AI‑Driven Stock Diagnosis, Transparent & Explainable

We don’t promise returns. Our AI applies consistent, rule‑based checks across fundamentals, price action and news—with plain‑English notes so you can review, learn and improve your judgment.

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What You’ll Get

AI-Powered Stock Diagnosis

Consistent, rule‑based scoring across fundamentals, price action and news sentiment—built to help you learn patterns without hype.

Faster, Wider Coverage

Scan more tickers in minutes. Our pipeline surfaces anomalies, risks and opportunities you might otherwise miss.

Explainable Results

Each flag comes with plain‑English notes, key metrics and links to sources so you can understand the ‘why’ and build judgment.

How It Works

Share a Ticker

Send a US symbol via WhatsApp. We only use public information—no paid tips or undisclosed promotions.

AI Analysis Pipeline

Models ingest financial statements, technical trends and news to score quality, momentum and risk signals.

Coach‑in‑the‑Loop

A human reviewer sanity‑checks edge cases and writes concise notes. Education only—no investment advice.

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Learning Modules

Understanding AI Diagnostics

How models read fundamentals, price behavior and news sentiment—what they see and what they miss.

Risk Literacy

Drawdowns, valuation traps, earnings quality and scenario thinking—recognize red flags before chasing narratives.

Interpreting Explanations

Learn to read feature importance, confidence, and alternative hypotheses to avoid over‑reliance on a single signal.

Case Studies (US Stocks)

Walk through past examples to see how AI and human review combined to refine the decision process.

Feedback from Scholars (Illustrative)

Prof. Elaine Zhou, Finance Researcher

“The emphasis on explainability and risk literacy is appropriate. It encourages users to question signals rather than chase them.”

Dr. Marco Alvarez, Data Science

“I like the coach‑in‑the‑loop approach—models surface hypotheses quickly; humans provide context and caution.”

Prof. Janet Lee, Accounting

“Integrating accrual quality and cash‑flow checks helps new learners avoid ‘story‑only’ stocks.”

Dr. Arun Menon, Behavioral Finance

“Clear comparisons to traditional methods reduce bias and help learners build disciplined habits over time.”

AI vs Traditional Diagnosis (Illustrative)

Aspect	AI‑Driven Diagnosis	Traditional Manual Diagnosis
Speed & Coverage	Scans many tickers in minutes; updates as new data arrives.	Limited by time; spot checks on a few names.
Consistency	Rules/thresholds applied uniformly to reduce drift.	Subject to mood, fatigue and changing heuristics.
Explainability	Notes + source links encourage verification.	Reasoning often stays implicit or unrecorded.
Risk Flags	Automatic surfacing of drawdowns, leverage, dilution, valuation stress.	Risk checks may be ad‑hoc or forgotten.
Learning	Structured summaries make it easy to review and improve.	Harder to retain learnings across names/time.
Bias Control	Backtests and out‑of‑sample checks curb narrative bias.	Higher susceptibility to recency and availability bias.
Limitations	Dependent on data quality; models can miss regime changes—human review required.	Can catch context and nuance but slow and inconsistent.

FAQ

Do you guarantee profits?

No. We do not guarantee any outcome. All materials are for education only.

Is this investment advice?

No. Nothing here is a solicitation or a recommendation to buy or sell securities.

Why WhatsApp?

Fast, simple, and direct for sharing checklists, charts and templates.

Which stocks can you check?

Any US-listed ticker. We rely on public sources and teachable frameworks.

Terms: For education purposes only. Nothing on this page is investment advice or a solicitation to buy or sell securities. We do not guarantee profits or outcomes. Markets involve risk and can lead to loss of principal. Always do your own research and make independent decisions.

We comply with Google’s financial services policies: no claims of “guaranteed returns”, “zero risk”, or “get-rich-quick”. Any examples are historical and illustrative, not indicative of future performance.