Charlie Pickell — Why I joined Hebbia

I first encountered Hebbia a month into my previous job — I was new to our public credit investing team, and open to trying any tool that might help me stand out. Coming off of my investment banking analyst stint, I had always felt there were many parts of finance that could be automated — “monkey work”, I called it — and now that I was freshly on the buyside, I thought now was the time to find ways around it: cut out the wasted time, focus on becoming the best investor I could be.

A few Hebbia searches on some public comps and a PDF table extracted straight to Excel is all it took to spark my intrigue. Combine that with the boom of ChatGPT in the fall of 2022, and I started to really think about how AI could have a massive role in the world of finance.

Two things I’m a real sucker for are efficiency and novelty. I despise wasted effort, and I’m always willing to give a chance to a shiny new thing — be it a restaurant, a hobby, or otherwise. All this to reason why I would spend real time learning about Hebbia and how to weave it into my day to day.

These were the earlier days of the product — at the time the only real functionality was search, and on some days it was a little hit or miss with answers, some files taking longer than I wanted to upload. But I gave it the patience I felt it deserved.

In reality, it was still saving me a decent amount of time even back then, particularly around earnings season for names I covered: with pre-indexed public filings, Hebbia allows you to easily search across the 10-K’s, Q’s, and earnings calls from the last 8 quarters for any public company. For comparable companies to names I covered, I could easily pull out key highlights from the latest earnings call, ask about management’s outlook, and even dig into more specific questions like “why were margins compressed in Q2” — all without having to read the transcript or spread financials.

As I became more familiar with the product, search only improved. I learned how to better ask questions and started incorporating Hebbia into my deal processes — I could securely upload entire folders of presentations, memos, research, and notes, and search across them seamlessly all at once.

Forget which expert commented on competitive differentiation? What were the key risks highlighted in a memo we worked on 3 months ago? What was leverage at acquisition by the sponsor? Hebbia could take me straight to the answer for any of these.

As time went on I became more and more impressed, and in my role, I saw firsthand how much time and effort was spent on searching for information — and how much more of these high paid individuals’ valuable time could be spent on analysis, sourcing new names, or deeper diligence.

I began thinking I wanted to be a part of it — try and jump aboard the AI bullet train as it passes by and begins to entrench itself in legitimately every industry. I thought with my background working with the product, my knowledge of the industry (albeit limited, though more than the average tech engineer), and my desire to tangibly change many of the antiquated procedures that still exist across the banking, investing, legal, and other industries, I needed to be a part of Hebbia. As soon as possible.

The team here is truly special — these are some of the brightest, outside-the-box thinking, go-for-it individuals I’ve ever met, and what they’ve built so far is truly incredible.

Since my early interactions in the days of search alone, they’ve added what I currently view as the most powerful feature — an LLM data grid that combines the AI reading power of search with the AI writing power of a dozen LLM’s to synthesize and generate not only answers, but also digestible outputs which allow you to compare across any cut of data.

Hebbia can sit atop a folder containing a full VDR — with LP’s, quarterly decks, financials, transcripts, etc. and help you get to an analysis that normally would take 2+ hours of reading through and pulling together thoughts to get a few intro-level outputs. My favorite example is asking it for a summary of the company in the VDR, asking it to explain the transaction at hand, asking for key highlights & risks, asking it to summarize recent financial performance — and then put this all into a table for me to copy into an email to the deal team. Two hours of work, done in two minutes.

There are so many other incredible use cases, particularly those which enable firms to leverage existing databases of files they’ve accumulated over decades — think of it now like having an analyst by your side who’s been at the firm for years, knows every deal, has read every file, and knows where everything is located.

The best part is, there are other features I haven’t even covered, and we’re rolling out additional ones as we speak that multiply Hebbia’s capabilities even further. Just knowing that gets me AMPED for what’s to come.

I’m two weeks in, and I continue to have my mind blown everyday.

If any of this sounds like it could be something for your company — please schedule some time at this link.

If any of this sounds like something YOU want to be a part of — we’re hiring. Please apply at the following link: https://lnkd.in/ezBN38Pn

Many thanks to the team at Sixth Street for all the learnings, and many thanks to the team at Hebbia for welcoming me in with open arms so far.

Charlie P