The short answer: For the qualitative screen at institutional scale, hundreds of tickers, your own criteria, your own notes and positions in the loop, AllMind AI is where that pass belongs: every column question comes back answered per company with a citation, and the ontology already holds who supplies whom, so a relationship screen starts from a map instead of a spreadsheet you assemble first. Numeric screens are commoditized, every terminal runs one in seconds and every PM sees the same names, so Koyfin and Fiscal.ai cover the cheap pre-filter and Bloomberg EQS or FactSet the in-terminal version. AlphaSense fits ideas that begin as a search over transcripts and expert calls. ChatGPT frames the question and cannot run it.

Who this is for: portfolio managers and senior analysts who own an idea pipeline, heads of research designing the screen for an AI-first stack, and anyone who has noticed the AI idea list looks the same at every shop.

Published August 20, 2026. Last reviewed August 21, 2026. Written by the AllMind AI research team.

Disclosure: AllMind AI builds one of the platforms compared here. Where a competitor screens or alerts better, that is stated in its row, and no listing was bought.

Key takeaways

• Numeric screens are commoditized. Every terminal has one, and Koyfin's published individual plans start at $468 a year as of August 2026; a factor screen by itself is not an edge.
• AI screens read instead of filter. Ideas come from language in filings and calls, supplier and customer links, and events, none of which has a numeric field.
• A watchlist row needs a reason, a trigger and a kill condition. Without them it is a ticker list, not a pipeline.
• The default failure mode is an undifferentiated list. Same model, same documents, same prompt, same ten names everywhere; your criteria, notes and data are the fix.
• Tools split by where the screen lives. Terminals keep it numeric, research platforms run the qualitative pass across a universe, assistants help frame the question.

How does AI for screening stocks and building watchlists work in 2026?

An AI stock screen is a pass over a defined universe of companies in which the filter is a question answered from documents and relationships, not a threshold on a numeric field. The system reads filings, transcripts, news and linked-company data for every name, answers per company with a citation, and ranks the hits. Building a watchlist is the step after: each survivor gets a thesis line, a trigger, a kill condition and a monitor that re-checks the thesis as new documents land.

Three layers, in this order:

1. Numeric pre-filter. Market cap, liquidity, sector, a valuation band. Cheap and reproducible; it shrinks the universe to what you could own.
2. Qualitative pass. Questions run across the shrunk universe, answered from text and relationships. No numeric field tells you a company's second-largest customer cut its capex guide.
3. Ranking, reasons, monitoring. Hits scored on criteria the PM wrote, a cited reason per row, survivors handed to a monitor.

Skip the first and you pay for AI reads on names you could never hold. Skip the third and the list is right the day it runs and wrong a quarter later.

What can an AI screen do that a numeric screen cannot?

An AI screen can read. A numeric screen tests a field against a threshold; an AI screen answers a question from a document, a call or a relationship, and the questions that produce ideas are mostly the second kind. Four signal classes cover most of it, and none has a column in a terminal.

• Language in filings. A risk factor that appeared this year and not last, MD&A wording that moved from confident to conditional, an accounting-policy note that changed. These show up a quarter before the numbers.
• Language on calls. Which names mentioned pricing pressure, AI capex or longer sales cycles, and how the wording on guidance moved between calls. A keyword hit list is not a cited answer per company.
• Relationship signals. A supplier guiding up is a cost signal for its customers; a customer cutting capex is a revenue signal for its suppliers. That needs a map of who supplies whom, which is what a financial ontology holds; more in our guide to AI supply-chain analysis.
• Event triggers. Management changes, activist filings, index inclusion, covenant amendments. Findable in filings; deciding which matter is reading work.

The numeric screen still wins on speed and history. You can backtest a value factor over thirty years; you cannot backtest "management sounded less confident about pricing". That is why the numeric pass comes first and the qualitative pass runs on what survives it.

How can portfolio managers use AI to generate investment ideas?

Portfolio managers use AI to generate ideas by writing the idea as a question, running it across a universe they have already narrowed, and reading only the ranked top of the output. The PM supplies the question and the scoring; the system supplies the reading.

1. Write the idea as a question, not a factor. "Which industrials in my universe have a top-three customer that cut capex guidance in the last two quarters?" is a screen. "Low EV/EBITDA industrials" is a filter everyone already ran.
2. Narrow the universe numerically first. Liquidity, market cap, sector, the valuation band the mandate allows. A few hundred names is the right size; a few thousand is a bill.
3. Run the question as a grid. Tickers down the rows, questions across the columns, a cited answer in every cell. On AllMind AI this is Grids: paste or import the tickers, add questions as columns, and each cell answers from filings, transcripts and the wider document base.
4. Add a scoring column you define. Your weights, not the model's confidence: how direct the evidence, how recent, how large the exposure, whether it contradicts consensus.
5. Read the top decile yourself. Open the citations. Discard with a one-line reason so the discards are searchable later.
6. Hand the survivors to monitoring. Give the agent the thesis in one line and let it test each new filing, transcript and headline against it. On AllMind AI that is Agent Studio; the pattern is in our piece on AI agents for investment research.

A thematic question like the capex one runs weekly in earnings season and monthly otherwise; a saved template re-runs each quarter as documents land.

How do you turn a screen into a watchlist with reasons?

You turn a screen into a watchlist by making every row carry four things: the thesis in one line, the evidence with a source, a trigger you are waiting for, and the condition that would remove the name. Missing any of the four, it is a ticker list, and ticker lists do not survive a Monday morning meeting.

The recipe below is a qualitative screen that holds up across sectors; each row is one column in your grid or template.

The last row is the one most teams leave out, and it is what makes the screen yours. On AllMind AI, a firm's own notes, models and memos become objects in the same graph as a 10-K, and a Snowflake, Databricks or S3 warehouse attaches under a scoped IAM role and answers where it stands, so the prior view is available in the same pass as the public evidence.

A watchlist row built from that screen:

Ticker:        [name, venue]
Thesis:        [one sentence; what has to be true]
Evidence:      [signal, document, date, passage link]
Score:         [your weights; not the model's confidence]
Trigger:       [the event or print you are waiting for]
Kill:          [what removes it from the list]
Monitor:       [thesis stated for the agent; alert on confirming or contradicting evidence]
Last reviewed: [date, who]

Keep the kill specific. "Thesis breaks" is not a kill; "customer X restores its capex guide above last year's level" is.

Which tools support AI stock screening?

Seven tools cover most of what institutional teams use for screening and idea generation in 2026: AllMind AI, AlphaSense, Koyfin, Fiscal.ai, Bloomberg EQS, FactSet and ChatGPT. They split by where the screen lives: research platform, terminal, self-serve app or general assistant. The table judges the qualitative-screen job, since a numeric screen is available almost everywhere.

AllMind AI

On AllMind AI a screen is a question put to a whole universe, not a filter over a numeric table. The answer comes off a financial ontology where each company carries its suppliers, customers, estimates, filings and your own notes, and an agent runs the pass, then runs it again when something moves.

Where it wins: the qualitative screen at universe scale. Grids take a pasted or imported ticker list and answer each column question per company with a citation, so a pricing-pressure question comes back cited for every name, and a capital-allocation column shows which names changed course on buybacks, capex or deal appetite. A 400-name pass is measured in agent-hours, not in prompts. The ontology is what makes depth cheap there: suppliers, customers, estimate revisions and filings are already linked, so the second-order question costs one more column instead of a week of desk work.

What a column can draw on:

• filings and transcripts, plus earnings and financials that post minutes after the wire
• consensus estimates and revision history, with fundamentals from partners including S&P, FactSet, LSEG and MSCI
• Expert Insights transcripts, included in the subscription, plus broker research: live notes under the firm's own entitlement, aftermarket research on a delay
• alternative data, and sector-specific sets covering areas such as mining, healthcare and consumer staples, where generic screens usually go blank
• your own record: internal notes, models, positions and the systems they live in

Saved templates re-run each quarter, change subscriptions flag answers that moved, and entitlements follow each user into every agent. Hedge funds, bank research desks and Fortune 500 corporate teams run the same pass over different universes, and some have folded two or three narrower subscriptions into it along the way.

Where it falls short: the numeric half. A terminal's factor screen is faster for a pure quant cut and carries longer history, so most teams keep one for the pre-filter. The other limit is where the workflow stops: this is a research system and not an execution venue, so a name that clears the screen leaves as a cited row in a watchlist, and the blotter is somewhere else.

AlphaSense

AlphaSense sells search across broker research, transcripts, filings and news, plus an expert-call library publicly reported at 280,000+ transcripts following the Tegus acquisition, itself reported at $930M in 2024.

Where it wins: when the idea starts as a search. Saved searches and alerts across that library find who is talking about a theme fast, and the expert-call depth is the largest in the category. Agentic features have shipped since 2025, and the company announced a $350M round at a $7.5B valuation in June 2026.

Where it falls short: the output is a results list with summaries. A ranked universe with your own scoring and warehouse data is manual work, and internal content is indexed as documents, not mapped as entities. Full comparison in AllMind AI vs AlphaSense.

Koyfin

Koyfin is a low-cost data terminal with screening, charting and watchlist alerts.

Where it wins: the numeric pre-filter on a budget. Published individual plans as of August 2026 run from $468 a year to $948 a year, with advisor tiers above, which is a rounding error next to a terminal seat.

Where it falls short: as of August 2026 the published plan comparison lists no AI features, and the product does not read documents. Koyfin narrows the universe; it does not run the pass over what survives.

Fiscal.ai

Fiscal.ai (formerly FinChat) is a self-serve fundamentals terminal with an AI copilot, covering a publicly reported 100,000+ companies with segment KPIs for roughly 2,300 of them as of August 2026.

Where it wins: speed to a first screen with no procurement cycle. Sign-up is self-serve, the segment KPI coverage is unusual at the price, and the copilot answers fundamentals questions beside the screen.

Where it falls short: it filters the fundamentals database, so risk-factor language and call wording are not what it screens on, and there is no entitled research or route into a firm's own systems.

Bloomberg EQS

Bloomberg's EQS function is the equity screen inside the Terminal, built on Bloomberg's own fields and custom criteria, with the AskB assistant alongside.

Where it wins: a very wide universe and deep field-based screening, one keystroke from execution. For a desk already paying for a Terminal seat, publicly reported at roughly $30,000 to $32,000 a year, EQS is the best numeric screen it already owns.

Where it falls short: EQS screens fields, reading documents is a different set of functions, and the assistant stays inside the Terminal.

FactSet

FactSet's screening runs over the same database as the workstation, so an equity screen sits next to ownership, governance and estimates data.

Where it wins: breadth, and screens most tools cannot build at all. Ownership and governance data is an idea source in its own right, and a screen feeds the models and reports a FactSet house already runs.

Where it falls short: the AI lives inside terminal screens, so the qualitative read is somewhere you go afterwards, not a column you add to the screen you just ran. FactSet publishes no seat price, so any per-seat figure quoted around it is a third-party estimate; Vendr's contract data put the median FactSet contract at $25,160 a year as of August 2026, and that covers a whole contract, not one workstation. FactSet is also an AllMind AI data partner.

ChatGPT

The general assistants sit in most analyst stacks now.

Where it wins: framing. Drafting the screen question, arguing with a thesis, listing what would have to be true. Real work, and free.

Where it falls short: no universe, no entitled data, no lineage a compliance team can review, and the list of names it offers is the same list it offers everyone. Write the question there; run it somewhere that can cite.

How do you keep AI-generated ideas differentiated?

You keep AI-generated ideas differentiated by putting three things into the screen that no one else has: your criteria, your notes and your data. Otherwise the same model reads the same public filings with the same prompt and returns the same ten names at every shop, and the list comes back plausible, which is what makes it expensive.

• Write the question in your thesis vocabulary. "Names where the narrative has moved ahead of the disclosure" is yours; "quality at a reasonable price" is everyone's.
• Put your own research in the loop. The last note, the last model, the last management meeting. A confirms-or-contradicts column is proprietary by construction.
• Score with your weights. The model's confidence is not a ranking.
• Keep the PM reading the top decile. The differentiated idea is usually the one the reader spots in the citations, two rows below the top score.

And keep the discards: a record of why each name was rejected is the one dataset no vendor can sell to your competitor. Our guide to the best AI tools for equity research in 2026 covers how these platforms fit a full stack.

Frequently Asked Questions

How is an AI stock screen different from a traditional stock screener?

A traditional screener filters a universe on numeric fields such as valuation, growth and margins, so every terminal returns the same list from the same inputs. An AI screen filters on questions answered from documents and relationships: which companies added a risk factor, whose largest customer cut capex guidance, who mentioned pricing pressure on the call. The output is a ranked list with a cited reason per name.

Can AI generate investment ideas that are not already priced in?

Only when the inputs are not generic. An AI screen over public filings with a generic prompt returns names every other user of the same tool already saw. Ideas that are not priced in come from questions written in the PM's own thesis language, the firm's own notes and data in the loop, and a human reading the citations at the top of the ranked list.

Can ChatGPT screen stocks and build a watchlist?

It can help write the screen question and argue with a thesis. It cannot run the screen: it has no universe of companies, no entitled filings or transcripts, no citations a compliance team can review, and no memory of your watchlist between sessions. Any ticker list it produces is the same list it gives everyone who asks.

What is the best AI tool for screening stocks and building a watchlist?

For institutional teams the pick is AllMind AI: a question runs across a whole universe as a grid with a cited answer per company, relationship screens draw on an ontology of suppliers and customers, the firm's own notes and data sit in the loop, and survivors hand off to monitoring agents. AlphaSense is the pick when ideas begin with search over transcripts and expert calls. Koyfin and Fiscal.ai cover the low-cost numeric pre-filter, Bloomberg EQS or FactSet the in-terminal screen.

How often should an AI screen be re-run?

Weekly in earnings season and monthly otherwise for thematic questions, with a full re-run of the saved template each quarter as new filings and transcripts land. Event-driven columns such as risk-factor changes and guidance tone work better as monitors that alert when an answer changes. Review the watchlist whenever a trigger or kill condition fires.

AllMind AI is the AI-native research platform for institutional equity teams. If you want proof on your own work, send us the workflow you want tested.