In the rapidly evolving Web3 infrastructure landscape, where customer needs shift as quickly as the technology itself, most companies struggle with a fundamental problem: they’re building solutions before they truly understand the problems. Juan Solares, Chief Operating Officer of Essential, has taken a different approach—one that replaces assumption-based sales with rigorous, hypothesis-driven customer discovery.
Juan’s background suggests why this methodology comes naturally. After earning a degree in Philosophy from the University of Dallas, followed by an MBA from Babson College in 2021, Solares developed an unusual combination of technical depth and analytical rigor. That intersection—understanding both infrastructure architecture and data-driven validation—shaped how he would later approach market discovery at Essential.
The Infrastructure Paradox
Solares’s journey into Web3 began with a recognition shaped by his healthcare background. “Infrastructure is invisible when it works perfectly, but catastrophic when it fails,” he reflects. “That principle guided my entire approach to understanding what builders truly need from Web3 infrastructure. We couldn’t afford to guess—we needed a systematic method to uncover the real problems.”
At Essential, a company building infrastructure for the next generation of Web3 applications, Solares faced a challenge common to infrastructure companies: potential customers often struggle to articulate their needs until they experience the pain directly. Banks exploring blockchain integration face different obstacles than stablecoin projects scaling operations or trading firms seeking faster settlement. Without a framework to organize these variations, customer conversations risk becoming disconnected anecdotes rather than actionable intelligence.
Building a Testable Framework
Rather than conducting unfocused customer interviews or building features based on internal assumptions, Solares led his team in developing a structured hypothesis framework for customer discovery. The team created a comprehensive list of hypotheses, organized by vertical, with each hypothesis tied to specific, observable customer behavior.
“We started by acknowledging that we didn’t know everything about our customers’ pain points,” Solares explains. “Each hypothesis was specific, testable, and tied to observable customer behavior.”
The framework required disciplined segmentation. Banks faced regulatory compliance challenges and legacy system integration issues that differed fundamentally from the scalability concerns of high-frequency trading firms. Stablecoin projects wrestled with transparency requirements and cross-chain interoperability that weren’t priorities for other verticals. By categorizing hypotheses according to these segments, Essential could conduct targeted conversations that yielded actionable insights rather than generic feedback.
What distinguished this approach was the emphasis on alignment before action. “Before anyone on the team reached out to a single prospect, we held alignment sessions,” Solares notes. “Everyone needed to understand not just what we were testing, but why. If different team members operated from different assumptions, we’d get conflicting data that would be impossible to synthesize.”
From Guesswork to Validated Learning
The hypothesis-driven model transformed Essential’s customer discovery from an art into a science. Each customer conversation became a structured experiment designed to validate or invalidate specific assumptions. Solares implemented a feedback loop where insights from early conversations informed refinements to later hypotheses, creating an iterative learning cycle.
The results often surprised the team. “We discovered that trading firms cared far more about transaction finality guarantees than we initially assumed, while banks were surprisingly flexible on certain technical specifications if we could demonstrate regulatory compliance,” Solares shares. “These weren’t insights we could have predicted—they emerged only through systematic testing of our hypotheses against real customer experiences.”
The methodology also revealed unexpected market segments. By analyzing which hypotheses resonated across multiple verticals, Solares’s team identified cross-cutting pain points that represented broader market opportunities. This led Essential to prioritize infrastructure features that served multiple customer types, accelerating both product development and market penetration.
Translating Technical Promises into Business Problems
Solares’s approach addresses a fundamental challenge in emerging technology sectors: how to build for markets that don’t yet fully understand their own needs. His insistence on hypothesis-driven customer discovery creates a documented trail of assumptions, tests, and learnings that becomes institutional knowledge as the company scales.
“In Web3, everyone talks about decentralization and trustlessness, but those are abstract concepts,” Solares observes. “What matters is understanding how a CFO at a bank thinks about settlement risk, or how a stablecoin issuer measures operational efficiency. Our hypothesis framework forced us to translate Web3’s technical promises into language that addressed real business problems.”
The structured approach also enables Essential to pivot efficiently when market conditions change. Because every initiative begins with explicit hypotheses, the team can quickly identify which assumptions no longer hold and adjust course without organizational confusion. This agility proves particularly valuable in Web3, where regulatory shifts and technological breakthroughs can reshape entire market segments within months.
The methodology cultivates what Solares calls intellectual humility within the organization. “The moment you write down a hypothesis, you’re admitting it might be wrong,” he explains. “That creates a culture where being wrong isn’t failure—it’s progress. We learn as much from invalidated hypotheses as from validated ones, maybe more.”
Building What Actually Matters
Beyond the tactical benefits, Solares’s framework reflects a broader philosophy about how infrastructure companies should approach their markets. By deeply understanding customer segments before building, Essential can allocate engineering resources to features that will actually be adopted, rather than impressive technical achievements that solve problems customers don’t have.
“Web3 is full of technically elegant solutions searching for problems,” Solares notes. “Our job is to reverse that equation—start with validated problems, then engineer elegant solutions. The hypothesis framework keeps us honest about which problems are real and which are just interesting engineering challenges.”
This customer-centric orientation extends to Essential’s product roadmap, where every major initiative traces back to validated customer hypotheses. The company maintains a living document that maps features to specific pain points and the customer segments that experience those pains. When prioritizing development sprints, teams reference this document to ensure alignment between engineering effort and market need.
The Compounding Effect of Structured Learning
As Essential continues building infrastructure for the next generation of Web3 applications, Solares’s hypothesis-driven playbook offers a template for how emerging technology companies can navigate uncertainty without paralysis. The approach demonstrates that systematic customer discovery processes aren’t bureaucratic overhead—they’re competitive advantages that accelerate learning and reduce waste.
“We’re still early in understanding what Web3 infrastructure needs to become,” Solares reflects. “But we’re less early than we were six months ago, and we’ll be less early six months from now. That’s the power of structured learning—it compounds. Every validated hypothesis makes the next one easier to form and test.”
For Solares, success in Web3 infrastructure isn’t measured by technological sophistication alone, but by the alignment between what gets built and what customers actually need. His methodical approach—grounded in engineering training, sharpened by business analytics education, and refined through real-world application—positions Essential to build infrastructure that doesn’t just impress developers, but solves the practical challenges that will bring Web3 mainstream. The question isn’t whether the approach will influence how the next generation of infrastructure companies operate. It’s whether they’ll adopt it before their competitors do.
Photo by LinkedIn Sales Solutions; Unsplash
