how i sold my company for eight figures at 19
trying to be as concise as possible.
1. the team matters most.
a startup does not need many people. it needs high talent density.
every extra person increases production capacity, but also the number of dependencies, meetings, and decisions to synchronize. a team of five has ten possible relationships. a team of twenty has 190.
the ideal team is small enough for information to travel instantly, but strong enough for each person to own a subject from end to end.
if someone tells me they are taking care of a technical problem, i should be able to assume it will be handled within an hour or by the end of the day, cleanly, without having to follow up.
in a startup, internal trust is infrastructure. without it, every decision requires another layer of validation. with it, the company moves at the speed of its best people.
2. in B2B, trust is part of the product.
this is especially true in tech, even more so in crypto, and absolutely critical when your infrastructure becomes the core of your customer's product.
the customer is not just buying an API. they are accepting a dependency.
if your infrastructure goes down, their product goes down. if your data is wrong, their users see the error. if you communicate badly during an incident, they carry your risk without knowing what is happening.
when a customer asks for a feature, reports a bug, or needs information, answer within a minute.
that does not mean solving every problem within a minute. it means confirming that you saw the message, identifying who owns the issue, giving an initial analysis, and saying when they will hear from you again.
a customer can accept an incident. they are much less likely to accept silence, vague answers, or deadlines that change without explanation.
reliability is not just an uptime percentage. it is also the ability to remain present, precise, and honest when something breaks.
this is what we always tried to do at Mobula. it is also what allowed us to build such a strong relationship with FOMO and with many other customers.
a competitor can replicate a feature. they cannot replicate years of trust in a few weeks.
3. do not confuse your current customers with your future market.
your customers understand their current problems very well. they do not necessarily know where your company needs to be in three years.
if you only build what they ask for, you will gradually improve the product they already use. you will not necessarily find the much larger market next to it.
this is what happened to us at Mobula.
for months, sometimes years, we focused on our existing customer base. we added data, integrations, and features that solved real needs.
but the economic depth of that market was limited. even with perfect execution, the ceiling remained low.
we should have invested much earlier in trading infrastructure: real-time data, routing, execution, security, and all the critical components trading applications directly depend on.
the problem was not the quality of what we were building. the problem was the size of the curve we were optimizing.
listen to your customers about the pain, but keep your own conviction about the direction.
ask where volume is moving, where budgets are growing, which features will become complete products, and which infrastructure will become impossible to remove once integrated.
if you capture 100% of your current market, is the outcome still large enough?
if the answer is no, working harder on the same product will not solve anything.
4. you do not know everything. try things.
we tried to build an explorer. we tried to compete with CoinMarketCap. we explored several products, distribution models, and markets.
many of those attempts failed.
the goal is not to avoid every failure. the goal is for each failure to buy enough information to improve the next decision.
in the beginning, you need to explore: ship quickly, measure, cut what does not work, and continue until one signal becomes stronger than the others.
but when that signal appears, your behavior needs to change. exploration becomes dispersion.
we eventually found trading infrastructure at the right time. from that point, we no longer needed ten new directions. we needed the whole team focused on this one.
many companies die because they focus too early on the wrong idea. others die because they keep exploring after finding the right one.
5. constantly watch your competitors.
a potential customer does not judge how much time you spent on your product. they compare what exists today.
a quick anecdote:
to close some of our customers, we simply recorded a Loom.
on the screen, two live Solana charts: Mobula on one side, our main competitor on the other.
the same transactions appeared on our side about 1.5 seconds earlier, consistently.
we were even faster than Axiom.
to the naked eye.
no sales deck, no complex benchmark, no promise about what the product would become six months later. you only had to look at the screen.
that is what it means to have an obviously better product.
coverage, latency, stability, data quality, documentation, pricing, support, integration time: on every important criterion, the customer should immediately understand why they should choose you.
test your competitors' products. read their documentation. measure their latency. look at what they ship, what they charge, and how they respond to customers.
if you are behind, accept it immediately. then multiply the pace by ten until you close the gap.
not by working ten times longer, but by reducing the time between every step: decision, development, testing, deployment, and measurement.
you cannot sell a product by asking the customer to believe it will become better later.
the difference should already be visible on the screen.
6. use AI.
for the past six months, no developer on my team has written a single line of code by hand.
AI produces the code. we define the systems, constraints, interfaces, tests, security invariants, and performance criteria. we review, measure, reject, and make it try again until we get the right result.
engineering does not disappear. it moves from writing to definition and verification.
the value of an engineer lies less and less in their ability to type an implementation quickly. it lies in their ability to formulate a problem precisely, choose the right architecture, detect a fragile solution, and prove that the system actually works.
a great team becomes much more productive with AI. a bad team simply produces bad decisions faster.
there is no moral reward for manually writing a function the model could produce in seconds.
what matters is the quality of the system shipped and the time it takes to put it into production.
7. speed needs to exist everywhere.
respond quickly to customers. fix quickly. try quickly. abandon quickly. observe quickly. decide quickly.
these are not separate qualities. they are the same culture applied in different places.
a startup has less capital, fewer people, less distribution, and less room for error than established companies.
its advantage is the time it takes to turn a fact into a decision, then turn that decision into a product.
being fast does not mean treating everything as an emergency. it means removing unnecessary waiting, giving every issue a clear owner, and never leaving a reversible decision blocked for a week.
at its core, the answer to the title fits in one sentence:
a small, very strong team that explored long enough to find a deep market, then executed fast and well enough to become a trusted dependency.
we were not right from the beginning.
we tried enough things, identified our mistakes quickly enough, then focused all our energy when we finally found the right direction.