AIB Research Beta

AI Stock Analysis

A research tool powered by 1000+ specialized AI agents built to explain business economics, pressure-test investment theses, and flag issues worth investigating further.

Optional: help shape the beta in 20 seconds
Research beta only. Not financial advice. No spam - just early access and free sample reports.
Built for

Investors who already use AI and want a deeper workflow than one prompt.

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Hundreds of specialized AI research agents reviewing different parts of the thesis.

Beta offer

Get 10 free stock audits so you can test more than one idea or watchlist name.

What the report helps you answer

AIB is designed to help investors analyze the business behind a stock, not just react to price charts or headlines.

Is the business actually durable? Review moat strength, pricing power, customer loyalty, unit economics, and competitive pressure.
How do customers actually behave? Look at purchasing behavior, switching friction, habit formation, demand signals, and possible consumer frustration.
Is management being candid? Flag vague language, missing unit or physical-volume disclosures, KPI changes, and incentive-driven narratives.
What could break the thesis? Identify disruption threats, weakening advantages, reinvestment problems, and hidden fragility.
Where might investors be biased? Use cognitive-bias checks to find overconfidence, narrative traps, social proof, anchoring, and misplaced certainty.
What needs further work? Surface the most important open questions an investor should investigate before committing capital.

Different from a screener, dashboard, or generic AI chat

AIB is meant to be a primary stock-analysis workflow for AI-friendly investors, not a replacement for raw data platforms.

Beyond screeners Screeners help you find stocks. AIB is designed to pressure-test whether a business thesis deserves attention.
Beyond dashboards Dashboards organize data. AIB turns the investigation into a structured research report with risk questions.
Beyond one prompt AIB uses specialized research agents for business quality, psychology, disclosure quality, valuation risk, and thesis stress testing.

How the audit works

The system is built around a structured research process designed to challenge the investment case from multiple angles.

I

Business Quality Review

Agents review operational quality, cost structure, customer behavior, pricing power, qualitative moat indicators, and business durability.

II

Psychology and Consumer Behavior Check

Specialized agents look for investor cognitive biases and economic behavior patterns in how customers buy, repeat, switch, complain, or stay loyal.

III

Management Candor and Disclosure Review

The audit flags unclear executive language, changing KPIs, missing physical-volume data, and cases where revenue growth may hide weak underlying demand.

IV

Standards-Aware Research Summary

The final report is designed with professional research discipline in mind, including source transparency, separation of facts from assumptions, and clear risk framing.

Sample audit excerpt

A simple preview of the kind of output the beta is designed to produce.

Business Thesis Stress Test Sample
Business Quality Review

The company appears to have durable customer recognition and favorable pricing power relative to several competitors.

Customer Behavior Review

The key question is whether repeat purchasing reflects genuine preference, switching friction, or a temporary lack of alternatives.

Disclosure Quality Review

The report would flag cases where management stops disclosing unit sales, physical volume, retention, or other metrics that investors need to judge real demand.

Research Summary

The business deserves further study, but the investment case depends heavily on whether the moat remains intact while management continues reinvesting capital efficiently.

Coming next: fair value ranges and public-source research signals

The current beta focuses on business quality, customer behavior, management incentives, disclosure quality, valuation risk, and red flags. Future versions will add approximate fair value ranges and public-source research signals from reviews, transcripts, filings, job postings, app reviews, forums, competitor materials, and other non-interview sources.

Vote on what to build next

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Fair value ranges Approximate intrinsic-value ranges and key valuation assumptions.
Management candor score Disclosure quality, KPI changes, and missing physical-volume checks.
Psychology and behavior audit Cognitive-bias checks and analysis of customer purchasing behavior.
Public-source scuttlebutt proxy Signals from reviews, forums, job postings, transcripts, and customer comments.
Buffett-style business review Moat, owner earnings, capital allocation, incentives, and durability.
CFA/FINRA-aware structure Source discipline, risk framing, assumptions, conflicts, and fact/opinion separation.

Get 10 free AI stock audits

Enter your email to get early access and test AIB across more than one stock idea.

Research beta only. Not financial advice. No spam.