Ask QX, an Indian AI for the world

Tilakraj Parmar: We have been working on the product for nearly eight years now. We are one of the first to build a product on a hybrid, node-based architecture. Our product is about 30 per cent GPT-based and the rest is a unique blend.

We are launching with 100+ of the planned 300 global languages and 12 Indian languages. This involved a significant effort to train and tune the programme just as the architecture and model-building.

Arjun Prasad: We are 3 Indian founders, bootstrapped and working for eight years on tech and then launching only when we were ready for the customer. Our strategy is to go for the B2C market and empower everyone with the power of AI.

Tilakraj: We have trained around 372 billion parameters which is roughly 6 trillion tokens — one token is roughly 750 words. Currently we are in the beta phase of web version that was launched in January.

We have almost 8 million users now, which is a big number for us because we were not expecting this kind of a response. We have had somewhere around 60 million prompts — questions that people ask on the platform.

So the goal is to not just focus on the English language. This is where we differ from the rest of the crowd. We want to get into the regional language market where we want people to think and write and create in their own language.

Tilakraj: On the web, we don’t have voice input yet, but on the app that we’ve just launched, it is present. In our voice input feature you can ask questions, but the answer will come in text as of now. Voice response will be coming soon.

Arjun: The multimodal product is in alpha phase. It’s in our roadmap and we will release it at some point in the year. We are one of the very few companies that actually believe in delivering the tech-first and then talking about it. It’s in alpha phase right now and we want it to be fine-tuned to make sure it’s a beautiful product.

We will soon have the text-to-image, text-to-voice, voice-to-text options. We are also working on ensuring that the usability — our UI, UX — is user-friendly.

Tilakraj: The team’s Indian origin had a huge impact on the platform’s language features, particularly in terms of cultural understanding and linguistic diversity. We decided not to work with translations but with original material in the given language. While it was a challenge to find so much digitised material in Indian languages — other than the top 6 or 7 languages. It wasn’t just literature that we needed. We need current, on-going material from all areas as well as in languages.

Tathagat Prakash: To address this challenge, we looked towards synthetic data. Over a period of time this synthetic data will be replaced by actual data. But the synthetic data would have done its job — of not making the user feel like they are dealing with a translation.

Challenges include diverse structures, scripts, phonetics, scarcity of quality datasets, capturing contextual nuances, integrating cultural context, resource constraints for less common languages, orthographic disambiguation, speech recognition challenges, semantic ambiguities and the need for efficient algorithms in various technical environments.

Tilakraj: We use contextual inferencing and we have also put “ethical dilemma” in it, where if somebody puts in a query about some famous movie stars or great personalities from India or anywhere one else in the world, the program will stop. “Is it necessary to give that answer?” These are few things that we’ve worked very hard on. Before launching the product, we wanted to be very sure about contextual inferencing and the ethical dilemmas it brings in so that you know the product doesn’t affect the end user adversely.