote areas.
After all the challenges of 2020, iMerit ended the year stronger than ever, reaching new highs in client satisfaction, revenue, employee growth, and workforce skills development.
The first challenge since inception was retaining the quality and content of the skilling structure and providing multi-level training to employees. Training in linguistic, cultural, and behavioral skills was required for client communication. Gauging the grooming necessary and the adaptive capability of each employee and thereafter training them was a challenge. A robust L&D structure and the use of technology and a custom learning management system helped the company maintain the quality of skilling and prepare teams to work on cutting-edge AI data projects. The pandemic was an extremely challenging period. All India and US centers (90% of the capacity) abruptly shut-in late March. The biggest shift involved changing our delivery process from 100% on-premises to 100% work from home (WFH). The challenge was even more because most employees come from humble homes with inadequate infrastructure, in communities with limited connectivity. So, the teams worked tirelessly to activate over 2,400 people under stringent lockdown by distributing machines, installing broadband/dongles in over 1,500 houses, and working with local ISPs to upgrade connectivity in very poor neighborhoods. Further, the families and communities of the employees were sensitized to the mission and unique demands of them working from home full-time. Team leads helped them transition while managing their own crises during the pandemic.
The trends we are seeing in big data analytics and AI vary depending on the maturity of the product or service being developed. Teams in the research and development stage are looking for large, structured ground-truth datasets to validate their hypothesis and get the project off the ground. The sensitivity to speed is lower as the project is in its early stages. Teams are seeking guidance to develop their frameworks and build a robust pipeline for model development with repeatable, explainable processes. As AI teams move closer to production, they are more focused on finding and solving edge cases in the datasets. The speed of iterations becomes more critical and real-time insights into the process are required. With a massive volume of data, workflow management and appropriate tooling become key.
AI in India is projected to grow at a CAGR of 20.2% and is expected to reach US$7.8 billion by 2025, according to a recent IDC report. There is increased activity and investment in automation and intelligent solutions across sectors, including healthcare, agriculture, retail and social media. Many customer service applications are seeing traction, a natural use case to tackle the massive volume of queries in any sector.
Focused research across India's academic institutions is required to build a strong ecosystem of experts to power the growth of AI. While leading engineering universities like the IITs and IISc have invested heavily in ML research, there is a need for wider adoption and AI-specific skill development across the academic landscape. Technical talent is currently seen in limited urban pockets but for widespread AI growth, knowledge and expertise need to be distributed across states.
Government investment in infrastructure for AI, along with interest and investment from the VC community, has already seen an upward trend. The creation of policies and frameworks that support data sharing and usage, especially in sectors like autonomous mobility, should be undertaken by experts and lawmakers with a robust understanding of AI and its applications.
As these sectors continue to mature, data preparation will be foundational, particularly in sectors where digital transformation is still underway. With a variety of data sources that need to be digitized and structured for use, data labeling and the creation of ground truth training data will be crucial. The explosion of IoT has resulted in vast volumes of sensor data that can be leveraged for AI applications. Challenging annotation workflows like multi-sensor fusion and those that require subjective analysis of scenarios will be required to map the world around and build ground truth for AI applications.
As the AI market moves closer to production and deployment, iMerit's work and growth will reflect the changing priorities of teams working to leverage ML across industries. Over the next year, it will make significant investments in technology to better enable AI data solutions across the lifecycle of products and services. The focus will be to develop and provide a complete package to clients from a workforce and technology perspective. This will involve an investment in the company's infrastructure and ability to scale alongside our clients. There will be a continued emphasis on solving complex challenges in ML DataOps. We will invest in building a deeper understanding of tools that can enable creation as data moves from the unstructured stage to pre-ML, and all the way to production, where data management is required.
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