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Entrepreneur Ventures is 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.
Zac Choi has spent two decades building and implementing data and AI systems for companies of every size, from mid-market SaaS darlings to global enterprises — and he saw a big problem coming before almost anyone else did. As AI adoption exploded, Choi predicted the technology would outpace most companies’ ability to actually use it, because their underlying data infrastructure simply wasn’t 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, Choi is going after an even bigger problem — making enterprise data legible for the AI agents that are about to become its primary users.
Dan Bova: What’s the elevator pitch for Big Context & Company?
Zac Choi: We’re heading into an era where the primary user of data will be an agent more so than a human. The infrastructure we’ve built our data estates on — whether you’re a small business or a large enterprise — 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’re 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
Can you break that down in simpler terms?
Data comes in from everywhere, at different levels of organized state. If you’re selling on Shopify or through a retailer, or you’ve got a POS system for your restaurant, every one of those sources is capturing data — some of it easy to use, some of it not — you have to become an expert of both the tool and the data underneath. That’s been the issue for the last three decades: data gets produced, but it’s 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 something means — it has no idea how to guess appropriately. That’s where a lot of AI implementations are falling short of expectations. It goes back to the old mantra: garbage in, garbage out. We’re basically the garbage men — going in, cleaning house, tilling the soil, so your data is ready for reliable use by AI.
What made you think this was the right business to build?
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 — structurally, they’re not compatible. Billions of dollars were going into AI projects, and nobody was cleaning their data fast enough. Second, it’s such a new technology that there’s a dearth of talent — not enough people have successfully implemented this before because it hasn’t existed before. You’d 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’s 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.
Where did that background come from — how did your career take shape?
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