AI equity research, on any stock in seconds

What is AI equity research?

AI equity research uses a large language model to read a company's public information: filings, fundamentals, recent results, valuation multiples and price action, and turn it into a structured, plain-English read on the business. Instead of working through a 200-page annual report yourself, you get the parts that matter organized in one place, in minutes.

The point isn't to replace your judgment, it's to do the slow background work faster. A good AI read gets you to an informed starting position quickly: what the company does, how durable it looks, whether it appears cheap or expensive, and what could go wrong. From there you do your own thinking. It's a research aid, not a recommendation engine.

Meet Vera, your AI research assistant

On RadarPulse, equity research is handled by Vera, an AI research assistant built into the dashboard. Ask Vera about any major listed stock and she assembles a read on demand, drawing on publicly available company data: financial statements and fundamentals, recent earnings and guidance, valuation multiples, and the current price and market context. Vera is coming soon and will be part of the Elite plan; you can explore the rest of RadarPulse on Basic first and join the waitlist by creating an account.

Two things define how Vera works:

• Sourced from public data. The reads are built from public company information and written in plain English, and Vera is upfront when a figure is an estimate rather than a reported number.
• Educational, not advice. Vera is an AI research assistant, not a licensed financial professional. The output is there to inform your own research: it never tells you to buy or sell.

The 12 research modes Vera offers

Rather than a single wall of text, Vera works in distinct research modes, so you pick the angle that fits the question you're asking. The twelve modes:

• Full Stock Research Report. The all-in-one read: business model, moat, financial health, growth, valuation, a DCF framing, red flags and a plain-English summary in one place.
• Long-Term Thesis (new). The multi-year picture: the structural drivers, durability and what would have to stay true for the story to hold over years rather than quarters.
• Earnings Call Breakdown. The latest results and management commentary distilled: what changed, what guidance implies, and the tone behind the numbers.
• Red Flag Detector. A focused sweep for things that warrant caution: deteriorating metrics, accounting quirks, concentration and other warning signs.
• Risk Map (new). The key risks laid out and organized: business, financial, competitive and macro: so you can see where a name is most exposed at a glance.
• Competitive Moat Analysis. The durability of any competitive advantage: brand, network effects, switching costs, scale: and how defensible it really looks.
• Valuation Comparison. How the current multiples stack up against the company's own history and against peers and the wider market.
• DCF Assumption Builder. A discounted-cash-flow framing that walks through the assumptions behind what future cash generation might be worth today.
• Catalyst Calendar. The upcoming events that could move the story: earnings, product cycles, regulatory dates and other scheduled milestones.
• Management Quality. A read on leadership: track record, capital allocation and how management has executed against what it promised.
• Bull/Bear Committee Debate. The strongest reasonable bull and bear arguments set side by side, so you see the tension in a name rather than a one-sided pitch.
• Beginner Walkthrough. The whole picture explained from the ground up, in plain language anyone can follow, ideal if you're newer to reading a company.

You can also run Compare mode to put several tickers through the same analysis at once and read them side by side, handy when you're weighing a few names against each other rather than studying one.

Frame the analysis for your horizon and risk posture (new)

On top of the modes, an optional horizon and risk lens changes which factors the analysis leans on. Set a time horizon: short-term, 1-year, 5-year or 10-year, and a risk posture, conservative, balanced or aggressive, and Vera reframes the same sourced data to emphasize what matters at that time scale and tolerance. A 10-year, conservative framing puts more weight on durability, balance-sheet strength and downside risks; a short-term, aggressive one foregrounds near-term catalysts and momentum. It's there to frame the research for how you actually think about a position, it never tells you to buy or sell.

AI equity research vs. a stock screener

A screener is great at finding candidates: filter by market cap, P/E, growth or yield and you get a list. But a list of tickers isn't understanding, you still have to figure out why each name looks the way it does. That's the gap AI equity research fills.

• A screener answers "which stocks match these numbers?", it's a filter over a database.
• AI equity research answers "what's actually going on with this company?", it reads the data and explains it.

They're complementary. Many people screen for ideas, then run an AI read on each survivor to understand the business before committing more of their own time. Vera is the second step, not the first.

A worked example

Say a screener surfaces a profitable mid-cap that looks cheap on earnings. You type the ticker into Vera and run the Full Stock Research Report: it explains the company sells subscription software to enterprises, flags meaningful switching costs but rising competition, notes the valuation multiple is below its own three-year average, frames in a DCF what continued cash generation might be worth, and highlights customer concentration as a red flag. Want to go deeper on a piece of that? The Risk Map mode organizes that concentration alongside the other business, financial and competitive risks, and the Bull/Bear Committee Debate lays out, side by side, the "durable, mispriced compounder" view against the "growth is slowing and one big client could leave" view. If you're holding for the long run, the Long-Term Thesis mode, or a 5- to 10-year horizon lens, reframes the same data around what would have to stay true for years.

In a few minutes you've gone from "cheap on paper" to a balanced, sourced picture of the debate, a far better place to begin your own deeper work than a row in a spreadsheet. None of it is a recommendation; it's structured context.

Limitations: and why it's educational only

AI equity research is powerful, but it has real limits, and using it well means respecting them:

• It can be incomplete or out of date. Like any AI, Vera can miss nuance or work from data that has since moved. Always confirm key numbers against primary sources such as the company's own filings.
• It can't predict the future. A DCF framing or a bull case is a way to think about value, not a forecast of the price.
• It's not personalized advice. Vera doesn't know your goals, time horizon or risk tolerance, and it isn't a licensed professional.
• It's one input among many. Treat a read as a starting point, then add your own analysis, the live tape, and other sources before you decide anything.

How it fits the rest of RadarPulse

Vera doesn't work in isolation. A company read sits in the same dashboard as live index prices, scored options flow, the Fear & Greed Index and institutional 13F holdings, so you can cross-check what the fundamentals say against what the tape and the "smart money" are doing right now. To run the same analysis on several names at once, see comparing stocks side by side, and for the full picture of how the modules fit together, the markets terminal overview ties it all in one view.

Frequently asked questions

What is AI equity research?

It uses a large language model to read a company's public data: filings, fundamentals, results and price action, and summarize it into a structured, plain-English read on the business. You get the moat, valuation, cash-flow math and risks in minutes instead of reading the whole annual report yourself. On RadarPulse this is done by Vera, an AI research assistant. It's a way to research faster, not a recommendation engine.

Is RadarPulse's AI equity research reliable?

Vera is a research aid, not an oracle. It's good at organizing a lot of information quickly and surfacing things you might miss, but like any AI it can be incomplete or out of date and it can't know the future. Use its reads as a structured starting point, confirm the numbers against primary sources, and never act on a single AI summary alone.

What data does the AI use?

Publicly available company information: financial statements and fundamentals, recent earnings and guidance, valuation multiples, and current price and market context. The reads are sourced from that public data and written in plain English, and Vera is upfront when something is an estimate rather than a reported figure. It doesn't use private or non-public information.

Is AI equity research financial advice?

No. The reads are educational and informational only: not financial advice, not a recommendation to buy or sell, and not a price target. Vera is an AI research assistant, not a licensed financial professional. Investing involves substantial risk of loss, and past performance doesn't predict future returns.

What stocks can it analyze?

Essentially any major listed stock, you enter a ticker and Vera builds the read on demand rather than limiting you to a fixed watchlist. Because the work is generated from public data, all 12 research modes apply whether it's a mega-cap or a smaller, less-covered name, and you'll be able to line several tickers up in Compare mode. Vera is coming soon to RadarPulse.

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