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Entrepreneur Ventures<\/a>\u00a0is an early-stage venture capital firm partnered with Entrepreneur Media that is dedicated to backing passionate and innovative founders as early as day one. In this series, we are profiling the amazing entrepreneurs Entrepreneur Ventures is working with to share their insights on building and growing a thriving business.<\/em><\/p>\n Zac Choi<\/a> has spent two decades building and implementing data and AI systems for companies of every size, from mid-market SaaS darlings to global enterprises \u2014 and he saw a big problem coming before almost anyone else did. As AI adoption exploded, Choi predicted the technology would outpace most companies\u2019 ability to actually use it, because their underlying data infrastructure simply wasn\u2019t ready. That instinct has already paid off once: he built and sold his first startup, String AI, to a telecom partner. Now, with Big Context & Company<\/a>, Choi is going after an even bigger problem \u2014 making enterprise data legible for the AI agents that are about to become its primary users.<\/p>\n Dan Bova: What\u2019s the elevator pitch for Big Context & Company?<\/strong> Can you break that down in simpler terms?<\/strong> What made you think this was the right business to build?<\/strong> Where did that background come from \u2014 how did your career take shape?<\/strong> You\u2019ve raised multiple venture financing rounds and also sit on the investor side of the table. What have you learned about fundraising?<\/strong> What are the most common mistakes you see first-time founders make?<\/strong> How do you approach problem-solving, whether it\u2019s a business dilemma or something going wrong?<\/strong> Entrepreneur Ventures<\/a>\u00a0is an early-stage venture capital firm partnered with Entrepreneur Media that is dedicated to backing passionate and innovative founders as early as day one. In this series, we are profiling the amazing entrepreneurs Entrepreneur Ventures is working with to share their insights on building and growing a thriving business.<\/em><\/p>\n Zac Choi<\/a> has spent two decades building and implementing data and AI systems for companies of every size, from mid-market SaaS darlings to global enterprises \u2014 and he saw a big problem coming before almost anyone else did. As AI adoption exploded, Choi predicted the technology would outpace most companies\u2019 ability to actually use it, because their underlying data infrastructure simply wasn\u2019t ready. That instinct has already paid off once: he built and sold his first startup, String AI, to a telecom partner. Now, with Big Context & Company<\/a>, Choi is going after an even bigger problem \u2014 making enterprise data legible for the AI agents that are about to become its primary users.<\/p>\n Dan Bova: What\u2019s the elevator pitch for Big Context & Company?<\/strong>
Zac Choi:<\/strong> We\u2019re heading into an era where the primary user of data will be an agent more so than a human. The infrastructure we\u2019ve built our data estates on \u2014 whether you\u2019re a small business or a large enterprise \u2014 has been predicated on human users asking predictable questions. Big Context is essentially helping make those data estates legible for AI-scale work. Before you can implement real AI in your company, you have to till the soil, and that soil is generally your data. We\u2019re an AI-native services company, which means we deliver these transformation services at near-software margins, leveraging AI and technology to do the traditionally unscalable work of services<\/p>\n
Data comes in from everywhere, at different levels of organized state. If you\u2019re selling on Shopify or through a retailer, or you\u2019ve got a POS system for your restaurant, every one of those sources is capturing data \u2014 some of it easy to use, some of it not \u2014 you have to become an expert of both the tool and the data underneath. That\u2019s been the issue for the last three decades: data gets produced, but it\u2019s still pretty clunky to use. Now you add AI, which is really just conversational retrieval of data. But when an AI agent looks at a messy data estate, it can only assert what it thinks<\/em> something means \u2014 it has no idea how to guess appropriately. That\u2019s where a\u00a0 lot of AI implementations are falling short of expectations. It goes back to the old mantra: garbage in, garbage out. We\u2019re basically the garbage men \u2014 going in, cleaning house, tilling the soil, so your data is ready for reliable use by AI.<\/p>\n
I think of it in two parts. First, going top-down, there is a perfect storm of market opportunity. I predicted AI implementations would fail before the first studies came out, because LLMs are a probabilistic tool and data work is deterministic \u2014 structurally, they\u2019re not compatible. Billions of dollars were going into AI projects, and nobody was cleaning their data fast enough. Second, it\u2019s such a new technology that there\u2019s a dearth of talent \u2014 not enough people have successfully implemented this before because it hasn\u2019t existed before. You\u2019d have to have been working on this for the last 2 to 3 years AND already been an expert in data infrastructure. And honestly, it\u2019s something my team and I have deep expertise in: I studied data science at Wharton, spent 20 years building and executing data and AI services, including large data transformation efforts at McKinsey, and my last startup was a production-grade AI product helping small and medium businesses triage their incoming texts and voicemails.\u00a0<\/p>\n
My parents were immigrants from Korea who came here in their early 20s. My dad\u2019s first job \u2014 around the time my mom was pregnant with me \u2014 was driving around New York City delivering fruit to grocery stores. Humble beginnings. My dad was a quantum physicist and my mom was a child psychologist who traded her career to raise my brother and I. I studied Cognitive Science at UPenn, and my first job out of college was doing research for Wharton, mostly stats and data science. That led to General Mills, then Clorox, where I led a team helping big-box retailers grow entire product categories \u2014 this was \u201cforward deployed\u201d work before it became popular in Silicon Valley. From there, I went to Green Dot, a fintech company, where I built their BI, trade and rev ops functions from the ground up through their IPO. After that, I went to McKinsey, where I led data transformations and eventually launched a service line helping mid-market enterprise tech companies \u2014 many of whom went on to become unicorns \u2014\u00a0 innovate, several years before their exits. Watching their trajectory is what convinced me to bet on myself. I left to build String AI, which got acquired by a telco about four and a half years later. A year after that, I started what\u2019s now Big Context & Company.<\/p>\n
I\u2019ve been fortunate to raise several rounds for several companies, and I\u2019ve also been an advisor and board member to a number of startups. I\u2019m always looking for ways to help other builders and operators too \u2014 and actively advise other startups, operators, and even creators on ways to transform their audience into business. In 2024, I met Alex and Leila Hormozi, who run acquisition.com, and convinced them to start a venture fund together \u2014 I\u2019ve made more than 30 investments over the last two years and seen a couple thousand deals a year. My take: if you have founder-market fit, that\u2019s a great start. In the past, ideas were cheap and building was hard. Now, building has become cheap too, so everyone\u2019s building something now \u2014 which means you need some deep expertise, a real pain point attached to large value pools, and the ability to convince investors and buyers that you\u2019re the one for the job. I\u2019ve noticed that VCs are scrambling to figure out where to invest and at what level of conviction given how quickly things are changing with AI. So, another thing investors weigh is how much of their capital you\u2019ll burn on rookie mistakes, which is why there\u2019s a bias toward second-time founders. It was much easier to raise for my second company than my first.<\/p>\n
Team is number one. Typically in software, most of the capital you burn through is talent \u2014 who you surround yourself with, the energy and passion they bring. My co-founder Joji John is also a 20+ year veteran and was a pioneer in BI & data warehouse technology, led AI & Data at Rakuten and most recently built applied knowledge graphs for AI analytics. When you can get the right team in place against a massively urgent & difficult problem, that helps immensely, but you also want to make sure that you can be agile together, make decisions quickly, and have a way forward when disagreements inevitably arise. I\u2019ve been in situations where two people produced more than a team of six or seven. Alignment is the other piece: there\u2019s an unwritten wall around the 15-to-20-month mark where tension develops, usually because someone isn\u2019t satisfied with the output they\u2019re getting for the input they\u2019re putting in \u2014 and that\u2019s hard to discover on day zero. The second big mistake is distribution. If it\u2019s easy to create supply for what you\u2019re producing (especially these days when AI is leveling the playing field), distribution becomes the moat. A lot of first-time founders focus too much on product and too little, too late, on distribution. My advice is to treat the market, or customer, like another co-founder. In my second company, I was selling the offering before I\u2019d written a single line of code.<\/p>\n
I call it the Zero-One-Two-Three framework: zero-based, first principles, second opinions, third eye. Zero-based means clearing my head of preconceived notions \u2014 a VC told me the idea sucked, a customer says that\u2019s the way it\u2019s always been done \u2014 and getting crisp on what the actual problem is and who it solves it for, because some problems seem interesting but aren\u2019t worth solving at all. First principles is how we were trained to think at McKinsey: break down the problem to its roots \u2013 ask yourself why, over and over, instead of accepting inherited constraints that don\u2019t actually apply to your problem. And second opinions matter, and not just from ChatGPT or Claude, because AI conversations can become an echo chamber \u2014 if you keep a conversation going long enough, it starts to drift, and you can talk yourself into believing you\u2019re right. You need a body of people you trust who can think clearly alongside you and can play devil\u2019s advocate too. On top of all that, you develop your third eye: i.e., it\u2019s much better to make a directionally correct decision quickly than to wait for the perfect answer. Over time, with this framework, you develop a real intuition and your decisions tend to be better more often. You\u2019re going to be wrong sometimes, but since you have to move fast, you need to get your shots on goal up \u2014 and that\u2019s a discipline you can build.<\/p>\n<\/p><\/div>\n
Zac Choi:<\/strong> We\u2019re heading into an era where the primary user of data will be an agent more so than a human. The infrastructure we\u2019ve built our data estates on \u2014 whether you\u2019re a small business or a large enterprise \u2014 has been predicated on human users asking predictable questions. Big Context is essentially helping make those data estates legible for AI-scale work. Before you can implement real AI in your company, you have to till the soil, and that soil is generally your data. We\u2019re an AI-native services company, which means we deliver these transformation services at near-software margins, leveraging AI and technology to do the traditionally unscalable work of services<\/p>\n<\/p><\/div>\n