See What Traditional Screeners Miss

MercuriusData provides a stock screener that explains its findings in plain language so you can make informed decisions.

The Problem with Traditional Screeners

What you get elsewhere

  • Raw numbers without context
  • P/E, P/B, EV/EBITDA — but no explanation of why they look that way
  • No insight into qualitative risks like customer concentration or regulatory exposure
  • You still need to read the annual report yourself

What MercuriusData gives you

  • Every number comes with a written explanation
  • AI-generated plain-text analysis of qualitative factors
  • Scores that quantify what's usually hidden in footnotes
  • Multi-model cross-validation — not just one algorithm's opinion

How the Analysis Works

1

We Ingest the Data

The platform pulls annual reports, financial filings, and supplementary data for thousands of publicly listed companies.

2

Multiple AI Models Analyze Independently

We then ask several leading AI models (including GPT, Claude, and Gemini). Each model evaluates qualitative factors, and assigns scores with written justifications. This isn't a single black-box algorithm — we try to show as much explanations as possible.

3

You Get Scores + Explanations

For every company, you see:

  • •A numerical score per category (e.g., Moat: 72/100, Risks: 12/100)
  • •A plain-text explanation of why that score was assigned
  • •The ability to compare how different models evaluated the same factor
  • •An overall qualitative profile that complements traditional financial metrics

Deep Dive: What We Analyze

Every company is evaluated across multiple qualitative dimensions. Here's what the AI looks for.

📊 Customer Concentration

Is the company dangerously dependent on a handful of clients? The AI reads revenue breakdowns, segment disclosures, and risk factor sections to assess this.

💰 Debt & Financial Health

Beyond just debt-to-equity ratios. The AI examines covenant structures, maturity schedules, interest coverage trends, and management commentary on refinancing.

🏗️ Competitive Position / Moat

Does the company have pricing power, switching costs, network effects, or other structural advantages? The AI evaluates management claims against actual financial evidence.

👔 Management Quality

What does the language in shareholder letters reveal? How does compensation structure align with shareholder interests?

📈 Valuation Gap

The AI compares its qualitative assessment to the market's current pricing. A high-quality company trading at a low multiple might be an opportunity. A low-quality company at a premium might be a trap.

🌱 Growth Sustainability

Is the company's growth organic or acquisition-driven? Are margins expanding or compressing? The AI reads between the lines.

Why Three Models Are Better Than One

Every AI model has biases and blind spots. By running the same data through multiple independent models, MercuriusData creates a natural cross-check.

When all models agree on a risk, you can be more confident it's real.

When models disagree, that's a signal to dig deeper.

The average score smooths out individual model quirks.

You see the consensus score at a glance, and you can always click through to read each model's individual analysis.

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