Devnagri Raises $600,000 Seed Round From Venture Catalyst, IPV And Others

The funding round will be utilized to strengthen its technology and scale its operations to help B2B sectors create more vernacular language content for end-users

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Devnagri, an Indian language translation engine with a focus on B2B, on Tuesday announced to have raised $600,000 from Venture Catalyst, Inflection Point Ventures (IPV), and other co-investors as part of Seed Round. Co-investors that participated in the funding include Mitesh Shah (IPV-First Port Capital), Rohit Chanana (Sarcha Advisors), Nimesh Kampani (Trica), Sameer Karulkar (Coverpage Ventures Advisory LLP), Prashant Sharma (Facebook), Karan Bhagi (HUL), and Deepak Sharma (Kotak Bank).

Devnagri

The seed funding round will be utilized to strengthen its technology and scale its operations to help B2B sectors create more vernacular language content for end-users.

 “There is a strong need for content in Indian Languages to be available over the internet, which helps the Indians to use technology (be it Entertainment, News, Education, Movies, etc.) in their respective language. With our current round of funding, we intend to scale our operations to tap B2B customers and enable them to create more local (Indian Language) content to reach end-users from tier-II and tier-III cities,” said Nakul Kundra, founder, Devnagri.

A brainchild of Nakul Kundra and Himanshu Sharma, Devnagri was established with a vision to make the internet accessible to 90 per cent of Indians and solve the problem of “lack of content in Indian languages” using tech. The platform combines Neural Machine Translation with machine learning and a community to power translations. Its AI-human combination can help businesses scale their operations anytime, in any language with up to 50 per cent reduced cost, real-time delivery tracking with 80 per cent reduction in translation time, and fivefold faster than professional translation.

With the latest infusion of funds, the company intends to increase its accuracy in Machine Translation and scale its operations to target businesses in sectors like e-commerce, BFSI, and publishers who depend on vernacular growth.

 “The Indian vernacular language and translation market size is worth $53 billion, which currently features edtech, e-commerce, publishing, and OTT Industries. With 100 crore Indians from tier-II and tier-III cities expected to join the internet (Because of affordable smartphones and data), the content availability in Indian languages is only 0.1 per cent, and less than 10 per cent of Indians are conversant in English. Devnagri is confident in plugging this huge gap using machine translation. As one of the leading startups in the NLP Industry of India, the company is aligned with Digital India and Atma Nirbhar Bharat's vision to enable the Internet in Indian Languages. They are showing immense exponential growth and we wish them success in their journey,” said Dr. Apoorva Ranjan Sharma, president, and co-founder, Venture Catalysts.

 “Indian languages are missing from the internet. In the last 10 years, many websites have started providing multi-lingual options for non-English users but it is still not enough. Devnagri has developed a model, which can scale the efforts to put Indian languages on the Internet map. Their application of ML and Neural machine translation will help companies use contextualized translation. We believe this would be a gamer changer in Indic languages on the internet with relevant used cases,” shared Mitesh Shah, co-founder, Inflection Point Ventures.

“Today, India has a digital ecosystem that includes close to a billion people and is being created at a scale unparalleled anywhere else in the world. We are in need to reach the last mile / last consumer of the internet in their language so that they can harness its power,” added Himanshu Sharma, co-founder, Devnagri.

Currently having over 5000 translators, Devnagri has an extensive and incessantly growing community aiming for scalability, savings, and satisfaction of its catering businesses.