AI stock research tools are software products that use large language models to help investors analyze companies, read filings, compare ideas, and keep track of what they believe and why. In 2026 this category has matured into several distinct families: general assistants that answer questions, cited research engines that return sources, screeners that score every stock, fundamentals terminals that organize filings and estimates, and workflow platforms that connect a thesis to evidence, testing, and follow-up.
The best AI stock research tool in 2026 is the one that fits the job you are trying to do. Every tool in this guide is strong at what it was built for. The practical question is how to match tools to jobs, and what to add when your research needs to survive contact with reality: earnings, guidance changes, new filings, and your own memory of what you believed.
This guide compares seven tools on consistent dimensions, using product and pricing information verified in August 2026, and explains where AlphaVue fits in the research workflow.
How AI stock research tools work in 2026
Before comparing tools, it helps to understand what happens under the hood, because the architecture determines what each tool can and cannot do.
Most AI stock research tools combine four layers. The first layer is a large language model, the reasoning engine that reads text, answers questions, and follows instructions. The second layer is data: structured financials, filings, transcripts, news, and estimates, either pulled live from providers or retrieved from a database. The third layer is retrieval and grounding, the mechanism that connects the model to the specific documents and numbers that should support its answer. The fourth layer is workflow: watchlists, alerts, thesis records, backtests, and portfolio context that let the research persist beyond a single conversation.
Three shifts defined the category in 2026.
First, models became dramatically cheaper to run. In August 2026, Google launched Gemini 3.7 Flash at roughly half the price of its predecessor, and OpenAI opened a limited preview of Ultrafast mode that runs its flagship model up to 14 times faster on Cerebras wafer-scale hardware. Lower inference cost makes multi-step research affordable at scale, which is the difference between asking one question and running a full research pipeline.
Second, finance data moved directly into the assistants. Perplexity expanded its Finance layer with live data tools drawing on SEC filings, FactSet, S&P Global, and LSEG, and added access to Morningstar and PitchBook data in May 2026. Fiscal.ai (formerly FinChat) built a fundamentals terminal with segment-level KPIs and click-through from figures to filings. The tools stopped being purely conversational and started carrying real data.
Third, the category split by job. Screeners like Danelfin own systematic scoring. Terminals like Fiscal.ai own data depth. Framework engines like invest-like own opinionated verdicts. Brokers like Robinhood own convenient digests. Workflow platforms like AlphaVue own the record of the research itself. Understanding which family owns which job is the fastest way to choose correctly.
The two big families of AI stock research tools
The first split is between general AI assistants and investment-specific platforms.
General assistants, represented here by ChatGPT and Perplexity, are broad by design. They can discuss almost any topic, including stocks, but they are not built around the lifecycle of an investment idea. They have no native concept of a thesis, a watchlist with memory, or a test that runs forward in time.
Investment-specific tools make a narrower promise. Danelfin commits to scoring and ranking. Fiscal.ai commits to fundamentals depth. invest-like commits to framework-based verdicts. Robinhood Cortex commits to quick digests inside a broker app. AlphaVue commits to an evidence-linked research workflow with testing and tracking.
| Dimension | General assistant | Investment-specific platform |
|---|---|---|
| Breadth | Can answer almost any question | Focused on the investing workflow |
| Structured finance data | Usually none built in | Terminal, screener, or provider data |
| Citations | Optional, not guaranteed | Often native or explicit |
| Thesis memory | None | Varies; strongest in workflow platforms |
| Testing | None | Screeners and workflow platforms offer it |
| Best use | Fast questions and brainstorming | Decisions you will revisit |
None of these labels is a defect. A general assistant that cites sources can be exactly what you need for a fast question. A scoring engine can be exactly what you need to build a watchlist. The mistake is expecting one tool to cover a job its design never intended.
How this comparison is organized
Each tool below is assessed on the same five dimensions:
- Intended user. Who the product is designed for, from casual retail investors to active researchers.
- Data and sources. Where the information comes from and how deep it goes.
- Citations and source validation. Whether claims can be traced back to a filing, a data provider, or a document.
- Financial workflow depth. Whether the tool supports a thesis over time: thesis memory, testing, forward observation, and portfolio context.
- Pricing and honest scope. What it costs and what it is not designed to do.
Pricing was verified against official product pages, or recent official snapshots, in August 2026. Vendors change plans frequently, so confirm current pricing before subscribing. None of these tools was hands-on tested for this article; capabilities are reported from official sources.
At a glance: the best AI stock research tools in 2026
| Tool | Type | What it does best | Data and sources | Citations | Thesis memory and testing | Pricing (verified Aug 2026) |
|---|---|---|---|---|---|---|
| ChatGPT (Plus) | General assistant | Fast answers, brainstorming, working with documents you upload | Web plus files you provide; no structured finance layer | Optional, via web search; not guaranteed | None | From $20/month |
| Perplexity (Pro) | Cited research assistant | Research with sources attached to each answer | Web plus Finance layer: SEC filings, FactSet, S&P Global, LSEG; Morningstar and PitchBook integration | Native citations on answers | Limited; no persistent thesis | From $20/month |
| Danelfin | AI scoring and screening | Systematic stock selection with AI scores | Fundamental, technical, and sentiment features per stock; AI price forecasts | Publishes track records for its scores | Portfolio alerts and sync; no thesis memory | Free; Plus $22/month; Pro $59/month; Elite $134/month |
| Fiscal.ai (formerly FinChat) | Fundamentals terminal | Deep financials, segment KPIs, and estimate changes | 100,000+ global securities; segment and KPI data; Morningstar equity research; estimates and revisions | Click-through from figures to filings on higher tiers | Dashboards and alerts; no thesis memory | Free; Pro $39/month; Enterprise $199/month |
| invest-like | Framework verdict engine | Value analysis grounded in named investing frameworks | Fundamentals plus retrieval over Buffett letters; seven documented frameworks | Reasoning and numbers behind each framework score | Portfolio score and alerts on Capital tier; no backtesting | Free; Pro EUR 12/month annual; Capital EUR 29/month annual |
| Robinhood Cortex | Broker-embedded digest | Plain-language summaries inside the Robinhood app | Robinhood market data | Informational only, not research | None | Robinhood Gold $5/month (US) |
| AlphaVue | Research workflow platform | Evidence-linked research with testing and thesis tracking | Web, filings, news, and events with an Evidence layer and data-health states | Claims linked to evidence | Multi-agent debate, thesis change log, Strategy Lab backtests, forward tests, portfolio impact | Free; Pro $9.9/month or $99/year |
Pricing reflects official product pages or recent official snapshots as of August 2026; confirm current plans before subscribing.
General assistants: ChatGPT and Perplexity
ChatGPT: a flexible reasoning partner
ChatGPT is the natural starting point for many investors because it is broadly capable. It can help frame a research question, stress-test an argument, and work through annual reports and transcripts you upload. The Plus plan, from $20 per month, includes advanced reasoning models, higher message limits, and larger uploads.
In a research workflow, ChatGPT is most useful in three places. First, framing: turning a vague interest such as "is Microsoft a good stock" into a specific uncertainty such as "does cloud growth support current margin expectations." Second, debate: asking for the strongest case against your view and checking whether it survives scrutiny. Third, document processing: uploading a 10-K or earnings call transcript and asking targeted questions about specific sections.
The limits are structural rather than a fault of the model. ChatGPT does not carry a proprietary finance database, so numbers it recalls from memory may be stale or wrong, and it cannot guarantee that a figure came from a filing. It does not maintain a thesis record across quarters, and it does not test rules against history. When a discussion turns into a decision you want to revisit, the question becomes where that reasoning is stored, how new evidence updates it, and how the original assumptions stay visible.
Perplexity: cited, current research
Perplexity brings sources into the conversation by design. Citations are native: every answer carries links to the pages it used, which is a meaningful improvement over a model that asserts without evidence. The Finance layer adds more than 40 live data tools drawing on SEC filings, FactSet, S&P Global, and LSEG. In May 2026, Perplexity also expanded access to Morningstar and PitchBook data, and Perplexity Computer extends the product toward multi-step research workflows. Pro is about $20 per month.
Perplexity is an excellent way to gather well-sourced evidence on current developments: what changed in guidance, what an analyst said this week, what a competitor disclosed. Because answers carry citations, you can quickly separate claims with provenance from model-generated filler.
For an investor, the natural next step after gathering evidence is organization: which claim matters, which source supports it, and what would change the conclusion. That organization is the job of a research platform. Perplexity is the best source-finder in this list; it is not a thesis keeper.
Screeners and scoring: Danelfin
Danelfin approaches stock research from the screening side, and it is the strongest systematic tool in this comparison. It analyzes hundreds of features per stock and distills them into an AI Score, with separate technical, fundamental, and sentiment signals, plus AI price forecasts and long and short trade ideas. The company publishes historical track records for its scores, including win rates, which is a level of transparency that is genuinely useful for evaluating any signal-based tool.
Danelfin's plans span a usable free tier up to power-user tiers: Free, Plus at $22 per month, Pro at $59 per month, and Elite at $134 per month, with annual discounts and an API on Elite. The free tier includes a daily top-10 newsletter, ten stock reports per month, and one portfolio with five positions. Higher tiers unlock unlimited reports and rankings, automatic portfolio sync with US broker accounts, trading parameters, CSV export, and historical daily scores back to 2017 on Elite.
Danelfin fits investors whose process is signal-driven: building a ranked universe, watching scores move, and acting on alerts. Its published track record also makes it one of the few tools where you can examine how a signal performed historically before trusting it.
The natural complement is a place to turn a signal into a testable belief and observe whether it holds up over time. A score tells you what the model thinks today. It does not record what you believed, why you believed it, or what happened after you acted. That is where a research workflow platform enters the picture.
Fundamentals terminals: Fiscal.ai (formerly FinChat)
Fiscal.ai, formerly FinChat, is built for fundamentals depth, and it is the closest thing on this list to an AI-native equity research terminal. Its strengths include up to 20+ years of financials on the top tier, segment-level KPIs across thousands of companies, adjusted figures, analyst estimates, estimate revisions, and Morningstar equity research integrated into the product. On Pro and above, US stocks support click-through audit from a figure back to the filing, so a number can be traced to its source document.
Pricing from an official snapshot: Free, Pro at $39 per month, and Enterprise at $199 per month, with annual discounts. Some third-party references in 2026 list different figures, so confirm current pricing on the Fiscal.ai site. The free tier is genuinely usable, with ten years of financials and ten AI copilot prompts per month.
Fiscal.ai is a strong choice for earnings season and for investors who live in the numbers: segment breakdowns, estimate trajectory, and as-reported versus adjusted figures. The AI copilot can answer questions about a company with the terminal's data behind it, and the click-through audit path is one of the most rigorous source-verification features in this category.
Apply this research method to your stock
Enter one ticker and get a research summary you can keep exploring.
For the same investors, the complementary need is a durable record of the reasoning around those numbers: what the thesis was, which estimate change mattered, and what was expected before the quarter arrived. A terminal organizes data exceptionally well; the judgment and the follow-up record still belong to you.
Framework verdict engines: invest-like
invest-like takes a judgment-driven approach, and it is the most opinionated tool in this comparison. It scores stocks against seven documented investing frameworks, including Buffett, Graham, Lynch, Greenblatt, Munger, Fisher, and Smith, and explains the reasoning behind each score. The Buffett Brain verdict is the flagship feature, and the Boardroom simulates a four-investor debate on a stock, which is a genuinely useful way to surface disagreement. Ask Buffett answers questions with retrieval over Buffett's letters, an unusually specific source of context.
Pricing: a free tier with three Buffett Brain verdicts per week, Pro at EUR 12 per month billed annually, Capital at EUR 29 per month billed annually with portfolio grading and pre-earnings briefings, and a one-time Founder's Plan at EUR 399.
invest-like is ideal when you deliberately work from named frameworks and want the reasoning laid out clearly. Each verdict shows the numbers behind the score and the reasoning behind the call, so you are not asked to accept a black-box rating.
The natural next step after a framework verdict is validation: expressing the testable parts of the belief as a rule, checking it against history, and observing it forward. Framework fit is a judgment about today; whether that judgment was useful is a question about time, and that is where AlphaVue's workflow picks up.
Broker-embedded digests: Robinhood Cortex
Robinhood Cortex is the convenience option inside a broker app. Cortex Digests generate plain-language summaries of what may affect an asset's price or a portfolio, available to Robinhood Gold members at $5 per month in the US, and free at introduction in the UK. Robinhood describes Cortex features as informational and not research or a recommendation.
Cortex is a useful daily companion for investors who already trade on Robinhood and want quick context on holdings: why a position moved, what an earnings report changed, what the market is pricing. It is the lowest-friction option in this list because it lives where you already trade.
When a digest raises a question worth investigating, the next step is a deeper research workflow that records the question, the evidence, and the conclusion. A digest is a prompt to research, not the research itself.
Research workflow platforms: AlphaVue
AlphaVue belongs to the workflow family: instead of answering one question, it organizes the entire research loop, from a stock idea to a reviewed thesis. It is designed to work alongside the tools above, because it answers a different question: where does the research live over time?
The loop has seven stages, and each has a distinct job.
Frame. The workflow starts with a stock question that defines the decision and the uncertainty, rather than a ticker alone. "Analyze Microsoft" produces a broad answer; "does the new capital expenditure cycle improve the long-term opportunity or weaken near-term cash economics" produces research you can act on.
Research. The company workspace brings together the business overview, financial and operating trends, price behavior, filings, news, and upcoming events, so the variables that deserve deeper investigation become visible.
Challenge. The Agent Command Center runs specialized analytical roles, including bull, bear, valuation, and risk views. Disagreement becomes visible instead of being buried in a short risk paragraph. The result sits beside the bull case, bear case, risk assessment, confidence, evidence, and invalidation conditions, so an investor can inspect the argument rather than accept the verdict on authority.
Verify. Material claims stay close to their sources through an Events and Evidence layer. The workspace exposes data-health states so incomplete or still-preparing information is not silently presented as complete. The practical verification sequence is short: read the conclusion, identify the claims that matter, open the supporting evidence, check the reporting period and source date, and record missing evidence instead of replacing it with a plausible assumption.
Validate. Testable beliefs become explicit rules in the no-code Strategy Lab. The investor defines the rule, universe, time window, and assumptions, and reviews returns, drawdown, benchmark comparison, transaction-cost sensitivity, trade count, and sample coverage. Historical replay examines what could have been known at the time, rather than testing a rule on data that includes the future.
Observe. A surviving rule can be frozen into a forward test that accumulates future observations without backfilling. The point is not to predict the market; it is to record what happens next honestly.
Review portfolio impact. A company-level update is connected to exposure, concentration, correlation, and the consequences of thesis failure, because the same evidence can matter differently to two investors with different positions. A thesis change log preserves what you believed, what changed, and when.
AlphaVue pricing is designed to be accessible: a free plan with 15 AI analyses per month, a 10-stock watchlist, and one Strategy Lab run and forward test per month, and Pro at $9.9 per month or $99 per year with 200 analyses, a 100-stock watchlist, email alerts and daily briefs, portfolio impact and decision history, and a 90-day thesis change log.
AlphaVue is transparent about what it does not replace. It is not a substitute for a data terminal with every metric on demand. Public data can be delayed, incomplete, or revised. Backtests can overfit. Forward tests can stay inconclusive for long periods. Portfolio scenarios depend on the assumptions and positions you supply. AlphaVue provides research support and is not personalized investment advice. What it adds is the part most tools leave out: a durable, testable record of the research itself.
Side-by-side: capability matrix
The table below compares the seven tools across the capabilities that matter most to a research process. A check means the capability is a core part of the product as documented; a limited rating means it exists in partial form; a dash means it is not part of the product.
| Capability | ChatGPT | Perplexity | Danelfin | Fiscal.ai | invest-like | Robinhood Cortex | AlphaVue |
|---|---|---|---|---|---|---|---|
| Structured financial data | No | Finance layer | Yes | Deep | Fundamentals | Broker data | Evidence-linked |
| Native citations or source links | Limited | Yes | Track records | Click-through (Pro+) | Framework reasoning | No | Evidence layer |
| Thesis memory over time | No | No | No | No | Journal (Capital) | No | Thesis change log |
| Backtesting | No | No | Yes (track records) | No | No | No | Strategy Lab |
| Forward testing | No | No | No | No | No | No | Forward tests |
| Portfolio context | No | No | Portfolio sync | Portfolio stats | Portfolio score | Holdings context | Portfolio impact |
| Alerts and briefings | No | No | Alerts | Notifications | Alerts and briefings | Digests | Alerts and daily briefs |
| Free tier | Yes | Yes | Yes | Yes | Yes | Via Gold | Yes |
| Price point | $20/mo | $20/mo | From $22/mo | From $39/mo | From EUR 12/mo | $5/mo (Gold) | From $9.9/mo |
The matrix makes the division of labor visible. Data depth lives in Fiscal.ai. Scoring lives in Danelfin. Cited answers live in Perplexity. Framework reasoning lives in invest-like. Convenience lives in Robinhood. Testing, memory, and portfolio context live in AlphaVue. For many investors the practical answer is not one tool but a combination.
What reliable AI stock research looks like
Whether you use ChatGPT, Perplexity, a terminal, a screener, or AlphaVue, reliable AI stock research tends to share four properties.
First, claims connect to sources. The most useful research makes the path from a conclusion to its supporting evidence short, so material claims can be checked before they influence a decision. Ask yourself: if the answer asserted a number, can you open the document that contains it within a click or two?
Second, data freshness is visible. Good research tools signal when information is incomplete, delayed, estimated, or still being prepared, instead of presenting everything with equal confidence. A figure labeled as reported, adjusted, or estimated means something different for your decision, and the tool should make that distinction explicit.
Third, the thesis has a memory. Research is a process that continues across earnings seasons. When the original belief, the supporting assumptions, and the later evidence are all preserved, it is possible to tell whether the thesis is actually changing or just being remembered more favorably. This is the quiet failure mode of most AI research: a one-off answer cannot be audited later.
Fourth, testable beliefs get tested. If a belief can be expressed as a rule, it can be checked against history and observed forward. That is how a narrative becomes evidence. A signal that looked great in hindsight is not the same as a signal that was recorded, frozen, and observed honestly.
AlphaVue is designed around exactly these four properties: the Events and Evidence layer for source verification, data-health states for freshness, the thesis change log for memory, and the Strategy Lab and forward tests for validation.
How to build a research workflow with these tools
The tools in this guide are strongest when combined. A practical workflow for a single stock might look like this.
Start with screening. Use Danelfin to surface candidates and understand what the market's signals say about each one. Build a short watchlist of companies that pass your initial filters.
Frame the question. For the first company on your list, write the exact uncertainty you are investigating. This is the step most investors skip, and it is the step that determines whether the rest of the research is useful.
Gather evidence with citations. Use Perplexity for current developments: recent guidance, analyst moves, competitive news, and the data points behind them. Keep the claims that matter, with their sources.
Go deep on the numbers. Use Fiscal.ai to pull segment financials, estimate revisions, and as-reported versus adjusted figures for the current and prior periods. Check whether the market's expectations are rising or falling.
Form a testable view. Translate the testable part of your belief into a rule: for example, a combination of price trend, earnings behavior, and valuation that defines a setup. This is where a workflow platform earns its place.
Keep the record and test forward. Use AlphaVue to build the case, surface the bull and bear arguments, link material claims to evidence, run the rule through the Strategy Lab, and freeze a surviving version into a forward test. Record what you believed and why, then let future observations accumulate without backfilling.
Review with portfolio context. When the thesis changes, check what it means for your exposure, concentration, and risk contribution before acting on the update.
The same loop works for other triggers. Before earnings, the loop becomes: pull the estimates and revisions (Fiscal.ai), read what the market is pricing (Perplexity), define what would confirm or invalidate the thesis (AlphaVue), and record the expectation before the report lands. After earnings, you update the thesis, link the new evidence, and check portfolio impact. Over time, the value of the workflow is not any single answer; it is the accumulating record that makes your own decisions inspectable.
Three scenarios in practice
Scenario one: preparing for earnings season
Earnings season is where the difference between tools becomes visible, because it is a race between expectation and outcome.
Two weeks before a company reports, start with the numbers. Pull the current estimates and the revision trend in Fiscal.ai. Are analysts raising or cutting numbers, and in which segment? That tells you what the market already expects.
Next, use Perplexity to read what changed in the quarter that is about to end: guidance from the company, competitor disclosures, regulatory news, and any data points with citations you can open yourself. Collect the claims that would change your view if they proved true.
Then define the expectation in AlphaVue before the report lands: which metrics confirm the operating thesis, which guidance change would invalidate it, and what valuation threshold changes the expected return. Record the date, the expectation, and the evidence. When earnings arrive, the comparison is no longer between the stock price and your memory; it is between the actual result and a documented expectation.
Scenario two: building a watchlist from a crowded screen
A screener can produce more candidates than you can reasonably research. The workflow that follows is what separates useful selection from random sampling.
Start with Danelfin and set your filters: score thresholds, signal types, and a universe you understand. Export the shortlist and resist the urge to act on the score alone. For each candidate, run the two-question test: what is the specific uncertainty that matters for this company, and what evidence would resolve it? Most candidates fail this test quickly, which is exactly what a filter is for.
For the survivors, go deeper with Perplexity for current developments and Fiscal.ai for segment economics. Then carry the strongest candidates into AlphaVue, where each one gets a framed question, a bull and bear challenge, an evidence link, and a decision about whether any part of the thesis deserves a real test.
The result is a watchlist built from a ranked screen but filtered by structured reasoning. Neither step alone is enough; the screen gives you breadth, and the reasoning gives you depth.
Scenario three: reviewing a thesis after a surprise
A thesis that was recorded is reviewable; a thesis that only existed in a conversation is not.
When a stock surprises you, the first move is to open the original research record: what did you believe, which assumptions were fragile, and what did you say would invalidate the view? In AlphaVue, the thesis change log preserves exactly that, including the date of each change.
Then collect the new evidence with citations, check whether it changes the assumptions that actually supported the thesis, and update the record rather than rewriting history. Finally, look at portfolio impact: how does this update interact with your exposure, concentration, and correlation? A company-level surprise is not automatically a portfolio decision, and the workflow is designed to keep those two questions separate.
Limitations to keep in mind
No tool in this guide removes the investor's responsibilities, and the honest framing matters more than the marketing.
Public data can be delayed, incomplete, revised, or affected by upstream errors. AI output can omit relevant context or make mistakes, even with strong retrieval. Citations reduce the risk of hallucination but do not eliminate it, and a source link is only as good as the source itself. Backtests can overfit to the period they were run on, and forward tests can remain inconclusive for long periods. Portfolio scenarios depend on the assumptions and positions you supply, and no tool knows your required return, time horizon, liquidity needs, taxes, or personal constraints.
The purpose of a good research workflow is not to remove those responsibilities. It is to make the research around them more explicit, repeatable, and inspectable, so that when you are wrong, you can see where the reasoning failed.
How to choose an AI stock research tool
- Choose ChatGPT when you want a flexible reasoning partner for framing questions and working through documents you provide, and you will handle source checking and record-keeping yourself.
- Choose Perplexity when you want cited, current answers and finance data with provenance, and you are comfortable assembling the research record elsewhere.
- Choose Danelfin when your process is systematic screening and you want ranked signals, alerts, and portfolio scoring rather than written theses.
- Choose Fiscal.ai when fundamentals depth matters most, especially segment KPIs, estimates, revisions, and filing audit trails during earnings season.
- Choose invest-like when you deliberately work from named value frameworks and want the reasoning behind each score explained.
- Choose Robinhood Cortex when you trade on Robinhood and want quick plain-language context on holdings without leaving the app.
- Choose AlphaVue when you want the research itself to be evidence-linked, testable, and tracked over time, from the first stock question to the portfolio review.
Budget matters too. If you want a single subscription, ChatGPT Plus or Perplexity Pro at $20 per month give you the broadest coverage. If you want data depth and are willing to pay for it, Fiscal.ai's Pro tier is the strongest terminal option. If you want the cheapest full workflow, AlphaVue's free tier and $9.9-per-month Pro plan are the most accessible entry points in this comparison, with Danelfin's free tier as the best zero-cost screener.
Many investors combine tools rather than choosing one. A common setup is a cited research engine for current events, a terminal for data depth, and AlphaVue as the workspace where the thesis, the evidence, and the tests live together.
The takeaway
The best AI stock research tool in 2026 matches the job you actually do. General assistants are the most flexible, cited research engines are the most source-transparent, screeners are the most systematic, terminals are the deepest on data, framework engines are the most opinionated, broker digests are the most convenient, and workflow platforms are where the research itself becomes a durable record.
The deeper lesson of 2026 is that the tools have specialized, and the winning move is to combine them deliberately. Use screeners to find candidates, cited research to gather evidence, terminals to verify the numbers, and a workflow platform to record the thesis, test the rules, and observe what happens next.
If you want to see the workflow family in action, start with one stock and one uncertainty. Use AlphaVue to build the case, inspect the disagreement, verify the evidence, and decide which part of the thesis deserves a real test.