Building an AI Startup in New York as a Foreigner
What five years bought
He spent five years at the financial management platform most used by small businesses in America. The main customers were service businesses: cleaning, landscaping, HVAC. Five years of direct contact taught him their workflows inside out. The important part: what those years built was not technical skill but domain knowledge. Which trades work in which order, where the bottlenecks form, what gets handled manually. He never wrote a line of code, and those five years became the foundation of the company.
Everyone repeats the same manual work
He found a pattern: small service businesses all follow the same flow. An inquiry comes in, they respond, negotiate, produce a quote, get the contract signed, send the invoice, collect the payment. Cleaning, landscaping, HVAC, identical. Customers ask nearly the same questions, and owners answer in nearly the same ways. All manually.
Some hire virtual assistants overseas to manage this, with time zones, messenger-only communication, and frequent mistakes, and even that passes for the efficient option. There he saw the opening: what if an AI agent took over this repetitive manual workflow, no time zone, no separate messenger, AI working alongside the owner.
From everything to one thing
The first concept was automating the whole workflow, inquiry to collection. But starting with small businesses, expectations ran too high.
If you are replacing my virtual assistant, you have to do everything my assistant did.
Covering everything meant too many use cases and an uncontrollable product surface. Then, through a private equity firm, he met a customer: a cleaning services company with 200 franchises across the US and Canada, struggling with customers paying late after invoicing, and paying a separate overseas team of four to chase receivables manually over email.
That is where the direction turned: instead of the whole workflow, do one thing at the very end, collections, and do it properly. From doing everything to doing one thing well. The essence of the job is simple: send the right message at the right time and follow up based on the response. Exactly the kind of work an AI agent does best.
The market of 1998 software
The target changed too. He had aimed at small businesses, but the actual paying customers turned out to be mid-sized companies above 30 million dollars in revenue, too big for his former platform, and not modern SaaS users either. One customer was running the business on a 1998 version of its software.
This is the crux: legacy industry, legacy software, legacy process. All manual, all old. And that oldness is exactly the opportunity for AI agents. Modern tech companies already automate internally or can build it themselves. A company on 1998 software has no internal capacity to adopt AI at all. That gap is the market.
An angel round with no product
He raised angel money before the product existed: 300 thousand dollars for 9 percent, a 3.3 million valuation, standard for Silicon Valley angels, he said. The team is two people: himself as CEO and a computer science engineer co-founder as CTO, the pairing of an engineer and a domain expert. The striking part is raising without a product; the domain expertise, market understanding, and problem definition were persuasive enough. Hiring will wait until ten paying customers and a proper round, and even then, only people who use AI well.
Customer discovery is still manual
He is building an AI agent company, and finding customers is thoroughly manual. The first customer came through a private equity introduction, the second through a PE partner, the third through the MBA network, the fourth through the previous client of an investor. Nearly every lead arrived by introduction. Private equity became the core channel: funds invested in franchises want their portfolio companies to run more efficiently, and the interests align exactly there.
When abroad becomes local
We asked about founding in the US as a foreigner. The answer came in two parts. One: use the experience and knowledge of the domain you were in as leverage in the AI era; the more legacy the field, the bigger the opportunity, and the stronger it gets when it touches pain points only locals know. Two: the moment abroad becomes local. Once the visa and immigration hurdles are behind you, people adapt quickly. What is harder may be the living part, opening accounts, signing a lease, setting up a phone, dissolving into the system of daily life. It looks separate from the business, and it is exactly the process that makes you local.
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This post is based on a conversation with a founder who worked as a product marketer in Silicon Valley before moving to New York to start an AI company.
