Divya Mehta — Why I joined Hebbia

It has been just over a year since ChatGPT first launched to the public.

Since then, I have spoken with nearly 100 companies of all sizes, ranging from large enterprises to startups, spanning industries from financial institutions to agricultural manufacturing. Almost every team at every company I spoke to had received a mandate from their executive team to develop a strategy for leveraging this powerful new technology that would benefit their business.

What did I learn from those conversations?

• FOMO is very powerful. Nobody wants to be left behind, especially when they hear that their competitors are hopping on the bandwagon.
• The majority of these companies are stuck somewhere between dabbling with ChatGPT and experimenting with a purpose-built internal tool.
• Few companies have widely rolled out a production-ready AI application that fully capitalizes on the advancements in AI over the past year and meaningfully impacts their business.

So, amidst all this GenAI hype and the challenges that come with it, why did I join Hebbia?

Hebbia is building an AI native platform that re-imagines how knowledge workers across industries fundamentally interact with their unstructured data.

• Building a chatbot using RAG is now pretty straightforward but falls short in handling the volume and complexity of unstructured data that most companies are grappling with. Furthermore, chat isn’t a viable or scalable option for serious work. To truly drive impact, Hebbia goes beyond RAG, allowing users to seamlessly search, communicate with, and generate reports across the entirety of their data corpus.

Hebbia has built an expertise in AI applications by being early-movers in this space.

• Given how nascent and technically complex the technology is, it’s no secret that the supply of AI talent is smaller than the demand. As a result, for companies new to the space, hiring and attracting AI talent is extremely difficult. The Hebbia team stands out, having launched the market’s first LLM document search and AI productivity tool before RAG and GenAI became buzzwords.

Hard-working team with the ability, drive, and speed to keep up with the pace of innovation

• LLM models will continue to improve and the number of models that become available will also continue to grow. This is great news for the industry but also necessitates constant updates to keep up with changing API contracts, growing context windows, and ambiguous best practices for evaluation and upgrading of models. Over the past 2 years, the Hebbia team has evolved rapidly to continuously improve their product as the technology advances.

Hebbia works with enterprises to train users on how to work with and get the most of their AI tools.

• Once you have built a great AI application, leveraging it to its maximum potential requires training users to understand the new paradigm of prompting. Teaching each user to think like a prompt engineer is the most significant skill adjustment for our generation. The Hebbia team works hands-on with customers to educate them on LLMs and prompting best practices to unlock incredible value, improve performance, and reduce hallucinations.

For all these reasons (and many more!), I am incredibly excited to be joining the Hebbia team on this journey.

We’re hiring across the board — come build with us!

Divya