Hitting $10k+/mo by using an agency as both testing ground and distribution

After experiencing both success and failure as an indie hacker, Arsen Ibragimov built an AI product and used his cofounder's agency as the testing ground. Now, Topliner is bringing in five figures per month.

Here's Arsen on how he did it. 👇

Developing a philosophy

I’ve been building things for a long time — way before I knew what a "startup" was.

In high school, I wrote small programs in Pascal and Delphi. I built utilities that solved real pain for classmates, and I even sold them to university students. Around the same time, I did dropshipping, but that was before it had a cool name. It was just: Find demand, find supply, don't hold inventory, move fast.

That early phase shaped my philosophy:

• Software is leveraged only if it removes friction.

• Distribution matters as much as the product.

• Cashflow teaches you faster than theory.

Initial entrepreneurial wins

My first serious business was e-commerce. We grew it to $1M revenue, and I sold my stake in 2014.

Then, I bootstrapped a MarTech SaaS for Instagram. It hit $1M ARR and 10k daily users, but I had to shut it down due to platform risk. That hurt, but it taught me a lesson: Never build your house on someone else’s land.

Then came the wildest chapter: I built a consumer fitness app with Khabib Nurmagomedov. We raised ~$800k and had massive distribution. But we failed. That failure taught me more than any success.

With the Khabib app, we had ultimate fame but weak retention. With my current company, Topliner, I inverted the model: Solve a boring, painful B2B problem where the product is the workflow.

Building to five figures and beyond

I wake up each morning with one thought: I want to create impact. The bigger the surface area, the better.

Executive search is the perfect place for this. It is a wealthy market, but the tech is outdated. Workflows are fragmented. People burn out doing manual tasks.

The real trigger for getting started, though, was GPT-4. My cofounder and I saw it and realized: "This changes the economics of work."

Our first idea was small: a curated database of top CFOs in the DACH region. But as we started building, we realized that building a database is building a static map of a moving world. We didn't need a map. We needed an engine that mapped the world in real-time.

So, today I’m building Topliner. It is an AI-native Operating System for Executive Search agencies. We built it in close partnership with The Big Search, a leading boutique agency in Europe.

We are currently in the five-figure monthly revenue range and growing steadily.

Starting with one problem

We started with the most boring part: company research.

Research is the "engine room" of executive search. It usually takes weeks of jumping between LinkedIn, Google, and spreadsheets. Worse, many agencies rely on their old internal databases and networks. But if you do that, you aren’t hiring from the market. You are hiring from your memory.

So we focused our AI on research first. The results were shocking:

• We reduced 6 weeks of manual research work down to 6 hours.

• Quality went up (fewer blind spots).

• Unit economics improved drastically.

Our V1 was simple: You describe a role. Topliner finds relevant companies and enriches them with data (headcount, funding, customers).

Black box vs glass box

At first, we were naive. We thought: "User gives a prompt -> AI does magic -> User gets a candidate." A black box.

We quickly realized nobody trusts a black box in high-stakes hiring. If the AI says, "This is the best CTO for you," the recruiter needs to know why. Maximum transparency. Evidence for every claim. The human can intervene at any step.

AI shouldn't replace the expert; it should give the expert superpowers. So we built for that.

We also had zero tolerance for hallucinations. In a consumer app, a bug is annoying. In executive search, a hallucination is a disaster. If the AI invents a fact about a candidate, we lose trust. If it misses a candidate, we lose the outcome.

So, we spend significant time on guardrails. The human always makes the final decision. Our job is to make the human faster and sharper, not to force blind trust.

A pragmatic stack

We keep the stack pragmatic and shipping-first:

• Backend: Node.js and PHP (Laravel)

• Frontend: ReactJS

• Data: MySQL

• Caching/queues: Redis

• AI: OpenAI and xAi models + our own orchestration layer for hundreds of AI agents

• Infra: Azure + OVH + internal services for enrichment and processing

Organic growth

We reached five figures per month with zero ad spend.

We have three main drivers for growth:

1. Distribution through The Big Search. We had a testing ground with real clients immediately. We didn't need to hunt for beta testers.

2. High-signal content. I don't post "5 tips for hiring." I post forensic breakdowns of how talent markets move. I treat recruiting as engineering, not HR. This attracts the right people: founders, operators, and partners.

3. The founder network. Founders talk to founders. When people see how fast we execute, they ask, "How?" Intros happen naturally. These conversations start with trust, not cold outreach.

Building from the inside

Building this inside a real agency (The Big Search) was the biggest unlock.

Most SaaS founders have to guess what the customer needs. We don't guess. We deploy Topliner inside The Big Search. It has to survive real mandates and real deadlines every day.

Every search mandate was a product test. Every "this feature is annoying" was a bug report. We didn't have to guess what users wanted. They were sitting right next to us.

Also, the partnership dynamic helps. Learco handles the business, relationships, and delivery pressure. I handle the system and product. The split is clean, and we move fast.

A two-stream business model

As far as our business model, we keep it simple with two streams:

1. Platform revenue: Agencies and partners use Topliner as their infrastructure. We take a revenue share on the search work executed through the platform.

2. Talent market mapping: We sell high-precision market intelligence to VCs, PE funds, and Enterprises. We show them exactly what the talent pool looks like for a specific role, faster and deeper than any traditional firm can. We’ve already worked with companies like Miro, Dutch Amazon BOL, Flo Health, and funds like Permira, Prosus, N47, and Atomico.

Unfair access to reality

Here's my advice: Start where you have unfair access to reality. Use your own workflow, your own data, or your partner's problems. Don't build for an imaginary customer.

Don’t aim for "a product people like." Aim for "an outcome people pay for."

And ship into real constraints early. Deadlines and paying customers are the best product managers in the world.

What's next?

Here's what I have planned:

• Near-term: Make Topliner the default workflow for every partner inside The Big Search.

• Medium-term: Package the OS so other boutique firms can use it. We want to power a "house of brands" — distinct agencies all running on our engine.

• Long-term: Executive Search seems boring to many indie hackers. But think about it: The people these firms place run the companies that shape our future. By building the OS for this industry, we aren't just saving recruiters time. We are making sure the right leaders find the right seats faster. If we do this right, we increase the velocity of innovation globally. That’s a mission worth waking up for.

You can follow along on LinkedIn and X. And check out Topliner!