I Quit Reading Analyst Reports and Built an AI That Does It Better — Here's What 3 Months of Solo Building Taught MeI have a confession.

I'm not a finance guy. My degree is in civil engineering, and my career has been in marketing communications. I've never worked at a hedge fund, never had a Bloomberg Terminal subscription, and I certainly never thought I'd end up building a stock analysis platform that processes 1,800 news sources daily.

But here we are.

The Frustration That Started Everything
Late 2025. I'm sitting at my desk, three browser tabs deep into an SEC filing for a mid-cap company I was researching. Another tab had a 47-page analyst report. Another had some guy on Reddit confidently saying the stock was "going to the moon" with zero evidence.

I remember thinking: this is broken. Not the stock market — the way regular people interact with it.

If you're a retail investor, your options are basically: pay $25,000/year for a Bloomberg Terminal, subscribe to five different services that each give you a piece of the puzzle, or scroll through Reddit and hope for the best. There's no middle ground.

And then it hit me — AI can summarize a 100-page filing in 30 seconds. It can cross-reference earnings data with sentiment analysis in milliseconds. Why are we still doing this manually?

So on December 5, 2025, I opened Replit and started building.

What I Actually Built
Stock Expert AI is, at its core, a translation layer. It takes the dense, jargon-heavy world of institutional finance and makes it readable. Not dumbed down — just clear.

The centerpiece is something I call the MoonshotScore. It's a 0-100 rating that synthesizes over 50 data points across nine factors: revenue growth, gross margin, operating leverage, cash runway, R&D intensity, insider activity, short interest, price momentum, and news sentiment. Each factor is weighted based on how institutional analysts actually think about stock evaluation.

It's not a "buy" or "sell" signal. It's a structured way to look at a company and understand where it stands, without needing to read three earnings reports and a 10-K filing.

Beyond that, there's a portfolio scanner that lets you upload a screenshot of your brokerage account and get instant diagnostics. A daily market journal that distills those 1,800 sources into about 25 original stories. A conversational AI assistant called Moon AI that can pull real-time data and explain what's happening with any stock in plain English.

And there's the weird stuff I built because I couldn't help myself — like the "Legends Council," which simulates how six different investing legends would evaluate a stock using their actual documented philosophies. Want to know what a Ray Dalio-style macro analysis says about Tesla? Or how a Seth Klarman value lens views a biotech startup? You can actually do that.

The One-Person Team (Sort Of)
Here's where it gets interesting from an indie hacker perspective. I'm one person. But the platform runs like it has a team of ten.

Early on, I realized I couldn't manually curate news, update stock data, generate analysis, write market summaries, monitor data quality, AND build new features. So I did what any reasonable person would do — I built AI agents to handle it.

There's an agent that monitors 1,800+ news sources and extracts the signal from the noise. Another that runs quality checks on the AI-generated content and flags anything that smells like a hallucination. Another that handles the entire content distribution pipeline — writing, formatting, publishing.

Each agent has a specific role and domain. They run autonomously on schedules. I wake up, check the ops dashboard, and most of the time everything just... worked overnight.

This is the part that still feels surreal to me. A single person running a platform that covers 6,000+ U.S. stocks with institutional-quality analysis. Not because I'm some genius — because the tools available to indie hackers right now are genuinely unprecedented.

The Tech Behind It
For the builders reading this, here's the stack:

Frontend: React + TypeScript + Vite, with a component library built on shadcn/ui. The design language is intentionally dense — inspired by Bloomberg Terminal aesthetics but with a modern dark theme.
Backend: Node.js + Express + TypeScript, running on Replit.
Database: PostgreSQL with Drizzle ORM, plus pgvector for semantic search across news articles.
AI: A custom LLM router that manages requests across OpenAI, Google Gemini, and Anthropic. It has automatic fallback logic — if one provider is down or slow, it routes to the next. This was critical for reliability.
Data: Financial Modeling Prep as the primary data source, with Alpaca for real-time market data and Yahoo Finance as a fallback layer. Benzinga WebSocket feeds for live news.
SEO/SSR: Custom server-side rendering for every stock page. Each of the 6,000+ stock pages gets a unique, dynamically generated SSR response with structured data, FAQ schema, and full content — not a client-side shell.
The SSR piece was actually one of the most technically challenging parts. Google needs to see real content when it crawls your pages, not a loading spinner. So every stock page generates a complete HTML document server-side with the latest price, AI analysis, company dossier, and structured data. It's like building 6,000 landing pages that update themselves.

The Moment That Changed Everything
For the first two months, I was building in a vacuum. Shipping features, fixing bugs, talking to myself in commit messages. The analytics dashboard showed a trickle of traffic — mostly bots.

Then something shifted. Real people started showing up.

The first piece of genuine feedback I got wasn't even positive in the traditional sense. Someone pointed out that the portfolio scanner was confusing on mobile. They'd actually tried to use it. They cared enough to tell me it wasn't working right.

I sat there staring at that message for a solid minute. Someone — a real person, somewhere in the world — had found my thing, tried it, and taken the time to tell me how to make it better. After weeks of pushing code into the void, that felt electric.

More feedback came. Some people loved the MoonshotScore but wanted to understand the methodology better (so I built a dedicated methodology page). Others said the daily brief was too long (so I tuned the curation algorithm). One user said they'd cancelled their Seeking Alpha subscription because the AI summaries on Stock Expert were more useful.

That last one kept me going for about two weeks straight.

What surprised me most wasn't the compliments — it was that people were actually building habits around the product. Coming back daily for the market journal. Checking MoonshotScores before making trades. Using the portfolio scanner after earnings season. The product was becoming part of their workflow, and I hadn't spent a single dollar on marketing.

What I've Learned
Start with the data model, not the UI. I spent the first week just designing the database schema. Every table, every relationship, every index. It saved me months of refactoring later.

AI agents are a multiplier, not a replacement. My agents handle the repetitive, scalable work. But every piece of content they generate goes through quality checks, hallucination detection, and confidence scoring. The AI doesn't run unsupervised — it runs with guardrails.

SEO is a product feature, not a marketing tactic. Every stock page on the platform is a potential entry point from Google. The company dossiers, the structured data, the FAQ schemas — they're not afterthoughts. They're core features that happen to also be good for search rankings.

Ship free, learn fast. The entire platform is free during beta. No paywall, no credit card required. This was deliberate. I needed real users doing real things to understand what actually matters. The business model comes later. The learning comes now.

Being solo is lonely but clarifying. There's no one to debate architecture decisions with at 2 AM. But there's also no one to slow you down. Every decision is yours, every mistake is yours, every win is yours. It's terrifying and addictive in equal measure.

Where It Stands Today
As of early 2026, Stock Expert AI covers 6,000+ U.S. stocks across NYSE, NASDAQ, and OTC markets. The AI generates company dossiers with SWOT analysis, investment thesis, growth opportunities, and risk factors — each one running 1,500+ words. The market journal publishes daily. The MoonshotScore updates continuously.

Traffic is growing organically. Users are coming back. And I'm still shipping features almost every day — sometimes two or three.

Is it profitable? Not yet. Is it sustainable long-term as a free product? Definitely not. But right now, the priority is building something genuinely useful, getting it in front of people who need it, and iterating based on what they tell me.

The revenue model is straightforward: freemium tiers with advanced features for paying subscribers. But I refuse to put up a paywall before the free tier is genuinely valuable on its own. Too many products charge you before they've earned your trust.

What's Next
I'm working on expanding the AI agent system — giving them more autonomy to identify market trends, generate thematic research reports, and proactively alert users about significant changes in their watchlists. The idea is that the platform should surface insights you didn't know you needed.

There's also a paper trading feature in beta that lets you practice strategies without risking real money, and a community prediction system where users can make and track market calls.

If you're curious, the platform is live at stockexpertai.com. Everything is free during beta. No sign-up required to browse stocks and read analysis — just jump in.

And if you're a fellow indie hacker building something ambitious with AI — I'd genuinely love to hear about it. The tools we have access to right now are absurd. A single person can build things that would have required a funded startup three years ago.

The gap between "idea" and "product" has never been smaller. The only question is whether you're willing to sit with the discomfort of shipping something imperfect and letting real people tell you what it should become.

I was. And that first piece of feedback made every late night worth it.

I'm Sedat — a civil engineer turned marketer turned accidental fintech founder. I build Stock Expert AI solo from my desk, powered by coffee and an unreasonable number of AI agents. You can find the platform at stockexpertai.com or reach me at [email protected].