How Factmata CEO Dhruv Ghulati grew Factmata to $52.8K revenue and 3 customers in 2024.

hello everyone my guest today is roof gulotti he's a forbes 30 under 30 leader in technology from uh for europe has built startups at on at entrepreneur first and tech stars london having started his career in finance at bank of america merrill lynch she trans uh transitioned into being a product leader engineer and scientist in the space of artificial intelligence and data science drew you're ready to take us to the top yeah let's go all right so you're building fact mata now tell us what the company does and what's the revenue model how do you guys make money yeah so so um fat matters vision is is that um we have a proliferation of online information um content coming from social media news sites and we've been focusing in the last kind of few years to how how to analyze that data how to make sense of it um what we've been missing on the internet a little bit is a sense of sort of a quality control layer on that content uh telling you if that content is credible safe uh and actually what's what it's saying from a deeper perspective so um what we're trying to build is a ranking engine for online content that understands content for its quality credibility and safety and who would pay for this kind of thing yeah so so what we're doing is in terms of monetizing that ranking engine is we take components of that ranking engine and essentially they are algorithms that analyze the tone of voice of language as well as they detect claims within that content um and we have two three main products today so one of them is a moderation service that can detect if a piece of content is is framed in a sexist racist or propagandist way and so we help platforms to moderate their content trust and safety teams um we have an api um that we monetize essentially any platform that wants to have tags on its content or filters on its content that tell you if that content's opinionated or propagandist or hate speech and so on um and finally we've been building a product this year that is essentially a new way of doing media intelligence and social listening so we scan the internet so that's twitter reddit facebook posts um news websites niche blogs we essentially within those extract the claims that people are making um the actual individual statements that people are saying so that we can provide analytics to brands government agencies consultancy firms and so on okay so you're upselling against those three product suites do you also have some kind of upsell based off number of seats or data usage maybe number of api calls per month yeah so on our api it's a number of api calls per month uh or based on volume uh in terms of our moderation service it's also based on volume so how many pieces of content we flag is hateful that were successfully taken down we get paid for that and in terms of our our media intelligence service uh we charge on a per topic basis so if you want to analyze claims that are made about coca-cola if you want to know those claims that made about the anti-vaxx movement uh the claims that are made about christchurch massacre johnson and johnson's talcum powder scandal will charge on a per topic basis interesting okay that's helpful to understand so give me we don't have time to go on every customer cohort down every single product line you have but give me a sweet spot on average what's the company or customer or brand gonna pay you per month or per year to use this technology yeah so obviously it differs but i'm gonna focus on where our main kind of revenue stream is right now which is around our expert media intelligence product um so um that ranges essentially um based on the type of topic and the complexity of topic that you're giving us but typically it ranges from uh two and a half thousand dollars per month um going up to even ten thousand dollars per month okay you say a fair average though might be like three grand a month something like that yeah something like that and paint so let's go deep on that so if i pay you 300 a month today about how many topics are you probably covering for me we're covering one topic for that okay covering one for that and typically what we have is we have clients wanting to you know you can imagine a big uh brand or a big pr agency or a big government they don't want to just analyze anti-backs content they might be thinking about all sorts of other types of fake news that they want to track uh or they might want to think about all sorts of topics that are relevant to their brand that they want to track so they'll take a bundle of let's say five or ten and then we'll charge on a discounted rate per topic um so per customer it ranges you know got it so it's not three grand per topic it's just your and your average is about three grand and then you know someone paying 10 grand a month might you might be covering 10 topics for them at a grand per topic because it's volume discount exactly i see okay put this stuff on a timeline for me when'd you launch the company uh so we launched the company in um so we raised our first check of funding and actually got started probably in november 2017. when was the first line of code first line of code probably november 2017. okay same and then and then you raised how much capital today uh we've raised two and a half million dollars today okay and how much was that first round do you remember uh that was a million dollars did you need that capital or would you do you wish you didn't take that dilution so early [Music] that's a great question um i would say um we in the nature of what we're doing right now started tackling a big problem like misinformation disinformation um i think we're lucky in a sense that we would raise capital um to experiment and build stuff even if that stuff was not client ready because we're sitting now like a year in where we've actually got the technology to then apply it to use cases whereas a lot of people who are now starting off having thought about the idea uh then have to go and build like a hp algorithm and they're like already 12 15 months behind so we knew that certain things would be valuable what's the team look like today how many folks yeah so we have 12 people um we have um three software engineers um five machine learning people and rest uh commercials so one salesperson um myself a head of product um and um yeah and a data product manager who helps us get our training data for our algorithms are are you and the other sales person are they quota carrying uh in terms of uh uh targets and so on no no like is it a true sales person do you have is it a true sales motion or there's a quota there's a commission etc yes absolutely yeah absolutely the way that we've done it actually is interesting because um our solution around detecting let's say claims of interest is a step change beyond existing competitors in the market so a lot of it is educating our customers and so the way that i've built our sales teams we have a lot of satellite sales people um in one in new york one in san francisco purely based on commission so we've managed to keep very very lean in that way so there's founder-led sales how do you retain that person so in new york if they're a good salesperson there's no way a small startup like you is going to be able to retain a good salesperson on a commission only structure someone else will come pick them off or there's not a good sales person um it depends because if the salespeople that we've got on on satellite are actually super relevant so they so this particular salesperson was at a company i can't say but basically our competitor right like this i've nabbed three of our competitors like top sales guys who are leaving uh or like um i thought you said you had two i thought you said you had two sales people today you you and one other person so me and other person the full-time guys and then we have these satellites oh they're not full-time okay and so you absolutely right um but but basically they have a black book of people you know 150 warm leads that they were speaking to before and so they get paid and i've done it on a very very high commission like like give me a general sense are you trying like 70 of first year acv 50 50. okay interesting and can i ask you when you add up all of your saddle i'm just curious as a channel if this is effective for you because i haven't heard of this kind of idea before across the entire satellite kind of commission army you've built how much have you paid out total over the past year um total i mean we're just going to market so i can't really say um but um but yeah i mean i mean i would say probably expected in the next month with the deals that were closing i would say probably uh five five out of the eight deals probably coming from from that structure okay i i'm just curious though from a volume so if like if your average monthly is three grand so first recipe is 36 you're basically paying 15k across eight five deals into that system you've set up yes so it's about 100 about 105 000 yes exactly but but for us that's that's it's about long term thinking versus short term right well yeah but the reason i'm asking though is because it drove you out of a cash gap issue right so if you're collecting monthly but you're paying 50 percent of your one questions up front yeah yeah yeah yeah sorry yeah so we the way that we agree our commissions is that they are that um you know we agree a schedule for paying them...