How to Research Any Company or Stock with AI (10 Finance Workflows)
Reliable, cited financial research without the hours. 10 AI workflows for companies, stocks, and markets in Claude or ChatGPT, plus a scheduled watchlist monitor.
Financial research has a terrible interface.
A simple question such as “Is this company worth researching further?” can send you through an annual report, three earnings calls, analyst estimates, recent news, competitor websites, market data, and a collection of Reddit threads you probably shouldn’t trust.
Then you still have to connect everything.
AI seems perfect for this kind of work. It can read faster, compare more sources, and explain the result without assuming you have a finance degree.
But finance is also one of the easiest places for a confident AI answer to become expensive.
A reliable answer needs current information, real sources, clear assumptions, and enough honesty to say when the data is missing. A general chatbot can help, but it doesn’t have reliable access to all the financial data you may need.
So we added four finance research tools and 10 ready-made workflows to Amplifiers.
The tools connect Claude and ChatGPT to current web information, company records, and licensed financial and economic data they can’t normally access, while the workflows tell the AI which tools to use, what to verify, and how to turn that data into a specific result.
To quickly show you how the new Amplifiers tools work, I asked Claude for Apple’s FY2027 revenue and EPS estimates. On its own, Claude refused rather than guess. Good guardrails! But after loading the Finance Deep Research amplifier, it returned the estimates, ranges, analyst counts, and sources.
Together, the new tools and workflows inside Amplifiers can research companies, compare valuations, prepare you for earnings, investigate price movements, map competitors, and monitor entire sectors.
You can run them inside Claude or ChatGPT by describing what you need in normal language.
Today, I’ll show you how they work through a couple of demos, then give you a prompt that builds your own automated finance monitor. It runs on a schedule, remembers its previous conclusions, and alerts you when something important changes.
PS. If you’re new here, Amplifiers gives Claude and ChatGPT the tools and workflows of a whole team of specialists. It takes about 2 minutes to connect.
After that, it can research, generate images, and produce high-quality work across marketing, design, writing, business, finance, and more. You’re giving superpowers to the AI you already use, without adding another pile of tools or subscriptions.
Here’s what we’ll cover:
The 10 AI finance workflows inside Amplifiers
The 4 research tools working underneath them
Two demos: researching NVIDIA, then a whole watchlist
Who these finance research tools are for
How to build your own AI finance monitor (prompt inside)
The 10 finance workflows inside Amplifiers
The workflows cover three stages of financial research. You can begin by understanding a company, move into valuation and competition, then keep monitoring what changes.
Each workflow gives the AI a clear research process to follow.
Understand a company
Company ramp: The first hour of researching a company, compressed into one report. It explains how the business makes money, how the financials look, who it competes with, what could go wrong, and what deserves further investigation.
Comps table: Compares a company with similar businesses using valuation, growth, margins, and other relevant measures. It also asks whether any premium appears justified.
Valuation check: Builds a discounted cash flow range and explains which growth and margin assumptions appear embedded in the current share price. The result is a range rather than one suspiciously precise number.
Prepare for earnings and monitor stocks
Stock analyst briefing: Produces full equity research on one company or a watchlist. You choose the depth, and the workflow can be scheduled so the result is waiting in Slack or your inbox.
Pre-earnings prep: Defines what would count as a meaningful beat or miss before the company reports. This gives you a frame for reading the numbers when they arrive.
Weekly earnings digest: Follows a watchlist through earnings season and translates each report into what happened, what management expects next, and which assumptions changed.
What actually happened: Investigates why a stock moved when the headlines feel incomplete. It compares the reported event with what the market expected and looks for details the first wave of coverage missed.
Understand a market
Sector heat map: Reads earnings and guidance across an industry to identify which areas are strengthening and where pressure is building.
Competitive landscape map: Finds public and private companies in a market, then groups and compares them using current company and registry data.
M&A rumor tracker: Investigates whether an acquisition rumor has credible sourcing, whether the buyer could afford the deal, and what the transaction could mean.
Paid AI Blew My Mind subscribers get PRO access to Amplifiers, including all 10 finance workflows and everything else inside: more than 120 workflows and 160 tools your AI can’t access on its own.
Behind these finance workflows are four specialized tools that give your AI assistant the current, traceable information it needs at every step.
The 4 research tools that make the answers reliable
Amplifiers gives your AI assistant access to four research tools covering different parts of finance research: depth, freshness, identity, and business context.
And you’re not limited to the ready-made workflows I shared earlier. You can call one tool on its own (like I did in the intro with Apple’s FY2027 revenue and EPS estimates), or mix a few together around whatever you want to find out.
1. Finance Deep Research provides depth
What it does: Powered by You.com’s Finance Research API, it combines current web intelligence with licensed data from S&P Global, SEC/EDGAR, FRED, BLS, Eurostat, the World Bank, the IMF, Nasdaq, and other institutional sources. It investigates across them and returns one cited report.
Why it matters: It checks periods, currencies, units, and definitions before using a figure. You.com reports 87.29% accuracy on FinSearchComp’s historical lookup benchmark, over 14 points above the next system tested.
Keep in mind: Deeper investigations can take several minutes because accuracy comes before speed.
2. Grounded Web Search provides freshness
What it does: Also powered by You.com, it returns current web and news results with real URLs, filtered by date, country, or trusted domains.
Why it matters: It catches events that happened after the latest filing and gives Finance Deep Research something current to investigate.
Keep in mind: Web quality varies, so good workflows prioritize filings, regulators, investor relations pages, and other primary sources.
3. The Global Trade Registry establishes identity
What it does: Powered by Veridion, it connects company names with registrations, addresses, identifiers, subsidiaries, and digital identities. Its company graph covers 642 million companies across 249 countries.
Why it matters: It distinguishes similar names, finds private companies, and connects entities belonging to the same corporate group.
Keep in mind: Companies can have several valid registrations, so the relevant legal entity still needs to be selected.
4. Company Enrichment adds business context
What it does: Veridion’s Company Enrichment adds industry, revenue estimates, employee count, ownership, location, offerings, and digital presence, with confidence scores and source trails.
Why it matters: It provides a starting profile for private and lesser-known companies with little published information.
Keep in mind: Enrichment can lag behind recent filings, so current financial conclusions should still use filings and earnings materials.
Together, You.com provides current, cited research, while Veridion identifies the right company and adds business context.
2 demos: researching one company, then a whole watchlist
To show you how the finance workflows work in practice, I tested two setups: one that researches a single company from three angles, and one that monitors several companies in a single briefing.
Example 1: Researching NVIDIA from three angles with AI
For the first test, I ran NVIDIA through three Amplifier workflows. Each one answered a different question:
How does the business make money?
What does the current valuation appear to assume?
Who or what could weaken its position?
Together, they covered the business, valuation, and risks. The research used information available through July 22, 2026 and prioritized company filings, earnings releases, and competitor disclosures.
1) Company ramp: understand the business before reading opinions
I started with the Company ramp workflow to understand where NVIDIA’s revenue comes from, what keeps customers around, and which dependencies could affect the business.
The prompt: Run the Company ramp amplifier for NVIDIA Corporation (NASDAQ: NVDA), using information available through July 22, 2026. Focus on one question: How does NVIDIA make money?
The result: A three-page report covering NVIDIA’s revenue streams, margins, dependencies, risks, and the questions worth researching next.
It showed that one part of the business generated 90% of NVIDIA’s revenue, then mapped what supports that position and where the company remains dependent on others. You can run the same workflow on any company to get this foundation before reading opinions about where it might go.
2) Valuation check: see what the share price already assumes
Once I understood the business, I asked what NVIDIA’s current share price expected the company to deliver.
The prompt: What does NVIDIA’s current valuation appear to assume? Return a valuation range and explain which revenue growth, margins, cash flows, and long-term assumptions would support it. Show how the result changes under conservative, base, and optimistic assumptions. Cite every financial input and show the calculations. Use amplifiers.
The result: A five-page report with cited financial inputs, calculations, three scenarios, and a sensitivity table.
The estimated values ranged from $148 to $656 per share. More useful than the range itself, the report showed which growth, margin, and cash flow assumptions produced each result. You can apply the same process to any public company to see what its price already assumes.
3) Competitive landscape map: find the threats beyond direct rivals
With the business and valuation covered, I moved to the risks that could weaken NVIDIA over the next few years.
The prompt: Who or what could weaken NVIDIA’s position over the next three to five years? Use the Competitive landscape map amplifier. Group the threats, rank the most important ones by probability and potential impact, and explain what evidence would show that each threat is becoming stronger.
The result: A six-page report ranking nine threats by probability and impact, with evidence that would show each one becoming stronger.
For NVIDIA, the biggest structural threat came from customers developing their own chips. For another company, the threats would be different, but the result would serve the same purpose: a list of risks and the signals that tell you when they are starting to matter.
You can also read the complete Claude conversation here.
Example 2: Monitor several companies at once
After three deep reports on one company, I tested a more compact setup: one briefing across an entire watchlist.
The prompt: Run the AI Stock analyst briefing amplifier for this watchlist: Microsoft, Tesla, Nike, Novo Nordisk. Create a table showing:
Stock
30 to 90-day outlook
3 strongest bull/bear signals
Biggest risk
Next catalyst
What would change the picture
What came back:
The complete conversation ended with one table comparing each company’s near-term outlook, strongest signals, main risk, next catalyst, and the evidence that would change the picture.
It also found a pattern across the watchlist: three of the four companies had earnings within the next two weeks. For Tesla and Nike, the margin numbers mattered more than the headline results because both had recently benefited from one-time items.
How you could use it: This works for companies you already own, stocks you’re considering, competitors in the same sector, or businesses connected to one theme.
You could also use it to monitor clients, suppliers, or partners whose results affect your own company. Run it once before an important event, or put it on a weekly schedule and keep the same table updated as prices, news, earnings, and risks change.
Who these finance research tools are for
The four demos focused on stocks, but the same research tools become useful whenever your work depends on understanding a company or market.
Maybe you need to know who is entering an industry, what competitors are investing in, whether customer budgets are changing, or which private companies are growing in the background. The tools can follow those questions too.
That makes them useful for:
Investors and analysts researching companies, valuations, earnings, competitors, market events, or an entire watchlist.
Founders and strategy teams trying to understand an industry, find new entrants, follow changes in customer spending, or evaluate a new market.
Marketing teams tracking competitor launches, pricing, campaigns, partnerships, messaging, and expansion into new audiences or regions.
Sales and business development teams building lists of companies by industry, size, or location, then researching each one before outreach.
Finance, risk, and procurement teams checking legal entities, monitoring important customers and suppliers, or finding signs that a business relationship may be changing.
Consultants and researchers preparing for a client, mapping a sector, investigating a market event, or finding private companies that receive little press coverage.
That list could keep going. Once Amplifiers is connected to Claude or ChatGPT, you can choose a ready-made workflow or ask your own question in the same way you normally talk to AI.
Every report captures one moment. The companies and markets keep moving after the report is finished.
For the next part, I wanted to turn that one-time research into a system that keeps watching and tells you what changed.
Build your own AI finance monitor (prompt inside)
We’ve covered who these tools are for, what you can research with them, and the financial and company data Amplifiers can reliably pull into Claude or ChatGPT.
The next step is to have your AI keep watching that information for you.
Your monitor could follow a stock watchlist, competitors, customers, suppliers, private companies, prospects, an industry, or any combination of them. You decide what matters.
For this, I created a Financial Monitor Builder prompt.
It guides your AI through creating a monitoring workflow that:
Researches what matters to you: Selects the right Amplifiers tools and returns the findings in a consistent briefing.
Runs automatically: Sets your frequency and guides you through scheduling it in ChatGPT, Claude Cowork, or Claude Code.
Tracks meaningful changes: Remembers its previous conclusions, compares them with each new run, and alerts you when something important changes.
Builds a history: Keeps past findings so the AI can later check what it got right or wrong.
All you need to do is paste the prompt I’ll share below into your AI assistant, then answer a few questions about what you want to track, which changes matter, how you want the briefing structured, and where you want to receive it.
The builder then gives you one complete workflow prompt.
That generated prompt is what you paste into the platform where you want the monitor to run.
I tested mine with Claude Cowork Scheduled Tasks. I pasted in the generated workflow, selected the schedule, and ran it. It connected to the Amplifiers tools, created the first baseline report, and prepared the monitor for its next run.
Everything worked exactly as planned. Claude ran the first baseline report and delivered the complete watchlist briefing to me in Slack.
One thing to know: the monitor saves its previous conclusions, risks, and things to watch in a simple monitor-log.md file on your computer. This gives every new briefing context, so it can show what changed and whether an earlier risk became real.
A scheduled task using a local file requires your computer to be on. But I found a workaround that keeps the log in the cloud, so the monitor can run while your computer is off.
You won’t need to figure out either setup alone. The prompt builder guides you through everything step by step.
The complete Financial Monitor Builder prompt is below for paid subscribers.
Copy it into your AI assistant, answer a few questions, and in about 10 minutes you’ll have your first automated monitoring workflow ready to run:
