I was the company's first US-based hire. The first job was not hiring sales. It was figuring out who would buy, why now, how to enter, and turning that judgment into positioning, channels, partnerships, and an operating playbook others could run.
I joined Dify in January 2025 as the first US-based hire. Demand was already there: a sizable open-source community, global downloads still growing, and inbound interest from North American companies. What the team did not yet have was someone in the right time zone to catch those signals.
What was missing was the North America playbook: who the paying buyer was, why they would buy, how the first meeting should be run, and how open-source users could become enterprise customers. Without answers to those questions, sales activity would drift.
The Asia and Europe motions could not simply be copied. The buyer, procurement rhythm, compliance expectations, and partner ecosystem were different. My first job was to make the market legible through customer conversations, channel feedback, field events, and partner intelligence.
The default answer inside the company was: North America needs sales. That was not wrong, but it was too late in the sequence. Sales only works if you first know who will pay, why they will pay, and what language they can trust.
Dify's early influence came from open-source users, especially individual developers. They built the global brand. In North America, however, the people signing contracts were different: IT leaders, AI platform owners, and automation buyers inside larger companies.
Those two audiences care about different things. Open-source users care about speed, flexibility, and what feels new. Enterprise buyers care about governance, compliance, traceability, long-term maintenance, and whether they can take responsibility after rollout.
So the real problem was not "sell more." It was to identify the paying buyer first, then rewrite positioning, materials, and sales motion around that buyer. If that step was wrong, hiring, events, and content would all compound the wrong assumption.
I started from who could sign a contract, not who was using the product. The target buyer was an IT or platform leader inside a mid-to-large enterprise, often pulled into AI platform ownership before the organization had a clear plan. They were understaffed, flooded with internal requests, and more afraid of a failed rollout than of missing the newest tool.
For that buyer, I rewrote the narrative and sales materials around governance, traceability, long-term maintainability, and internal credibility. This was not copywriting. It was repositioning the product from a developer tool into an internal enterprise platform.
The open-source community and real usage were the trust entry point in North America. Without that proof, unfamiliar enterprise buyers would not take the meeting. But moving from individual usage to an enterprise contract required a different motion: sales follow-up, co-creation with customers, compliance material, and security review.
I separated the two systems. Open source built trust and generated leads; sales converted the right leads into enterprise procurement. They shared the top of the funnel, but the downstream work was different.
A Series A company headquartered outside the US rarely wins a first meeting on brand alone. It wins when someone the buyer already trusts is willing to introduce, explain, or vouch. I mapped cloud ecosystem partners, systems integrators, ecosystem advisors, and potential co-creation customers, then ranked them by one practical question: can this relationship help us meet the right buyer faster?
By the time I moved into a strategy and partnerships role, the map covered 50+ North American AI, developer-tool, and cloud ecosystem nodes, each with an owner, a next step, and a follow-up rhythm.
The most important artifact was not a pitch deck. It was a fixed cadence: weekly market intelligence and a biweekly strategy memo for founders and investors. The inputs came from customer calls, open-source community feedback, partner conversations, and competitive moves.
That memo became material leadership actually used to make decisions. It also made the work transferable once the team grew.
For an early market, the important question is not how much one person can personally do. It is whether the work can keep running after another person picks it up. Five outputs became durable.
The value of early-market work is not only making something happen once. It is turning the work into a system other people can keep executing.
The test of an early operator is not what they shipped once. It is whether the system they left behind still produces decisions without them.
The numbers proved North America was not a theoretical market. The work produced closed revenue, clear North America-sourced recurring revenue, and a partner network that could keep moving.
The more important change happened inside the organization. The biweekly strategy memo became something the founders and lead investor opened regularly. Market information stopped living only in scattered chats and started entering company decision-making.
After I changed roles, the operating document kept running. Someone else writes it now. That matters more than a single metric, because it proves the work was not dependent on one person; it had become a system.
When I joined, I thought the job was mostly about closing deals and building a partner pipeline. Both mattered. But the core work was turning scattered market signals into a fixed rhythm.
Every two weeks, customer conversations, partner feedback, competitor moves, and internal questions had to become a document people could discuss. Once the document became stable, founders could use it to make decisions, the team could use it to take over work, and market judgment no longer lived only in one person's head.
Being first-in means creating the operating rhythm before anyone has a name for it. Once the rhythm becomes company memory, the seat can be handed to someone else.