Importance and Relevance of Jugalbandi

The current approach to AI-based solutions has two issues: the proprietary nature of the tools being developed and the lack of collaboration among creators. This results in AI solutions that require considerable private investment and are often kept behind closed doors. As a result, entrepreneurs, particularly in the development sector often start from scratch, leading to inefficiencies and higher costs. To overcome this, OpenNyaAI mooted the design and development of AI public goods like baseline models, datasets, and benchmarks, which, despite being expensive and time-consuming to develop, offer high repeat value. These public goods can be used to quickly build POCs that can aid any social entrepreneur to garner funding and support for various applications.

While the Jugalbandi stack is not restricted to any particular sector or domain, it was initially devised to make law, justice and government schemes more accessible to Indian citizens. With the low proliferation of legal services in the hinterlands, with some reports suggesting that only about 0.5% of the population having access to legal aid, this was the ideal domain as a unit of change. But the original proposition of accessing critical information in a simplified manner in your own language was powerful enough to not be bound by only a specific sector. Additionally, as the capability of the LLMs kept improving exponentially, it became increasingly clear that the applications of Jugalbandi that utilised these LLMs could be sector agnostic.

Although LLMs like GPT have been advancing rapidly from GPT-3 to newer versions like GPT-4, and GPT-4o, their quick development rate poses a challenge. While the digitally equipped can fully utilize these technologies, people lacking digital and language skills risk being left behind, widening the gap between those who are digitally literate and those who aren't. Additionally, these LLMs many times run the risk of hallucination which is an unaffordable flaw when providing with crucial information that directly impacts the actions that a user may take, Jugalbandi’s value add to address this problem is three-fold:

1. Make the conversational interfaces accessible across many more Indian languages

2. Restrict the possibility of the LLMs hallucinating. Overcoming a key roadblock to ensure that AI based solutions are more reliable for someone without the time or capability to immediately fact check the information provided to them

3. Through the use of finite-state machines and support across multiple channels, make various services available to the user beyond conversing with the LLMs, on any medium of their choosing