Before starting Valarn, I spent several years working alongside professional investment teams.
One thing became obvious very quickly.
Professional investors don't make decisions from a single news article or one earnings report.
They spend hours—sometimes days—researching a company from every possible angle.
Individual investors rarely have that luxury.
Most people are balancing careers, families, and busy lives. They might have an hour after work to research a stock before making an important decision.
That's not a lack of intelligence.
It's a lack of time and research tools.
That's the problem I wanted to solve.
The Research Gap#
Large investment firms have entire teams dedicated to research.
One person studies company financials.
Another watches economic trends.
Someone else follows industry news.
Others focus on risk, technical analysis, or market sentiment.
These experts challenge each other's ideas before any important investment decision is made.
Most individual investors have:
- Financial news websites
- A brokerage app
- Social media
- Maybe a YouTube video or podcast
There's nothing wrong with those resources.
But they're not the same as having a team of specialists reviewing the same company from different perspectives.
I believed AI could help close that gap.
Where the Idea Came From#
In 2024, I started experimenting with AI for financial research.
Like many people, I asked AI to summarize earnings reports, SEC filings, and company news.
The responses sounded convincing.
But they often lacked depth.
Sometimes they were even confidently wrong.
Then I realized something.
Professional research isn't produced by one analyst working alone.
It's created through discussion, disagreement, and constant questioning.
One analyst finds the opportunity.
Another points out the risks.
A third challenges the assumptions.
The best ideas survive because they've been tested.
That led to a simple question:
What if AI worked the same way?
Instead of one AI giving one opinion...
What if multiple AI specialists examined the same company from different angles, challenged each other's reasoning, and then worked together to produce a balanced research report?
That became the foundation of Valarn.
Building a Team of AI Analysts#
Instead of relying on one general-purpose AI, Valarn uses a whole team of specialized AI analysts — today, up to around 25 of them, organized into five categories.
Each has a specific job. Here are just a few:
- Fundamentals Analyst — Reviews financial statements, earnings, valuation, and company performance.
- Market Analyst — Looks for price trends and market structure.
- Sentiment Analyst — Tracks news, earnings calls, analyst revisions, and market sentiment.
- Macro Analyst — Watches economic trends that could affect investments.
- Risk Analyst — Searches for reasons an investment idea could fail.
The Risk Analyst is one of my favorites.
Its entire job is to disagree.
Seriously.
While the other analysts build a case, the Risk Analyst tries to tear it apart.
Sometimes it's frustrating.
Usually it's exactly what's needed.
Because good research isn't about proving you're right.
It's about finding out where you might be wrong.
Why Debate Matters#
Professional investment teams debate constantly.
The goal isn't to win an argument.
The goal is to find the strongest conclusion.
We wanted our AI to work the same way.
Instead of giving one quick answer, Valarn allows multiple AI analysts to examine the same evidence from different viewpoints before producing a final research summary.
Sometimes they agree.
Sometimes they don't.
That disagreement is valuable.
It often reveals risks—or opportunities—that would otherwise be missed.
Who We Built Valarn For#
Valarn isn't designed to replace financial advisors or professional investment managers.
It's built for people who enjoy doing their own research and want better tools.
People who:
- Manage their own investments.
- Want more than headlines and social media opinions.
- Like understanding both the positive and negative sides of an opportunity.
- Don't have hours every day to read hundreds of reports.
These are engineers, teachers, doctors, entrepreneurs, retirees, and lifelong learners.
They're more than capable of understanding institutional-quality research.
They just don't have access to an institutional research team.
What We've Learned#
One of our biggest surprises was what users cared about most.
We assumed everyone would focus on the final research view.
Instead, many users spend the most time reading the debate between the AI analysts.
They want to understand why the AI reached its conclusion.
Another lesson?
Uncertainty is valuable.
Sometimes every analyst agrees.
Sometimes they don't.
Showing that disagreement helps people make better-informed decisions instead of blindly trusting a single answer.
We've also learned that transparency builds trust.
Rather than asking users to simply believe the AI, we let them explore past analyses and understand how conclusions were reached.
Trust should be earned—not assumed.
Our Mission#
For decades, the best research tools have mostly been available to large financial institutions.
AI is changing that.
Today, it's possible to give individual investors access to research that once required an entire team of analysts.
That's what excites me.
Not replacing human judgment.
Supporting it.
Helping people understand both the opportunities and the risks.
Helping them ask better questions.
Helping them make more informed decisions.
We're Just Getting Started#
AI is improving at an incredible pace.
So is Valarn.
We'll continue adding new data sources, improving our AI analysts, expanding our research capabilities, and making every report easier to understand.
Our goal has never been to tell people what to buy or sell.
Our goal is to help people think more clearly.
To give everyday investors better research, better context, and greater transparency, so they can make their own decisions with confidence.
If we can help narrow the research gap between Wall Street and individual investors—even a little—we'll consider that a success.
And we're just getting started.
Valarn
Founder
Valarn Research Team
