Since entering the workforce — I have been obsessed with the way that people experience work. Most of us spend at least ⅓ of waking hours at work — so naturally it impacts our mood, life, who we are, and view of the world (and we know that proportion of time is higher for knowledge workers…)
We exist at a pivotal inflection point in working history: where people are more burnt out at work than ever, and where cutting edge technology has the potential to reimagine how we’ve been doing our jobs.
After spending thousands of hours speaking to workplace leaders while at TriplePlay — it became clear to me that the biggest unlock to improving workplace experience is changing how people actually spend their time during their work day.
People want to feel purpose from their work, and simply put — smart people want to spend less time doing stupid tasks.
Hebbia is the first ML product I’ve seen that is purpose-built to give you time back.
This week my instagram feed is covered in ML generated photos from Lensa. Before that, people were using DALL-E to construct medieval villages out of papayas.
I appreciate tech for entertainment — but entertainment barely scrapes the surface of ML’s potential to meaningfully change our day-to-day.
Knowledge workers have access to more information than ever, which makes finding the information we actually need — harder than ever to find.
As a result, knowledge workers spend 19% of their time searching and gathering information. That accounts for 2+ hours of a 12 hour day. 10+ hours in a week…
Hebbia’s core tech powers search that is 10x more accurate than other tools on the market. You can measure that in MRR if you do some fancy math — but I measure this in clear examples I see everyday — of how this semantic search is so much more powerful than what we are used to.
Below is a search for “trends in consumer retail spending” across SEC earnings calls and filings.
What’s cool about the passages Hebbia shows me is that it surfaces the most relevant passages across thousands of documents — showing me key trends in US consumer retail spending, including holiday season spend, and discretionary spend behaviors.
You’ll notice that the first two passages don’t say the word “trend” — but Hebbia’s semantic search understands that “growth” is a trend — and returns the relevant results.
The less time workers spend searching — the more time they can spend analyzing, synthesizing — doing the work that matters, and gives them purpose.
I will spare you the rest of my blog-style demo (though I am happy to schedule a call if you reach out). But, I will say that my first searches in Hebbia made it clear to me that having access to this technology would have fundamentally changed the way I worked (and honestly my life) over the last few years.
I was inspired to help build it and put it in the hands of… everyone.
The biggest lesson of my career has been that if you get the right people in a room — there is no unsolvable business problem.
The more time I spend working with this team — the more confident I become that Hebbia has the right people in the room. There is a unique blend of pure intellectual horsepower and empathy that exists here.
The team is highly technical, and deeply dedicated to user experience.
When I was first introduced to George and Swetha — I knew I wanted to bet on them (and that was even before I heard the news of the Series A they raised with Mike Volpi at Index).
Across both business and engineering my colleagues are some of the most talented people I have ever met — yet no one takes themselves too seriously, and everyone cares about the journey being fun.
I believe I found the smartest group I could find — and we’re working on a solution to a problem that can change the world. I’m excited to share more updates along the way.
And if you want to be on the journey with us, please reach out to me (or anyone on the team).