In the crowded world of Web3 infrastructure, access to decision-makers at major financial institutions can feel like a locked vault. Most early-stage startups rely on warm introductions, expensive intermediaries, or sheer persistence to break through. Juan Solares, Chief Operating Officer at Essential, has taken a different path—one built on disciplined preparation and a hypothesis-driven approach to customer development that turns abstract market assumptions into concrete learning milestones.
The approach isn't revolutionary in theory. Customer development frameworks have existed for years. But Solares's execution—shaped by his background in Philosophy and entrepreneurship—demonstrates how structured methodology can level the playing field for resource-constrained teams competing in emerging markets.
The Challenge of Structured Discovery
Essential's challenge mirrors that of many 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 their operations or trading firms optimizing settlement. Without a framework to organize these variations, customer conversations risk becoming a collection of disconnected anecdotes rather than actionable intelligence.
Solares's response was to develop what he calls "learning milestones"—specific, testable statements about customer pain points that guide how Essential approaches potential customers. The team created a structured list of hypotheses, segmented by vertical, with each hypothesis tied to observable customer behavior.
"The real breakthrough came when we held alignment sessions before any outreach began," Solares explains. "Everyone on the team needed to share the same assumptions about what we were testing and why." The sessions transformed how Essential's team prepared for customer discovery. Each hypothesis became a learning goal with clear success criteria, enabling the team to synthesize insights across multiple conversations rather than treating each interaction as a one-off event.
Testing Theory Against Reality
At a major crypto and fintech conference, Solares put the framework to the test. Speaking with three distinct customer profiles—traditional banks, yield-bearing stablecoin projects, and a crypto trading firm—he conducted what amounted to rapid-cycle experiments on Essential's value propositions.
The compressed feedback loop proved invaluable. "I could test a value proposition with a bank executive at 10 AM, adjust my framing based on what I learned, and test the refined version with a stablecoin founder at 2 PM," Solares reflects. Two of those conversations converted into concrete follow-up calls, but the real value emerged in the details that surface-level research couldn't capture.
The trading firm's intense focus on latency hadn't appeared in Essential's initial hypotheses. The stablecoin project's regulatory transparency requirements were more nuanced than anticipated. These weren't minor variations—they were insights that reshaped Essential's technical roadmap. "That's the value of hands-on outreach," Solares notes. "You learn what you didn't know to ask."
The Mechanics of Access
The methodology's power became evident when Solares secured a conversation with the Head of Blockchain and Crypto at one of the largest U.S. banks—the kind of access that typically requires either significant reputation or expensive intermediaries. The breakthrough came down to preparation and framing.
Rather than pitching Essential's product, the outreach shared insights from validated hypotheses about specific challenges facing similar institutions and offered to test whether solutions might address this particular bank's variation of those challenges. "The message positions us as thought partners, not vendors," Solares explains. "It shows we've done our homework and respect their time."
The preparation process is methodical: research into the prospect's business model, regulatory environment, and public statements about blockchain strategy, followed by identification of which validated hypotheses likely apply. The result is outreach that's relevant, timely, and grounded in demonstrated market understanding rather than generic value propositions.
Building Institutional Memory
For Solares, the operational discipline extends beyond individual conversations. After every significant customer interaction, Essential conducts rapid debriefs that explicitly document which hypotheses were validated, which were invalidated, and what new questions emerged. That documentation becomes institutional knowledge that informs product decisions months later.
The approach creates unusual transparency with investors. Rather than updates about "customer interest" or "pipeline development," Solares provides concrete data on hypothesis validation rates and how customer insights translate into product decisions. "Investors don't just want to know we talked to customers—they want to know what we learned and how that learning changed our trajectory," he observes.
The framework also enables Essential to prioritize conversations based on learning value rather than deal size alone. "Sometimes the most valuable conversation isn't with the biggest prospect," Solares notes. "It's with the prospect whose needs most clearly represent where the market is heading."
The Competitive Edge of Systematic Learning
As Web3 infrastructure matures, companies face a fundamental challenge: how to understand customer needs in markets that don't yet fully comprehend their own requirements. The ability to scale customer insight without proportionally scaling headcount—to learn faster rather than louder—increasingly determines which startups survive the transition from early adoption to mainstream viability.
Solares's playbook suggests that systematic approaches to customer development function less as bureaucratic overhead and more as competitive advantages for lean teams. The discipline forces clear thinking about what the company knows, what it assumes, and what it needs to learn. Each validated hypothesis compounds into better market understanding; each invalidated one prevents wasted engineering effort.
"We're in a race to understand customer needs before the market fully crystallizes," Solares reflects. "The companies that win won't necessarily be the most technically sophisticated—they'll be the ones that most accurately map their capabilities to real customer pain points."
The hypothesis-driven approach doesn't eliminate uncertainty—it structures how teams navigate it. For Essential, that structure has translated into access that seemed improbable, insights that have reshaped product direction, and a learning velocity that enables a small team to compete with better-funded competitors. The framework's true test will come as Essential scales, but the early results suggest that treating customer development as a systematic discipline rather than an organic art may be one of the few sustainable advantages available to startups operating in emerging technology markets.
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This article is published under HackerNoon’s Business Blogging program.