Disprz provides a one-stop solution to meet all the learning needs of employee onboarding, training, and development of both our frontline & knowledge workers.

What inspired you to start this particular startup?

Driven by a passion to empower employees and enable businesses to thrive in the face of technological advancements, Subbu Viswanathan, along with Kuljit Chadha, founded Disprz.

Their initial venture focused on bringing tech into classrooms. However, during their exploration, they identified an opportunity to create an even greater impact: revolutionising corporate learning & skilling with technology.

They set out to bridge the gap between traditional training methods and the demands of a modern workforce, with a deep understanding of the evolving business landscape and the critical need for continuous skill development.

With a clear vision, Disprz is changing how organisations approach talent development. By enabling employees with the right skills, they are driving success for over 250 global organisations and advancing the professional growth of 2.5 million+ employees.

What started as a mobile-based LMS is now a comprehensive skilling ecosystem, and fast-evolving into a People intelligence suite.

Can you briefly explain your company’s mission and vision?

Disprz is on a mission to enable every person to advance at work and in life, through the best technology-led, scientifically-backed skilling experiences.

With a highly scientific, data-led, skill-based yet people-centric approach, Disprz is creating waves in the career progression and talent development space. Their powerful corporate learning, skilling and talent mobility suite cater to a diverse workforce, including frontline and knowledge workers, to help them grow professionally.

What trends do you see in the industry – 1 year, 5 years, and 20 years from now?

The top three trends that we expect:

The corporate learning & skilling space will see the impact of Generative AI revolutionising content creation and enabling personalised learning experiences.

People analytics will align more closely with business priorities, leveraging data to optimise workforce planning and connect skill development with strategic goals.

Blockchain technology will play a key role in skill matchmaking, creating transparent platforms for talent acquisition and management, facilitating talent mobility, and making talent-related processes, including hiring, more scientific.

These trends will shape a future where learning is personalised, skill development is aligned with business objectives, and talent is efficiently matched with opportunities. Disprz is planning quite a few exciting innovations in these dimensions.

What specific AI techniques or algorithms does your startup leverage, and why did you choose them?

At Disprz, we harness a range of cutting-edge AI techniques and algorithms to drive innovation in the corporate learning and skilling space. Notably, we employ Natural Language Processing (NLP) techniques to automatically tag content, assessments, and people to specific skills and skill proficiencies. This not only streamlines the process of skill identification & assessment, but also enhances the relevance of learning experiences, and helps in determining the role-fitment of individuals to make informed talent advancement decisions.

We have now recently also started including Large Language Models (LLMs) to help users find the best content faster through deep and accurate search, save time through smart summarisation, author / repurpose content in engaging formats, administer learning conversationally, and find personalised upskilling pathways to meet specific short-term and career goals. These strategic AI implementations underscore our commitment to providing sophisticated, data-driven, and user-centric solutions, ultimately fostering skill development and organizational growth.

What are your thoughts on the responsible use of AI, and how do you integrate ethical considerations into your AI development process?

Disprz contributes positively to skill development, learning, and performance enhancement across diverse individuals and organizations

Our AI is largely NLP-based, and we are unwavering in our stance against the incorporation of discriminatory or biased parameters and features within the predictive analyses we do on people’s performance. We avoid utilising factors such as ethnicity or such, that could potentially perpetuate bias and inequality in the talent decision-making processes.