Intelligence Search: How to Find Every S&P 500 Stock Exposed to Any Theme, Risk, or Macro Force

The Research Problem No Screener Solves

A stock screener tells you which companies have a P/E under 20, revenue growing 15% annually, and a net margin above 10%. That's useful. But it can't answer questions like:

• Which S&P 500 companies have material China supply chain exposure?
• Which financials have elevated credit risk concerns beyond what rates show?
• Which retailers have pricing power deteriorating right now?
• Which healthcare names are at risk from GLP-1 drugs eating their market?

These questions live in language — in earnings transcripts, 10-K risk sections, and analyst commentary. Not in numbers you can screen.

For the past decade, answering these questions at scale required an enterprise research platform subscription. The kind that costs $40,000 to $100,000 per year and is licensed to hedge funds and large asset managers.

Intelligence Search changes that.

What Intelligence Search Actually Does

Intelligence Search is a full-text search engine built on top of AI-generated research reports for every S&P 500 stock.

Type any theme, risk factor, macro keyword, or business concept. The system searches across all 500+ companies' structured intelligence reports — seven sections per company — and surfaces every name where that topic appears, ranked by relevance.

What you get back isn't a list of tickers. It's a set of result cards, each showing:

• The company name and sector
• Which specific section(s) matched (Business Model, Risk Factors, Stock Drivers, etc.)
• A 160-character context snippet showing exactly why the company matched and how the topic appears in its report

That context is the key difference. You're not just finding stocks that mention "China" — you're finding stocks that mention China in the context of supply chain dependency, regulatory risk, revenue exposure, or competitive threat. And you can see the distinction immediately without clicking into each report.

Seven Sections. One Search.

Every Intelligence Report is structured into seven sections. All seven are fully searchable:

When you search "interest rate sensitivity," you'll find matches across Stock Drivers (where rate exposure affects the catalyst thesis), Macro Sensitivity (where the AI assessed the qualitative impact), and Risk Factors (where rate-linked debt concerns appear). You see all three dimensions in a single results page.

You can also filter by section — so "debt" in Risk Factors only surfaces companies where debt is identified as a genuine structural concern, not just companies that mention the word in passing.

Synonym Expansion: Why It Works When Keyword Search Doesn't

One of the less obvious features is how the search handles language variation.

A company's intelligence report might describe tariff exposure using the phrase "trade conflict." Another might say "trade restrictions." A third might say "deglobalization pressures." A simple keyword search for "tariffs" would miss all three.

Intelligence Search maintains a semantic synonym library covering 50+ groups:

• tariffs also matches: trade war, trade conflict, trade restrictions
• EV also matches: electric vehicle, electric vehicles
• AI also matches: artificial intelligence
• semiconductor also matches: chip, chipmaker, GPU, graphics processing unit
• interest rate also matches: rate hike, rate cut, FOMC, Fed, Federal Reserve
• GLP-1 also matches: obesity, weight loss drug, anti-obesity

This means your search for "reshoring" catches companies discussing "nearshoring," "onshoring," and "deglobalization" — because they're all describing the same structural shift using different vocabulary.

For multi-word queries, the system switches to AND matching: every word in your query must appear in the same section of the report. This surfaces companies with concentrated thematic exposure, not just passing mentions.

Quote Board: From Research to Live Prices in One Click

After running any search, you can switch to Quote Board mode.

This takes every company that matched your query and displays their live stock prices in a real-time board — price, change, and percent move, updating continuously.

The workflow this unlocks:

1. Search "China supply chain risk" → 47 companies match
2. Switch to Quote Board → See which of those 47 are moving today
3. The ones down 2% on a day when US-China tensions are escalating? Those are the names pricing in the risk you just researched.

This bridges the gap between thematic research and market action. You're not just building a list — you're building a live watchlist of every company with a specific exposure.

Macro Driver Mode: Position Around a Scenario

Beyond keyword search, Intelligence Search includes a Macro Driver mode that lets you run hypothetical macro scenarios against the full S&P 500.

You select conditions — oil rising, rates falling, dollar strengthening — and the system immediately splits 500 companies into beneficiaries and losers based on each company's curated macro driver mappings.

This isn't AI inference. It's deterministic: each company in the database has been assigned the macro indicators that actually affect its business. Rising oil is mapped as positive for energy producers and negative for airlines. Falling rates benefit rate-sensitive financials and hurt net-interest-margin lenders. The math runs on the scenario you set.

Use cases:

• Fed signals a rate cut → instantly see which sectors benefit most
• Oil spikes on geopolitical news → see the S&P 500 split between winners and losers before you've read a single headline
• Dollar strengthens → filter for multinationals with high foreign revenue exposure

How This Compares to Enterprise Research Platforms

For context: the enterprise research search space has several well-established players, each built for institutional workflows.

AlphaSense

AlphaSense is an AI-powered search platform that indexes earnings call transcripts, SEC filings, broker research reports, and news across thousands of companies globally. It's designed for institutional analysts who need to search unstructured documents at scale — finding every mention of a specific topic across thousands of pages of filings and commentary.

What it does well: Deep document search across raw source material. If you want to find every earnings call where a CFO mentioned "tariff headwinds," AlphaSense can surface all of them with context. It also has a market intelligence layer, sentiment analysis, and integration with broker research.

The tradeoff: It's built for power users doing deep research on specific companies or sectors. The interface is designed around document review workflows. Pricing runs in the tens of thousands of dollars per seat per year — it's licensed to Goldman Sachs analysts, not individual investors.

Where Intelligence Search is different: Rather than searching raw documents, Intelligence Search searches structured, pre-digested research. The AI has already read the earnings transcripts and 10-Ks — you're searching the synthesis, not the source material. This makes Intelligence Search faster for thematic discovery across 500 companies simultaneously. AlphaSense is better for deep, primary-source due diligence on a specific name; Intelligence Search is better for "which 500 companies are exposed to this theme right now."

Sentieo / Visible Alpha

Sentieo (now part of Visible Alpha) combined semantic document search with charting and note-taking tools for buy-side analysts. It indexed filings, transcripts, and news with annotation and collaboration workflows.

Visible Alpha focuses more specifically on consensus financial model analysis — building standardized financial models from analyst estimates to identify where consensus assumptions differ from your own.

The tradeoff: Built for the sell-side and buy-side professional workflow. The strength is integration between document research and financial modeling. It's enterprise-licensed.

Where Intelligence Search is different: Intelligence Search doesn't try to be a document library or a modeling tool. It surfaces thematic exposure across a pre-structured research corpus. The use case is different: rapid discovery rather than deep document review.

Bloomberg Terminal

At $25,000+ per year, Bloomberg Terminal provides the broadest coverage of any financial data platform — fixed income, equities, currencies, news, and messaging. Bloomberg's search capabilities are substantial, but they're designed for navigating Bloomberg's own data ecosystem rather than semantic search across company research.

Where Intelligence Search is different: Bloomberg is infrastructure for professional traders and portfolio managers. Intelligence Search is a focused research discovery tool accessible to any investor.

The Core Distinction

The pattern across enterprise research platforms is consistent: they're built around document retrieval — surface the right filing, transcript, or report so a human analyst can read it. The value is in the completeness and quality of the document library, plus the search to navigate it.

Intelligence Search takes a different approach: semantic search over structured, AI-distilled synthesis. Instead of returning documents, it returns answers — telling you which companies have a specific exposure and exactly where in the research that exposure appears.

Neither approach is universally better. For a portfolio manager doing deep due diligence on a single position, primary source documents matter. For an investor trying to quickly map which 500 S&P stocks are exposed to a tariff escalation scenario, pre-structured synthesis is faster and more actionable.

What Makes Thematic Search Useful Right Now

Thematic research has historically been expensive because it requires reading — a lot of it. To know which retailers have pricing power concerns, someone had to read the earnings calls. To know which industrials have China supply chain dependency, someone had to read the 10-K risk factors.

AI changes the economics of that reading. When research reports for 500 companies are generated by AI from the underlying fundamentals and earnings transcripts, those reports become a database. And databases are searchable.

The practical effect:

• A theme that would take an analyst two weeks to map across the S&P 500 takes thirty seconds
• The results link directly to live quotes so you can see which names are actually moving
• Macro driver analysis converts a scenario into a watchlist before the news cycle finishes

The intelligence isn't magic — it's AI reading the same sources an analyst reads and structuring the output. What changes is scale and speed.

Getting Started

Intelligence Search is available at /intelligence-search.

A few searches worth trying:

• AI — See which S&P 500 companies have material AI revenue exposure vs. those citing AI as a competitive risk
• China — Map supply chain dependency, revenue concentration, and regulatory risk across sectors
• GLP-1 — Find healthcare companies whose markets are being disrupted by weight loss drugs
• pricing power — Surface companies where the intelligence highlights strong vs. deteriorating pricing ability
• credit risk — Find companies where elevated leverage or refinancing risk appears in Risk Factors

Filter by sector to narrow results. Switch to Quote Board to overlay live prices. Use the Macro Driver mode to run a scenario instead of a keyword.

The goal is simple: make the kind of thematic research that used to take days — and cost a seat license — available in seconds.