Knit raises $16.1M Series A to replace weeks-long enterprise consumer research with AI-driven insights in days

I'll let you do the honors, Sean. First gong hit for me for the day for the day. I'm going to have to hit the gong for Palanteer. But how are you doing? uh would you mind kicking us off with a little bit of introduction on yourself and give us the latest news? Yeah, for sure.

Well, first of all, thanks for having me here. My name is one of the co-founders and the CEO of NIT. And uh yeah, we're excited. We announced our uh $16. 1 million series A on Thursday. Let's go. Congratulations. Back hand. You love it. Amazing. Talk to us about the company. How are you pitching it right now?

Who are the customers? Break it down for us. Yeah, for sure. So yeah, NIT, we have built a system of agents that is really building the simplest way for enterprise consumer researchers to go from, hey, I have a business question to here's a story I can share with my stakeholders.

Um, so yeah, what we're solving is this age-old problem of anytime you want to talk to your customers, especially these large enterprise brands, it's taking them four to six weeks, they're spending anywhere from tens of thousands to hundreds of thousands of dollars.

and we're helping them do that now in days instead of weeks and at a fraction of the cost. Uh so working with the largest enterprise consumer brands today. What is the is is the key differentiator just uh AI? Are there other differentiations on the the go to market strategy or the the way you build the product?

like is there counterpositioning against the big you know 800 pound gorilla in this category that uh you know we we we know and uh might be at a basketball game but uh you know he's he's definitely not taken days off recently. Yeah. So um yeah I mean great question there.

So basically at NIT you know what we've really kind what we're putting our hat or hanging our hat on right now is this idea of researcherdriven AI right so we were we're an AI native company we started the company you know a year or two ago when when uh AI was uh still not not as prevalent in our industry as it is today but what we realized is that you know you can you can run this data through the AI but really what it's missing is the context that the researcher brings understanding how this shows up in your business how do you actually sell put into the organization.

So throughout our plat platform, the way that shows up is one, we always have the researcher at the center of how we run research. So the researcher on our platform will go in and share this is you know more information about my business. Here's the context of my business.

Here are my stakeholders and how they like seeing the data so that we can get the output as close to that stakeholder ready. And then the second is we've invested pretty pretty heavily into an internal research team. Um, so throughout the process, all of our customers are paired with a dedicated expert researcher.

And what we've seen is that that gets the AI to a better spot, a place where um, our research customers can actually then take this, it's a really good first draft. They clean it up a little bit and then they can share with their stakeholders pretty quickly. How are you closing all these logos?

You got Amazon, Mars, ESPN, T-Mobile, Paramount, NASCAR, MOET, Hennessy, Overtime, insane lineup, WNBA, crazy insane, crazy stacks for series A. I'm sure I'm sure with some started off with some good old hustle, you know, I I flew out, met a bunch of our first customers that just kind of figured it out.

But really what we've invested, going back to this researcher-driven AI piece, is we've really invested in the human aspect of it. So um we primarily meet most of our customers through events. We show up uh we go to all the major events in our industry.

Um we we show up to their offices, take them out to you know take them out to dinner and and invest heavily in based meetings. Um the other aspect is really around our What about NASCAR? You you you did you ever crush a beer at at a NASCAR event for research purposes?

I feel like that that's a good good sign of respect in NASCAR. The NASCAR team was kind enough to invite us out to Daytona when we kicked off our partnership. So, yes, they they know how to have a good time for sure. Just watch. They only drink champagne. It's just the biggest narrative violation of all time.

Uh it'd be great. Oh, what? So, where I mean you're talking about the customer the human centric research where where do humans excel in this uh in in this problem set in this domain? And then where does AI thrive?

like where do you not want to delegate a piece of the work to an AI and where do you want to spend zero minutes of human time focused on something? Yeah, absolutely.

So, you know, where we've seen humans really excel is is storytelling and our entire platform is built around how can we take all the time inensive kind of grunt work that goes into research.

So think, you know, setting up your first draft of the questionnaire, analyzing all this unstructured qualitative data, whether it's video data, open-end text data, and we've built really good AI that can get you that first draft, whether it's a questionnaire or a final report.

And really where the human researcher comes, both um our internal team as well as, you know, the external kind of partners that we work with is how do we take that those insights and tell a really powerful story here? something that will actually drive action from our marketing team or product team or strategy team.

Yeah. How important is it to build a like liquid supply of people that are willing to answer surveys? I you know when I think about Amazon, Mars, ESPN, T-Mobile, Paramount, NASCAR, uh Moa Hennessy, JBL, like a lot of those research projects are probably just I want to know how Americans feel about my product.

It's not this hyper specific talk to just my customers, my my 10 people. I can probably call those people if I'm running that company.

But if I want to know how is my brand perceived or globally T-Mobile, T-Mobile also can do like like easily survey active customers, new cohorts, old cohorts, whereas like Moa can't necessarily. And so how how important is it over time for you to be able to bring surveyable people to a platform? Yeah. Yeah.

Well, first of all, I would say we're we're very data agnostic, right? So, we work with our partners where they layer us on top of their existing customer list. Um, we work with some of the larger panel companies in our space.

But, as you know, making that stance that we're data agnostic, one thing that we've invested a lot of energy in and technology in is how do you make sure the quality of that data is really good. So, a big problem in our industry today is uh data quality challenges.

There's fraud, there's bots, there's there's a lot of uh not so great stuff happening. And so what we do is we've invested a lot in our AI tech to especially if you think about video for example, if you collect video responses, you can actually see the human on the other end.

We analyze across seven different plus parameters to make sure that that human is who they say they are and actually speaking on topic. So the data quality is an area we've really stood out for our partners as well. Very cool. Anything else? No, this is great. I think we're good. Thanks so much.

Congratulations on the progress. We will talk to you soon. Have a great rest of your day.