The short answer: for a wealth manager whose own people write the stock research, work that ends up in a client file with numbers someone else relies on, AllMind AI is the platform that fits. A holding write-up there runs off filings, LSEG estimates, entitled broker research and the firm's own frameworks at once, and every figure opens the passage it came from. Below that line the split is real: an advisor running ETF models with a handful of single names is better served by Koyfin or YCharts, with Fiscal.ai as a cited copilot on filings. AlphaSense is the buy when broker research and expert calls are what the firm wants, FactSet if the seats are already paid for. ChatGPT drafts client letters well and is the wrong place for the numbers of record.

Who this is for: RIA principals and CIOs, wealth-management research leads, portfolio managers at investment counsel firms, and the compliance officer who signs off.

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, and it is built for institutional teams. We name the cases where a self-serve tool is the better purchase, and nothing below was paid for.

Key takeaways

• Most RIAs do not need an institutional research platform. ETF models plus a short list of single names is covered by a self-serve terminal and a copilot.
• The line is crossed when stock research becomes a document someone else relies on. Client files need sourced numbers and an audit trail; that is where a charting terminal stops.
• Price gaps are large, and only partly public. Koyfin's published plans run $39 to $299 a month as of August 2026; a third-party review put a YCharts Professional seat near $6,000 a year in April 2025; FactSet publishes no seat price at all, and Vendr's anonymized contract data puts the median FactSet contract at $25,160 a year.
• The SEC is looking. Its fiscal 2026 exam priorities, released November 17, 2025, name AI use by advisers, and the March 18, 2024 AI-washing settlements tested two advisers' AI claims against what their tools did.
• ChatGPT belongs in the stack, outside the file. It turns a finished note into a client email well; it holds no licensed data, no lineage and no record of what it was asked.

What does an RIA need from AI equity research that an institution does not?

An RIA needs research it can explain to a client and defend to an examiner, on a budget that is a rounding error next to a hedge fund's data spend. Institutions buy depth across hundreds of names; an advisory firm covers a concentrated book and explains it to people who do not read 10-Ks. Three requirements decide the tool:

• Client-facing explanation. The note has to become why we hold this for you, in plain language, often the day a client calls about a headline.
• Fiduciary documentation. Every holding in a discretionary account needs a current rationale on file, with numbers that trace to a filing, not to a chat window.
• Cost proportional to the book. Two researchers at a $400 million firm cannot justify a terminal per seat; a team of four at $3 billion can.

AI tools for RIAs and wealth managers doing equity research: the 2026 field

Eight tools cover most of what RIAs and wealth managers shortlist for equity research in 2026, and they split by whether the firm employs anyone to write research. AllMind AI, AlphaSense and FactSet assume a research team. YCharts, Koyfin and Fiscal.ai are self-serve and priced for small firms. Morningstar Direct Advisory Suite is an advisor platform adding AI, and ChatGPT is already open on every desk.

AllMind AI

AllMind AI is built for institutional research desks: agents working over a maintained map of companies, estimates, filings and the firm's own notes, with every number traced back to the passage it came from. The same system is bought by banks, hedge funds and Fortune 500 corporates, which is why it fits a wealth manager once the research function looks institutional, however few people staff it.

Where it wins: the closest fit for an advisory firm is the asset management workflow AllMind AI runs for long-only managers and investment counsel firms, where research is three or four people and there is no data engineer. Five things matter at that size:

• Write-ups per holding. A know-your-product write-up runs across the book in the firm's own template, every figure carrying the source it came from.
• What sits behind the write-up. 6,800+ datasets, including FactSet fundamentals, LSEG (IBES) estimates, S&P Global and MSCI data, EDGAR and SEDAR filings, global investor-relations material for the non-US holdings, earnings and financials that post within minutes. Broker research comes in under the entitlements the firm holds, and where it holds none, aftermarket notes still arrive on a delay. Expert Insights transcripts are part of the subscription. A TSX-listed holding runs the same way a US one does.
• The house view loaded in, and the rest of the firm's systems with it. Your white papers, frameworks and DCF conventions sit in the room for a name, so the analysis argues from your material and sell-side opinion stays out. The same connection reaches portfolio and CRM extracts, internal dashboards and APIs, and anything in a Snowflake, Databricks or S3 warehouse, which is read where it sits through a scoped role instead of being copied into another vendor.
• Relationships the analyst would otherwise chase by hand. Because holdings, suppliers, customers, estimates and the firm's last memo are connected as entities, a client question about a headline resolves across all of them in one pass instead of four searches.
• Citations that travel, and a record by default. A citation opens for whoever you send the write-up to, seat or not, so a client or committee member can check a number without a login. Every question and export is logged, and customer data is not used to train models.

The work this pays for is long: a full book refresh across forty holdings is an agent running for a stretch of hours, not a chat answer, and multi-family offices that reached that cadence have folded two or three separate subscriptions into it.

Where it falls short: AllMind AI is built for institutional teams and priced by quote, and the jobs it is designed around repeat every week or every quarter. An advisor with three ETF models and five single names does not generate that cadence and should buy elsewhere. A three-person research team that publishes write-ups every quarter does, and it is the size where one system covers what the firm would otherwise buy from several vendors. It is not a trading or execution terminal. Getting real depth also means connecting the firm's own systems, which is a scoping conversation with whoever runs your data, not a signup.

YCharts

YCharts is a research, charting and proposal platform for financial advisors: fundamentals, screeners, an Excel add-in, model portfolios, a proposal builder, and AI-assisted answers layered on YCharts' own dataset.

Where it wins: it was designed around the advisor's week: screen, chart, build the model, generate the proposal with talking points, share firm-wide. Nothing else here covers that many advisor steps.

Where it falls short: the AI works over YCharts' own dataset, so it will not read a 10-K for you or hold the firm's notes, and YCharts does not publish which tier includes it. Prices come by phone; a WallStreetZen review last updated April 4, 2025 put the Professional tier at $500 per user per month billed annually ($6,000).

Koyfin

Koyfin is a self-serve market data and charting platform with dashboards, screens, estimates and fundamentals, plus model portfolios and branded client reports on its Advisor plans.

Where it wins: price, and a free tier to start on. Koyfin's published plans run $39 a month for Plus, $79 for Premium, $209 for Advisor Core and $299 for Advisor Pro as of August 2026.

Where it falls short: the pricing page listed no AI features on any plan when we checked it on August 20, 2026. Koyfin is where you look at a company; the research still gets written somewhere else.

Fiscal.ai

Fiscal.ai (formerly FinChat) is a fundamentals terminal and API with an AI copilot. It markets coverage of 100,000+ public companies, with segment-level KPI breakdowns on roughly 2,300 of them (vendor figures, August 2026).

Where it wins: it is the best self-serve answer to what an RIA asks about a single name: what the segments are doing, what management said, where that number sits in the filing. The copilot cites.

Where it falls short: no broker research, no expert content, and no way to load the firm's own notes or templates, so the write-up is still assembled by hand. Nothing logs what was asked.

Morningstar Direct Advisory Suite

Morningstar Direct Advisory Suite is Morningstar's advisor platform for research, portfolio analysis and proposals. On March 9, 2026 Morningstar added an AI assistant inside it.

Where it wins: for a practice standardized on Morningstar data and ratings, the assistant handles rating-change alerts, meeting briefs, research support and turning a client statement into a proposal, inside the platform. Morningstar states client data is not used to train models.

Where it falls short: the center of gravity is funds, managers and portfolios, so single-stock depth trails the fundamentals terminals. Morningstar's own release describes the initial rollout as a subset of US-based users, with broader US and Canadian availability anticipated through 2026.

AlphaSense

AlphaSense sells search over broker research, expert call transcripts, filings and news; its expert library is publicly reported at 280,000+ transcripts as of August 2026.

Where it wins: if the firm wants to read what the sell side and former executives think about a name, AlphaSense is the deepest library anyone can buy, and the summaries are fast.

Where it falls short: it is priced for enterprises and quoted, and most advisory firms would use a small fraction of the library. It searches and summarizes; the write-up is still your job. Cheaper routes to the same content are in our AlphaSense alternatives guide.

FactSet

FactSet is a data terminal with deep fundamentals, estimates, ownership and portfolio analytics, and AI assistants that now sit inside the workstation.

Where it wins: a wealth manager already holding FactSet seats for portfolio analytics gets a serious equity research surface for no extra spend, and the data quality is the category's reference point. AllMind AI is a FactSet data partner; firms that move up-stack usually keep it.

Where it falls short: the seat is quoted, never listed, and the contract sizes procurement trackers report are hard to justify for two researchers at a $500 million RIA. The AI also stays in terminal screens without assembling a document. See AllMind AI vs FactSet.

ChatGPT

ChatGPT (or Claude, Gemini, Perplexity) is a general assistant that advisory firms use daily for drafting, summarizing and explaining.

Where it wins: converting a finished note into a client-ready paragraph, explaining why free cash flow conversion fell, rough-drafting a quarterly commentary. Cheap, fast and good at all three.

Where it falls short: it holds no licensed data, its numbers do not trace to a filing unless you paste the filing in, and consumer plans keep no firm-side record of prompts and outputs. Compliance counsel generally advise treating AI-drafted text that reaches a client as a firm record under the Advisers Act books-and-records rule, so the firm has to capture it.

Which tier fits which firm?

Which tier of tooling fits a wealth management firm is decided by the research model, not by the AUM figure or the staff count. A $2 billion practice run entirely through ETF models belongs in the first row; a $600 million firm with a 25-stock book and a committee that reads the working belongs in the third.

When should a wealth manager move to an institutional AI platform?

A wealth manager should move to an institutional AI platform when three things are true at once: someone is paid to write stock research, the output goes into a file that a client, a committee or an examiner relies on, and the book has enough single names that the writing is always behind. Below that threshold the self-serve tier is the right size. The signals are mundane:

• A holding-rationale file that is always a quarter behind.
• A committee that asks for the working behind a number.
• A compliance officer asking where a figure in a client letter came from.
• Two analysts covering overlapping names with notes in separate places.

At that point the comparison is against analyst hours, not against a $468-a-year subscription; AllMind AI's pricing is quoted per firm. The scoring method is in how to pick an AI tool for asset management, the wider ranking in best AI stock research tools for professional investors, and the family-office version of the fork in best AI tools for family offices.

How do RIAs document AI-assisted research for compliance?

RIAs document AI-assisted research the way they document an analyst's draft: as a record with an author, a date, sources and a reviewer. The SEC's fiscal 2026 examination priorities, released November 17, 2025, say examiners will check that representations about AI are accurate, that controls match disclosures, and that AI-assisted advice fits the client's profile.

The March 18, 2024 settlements with two advisers that overstated their AI use, $400,000 in combined penalties, were brought under the Advisers Act antifraud provisions, the Marketing Rule and the compliance-program rule. The documentation an RIA already keeps is the documentation that covers AI. Five habits cover most of what an examiner asks for:

• Keep the output and its sources together. A write-up whose figures open to the filing passage documents itself; a chat transcript with pasted numbers does not.
• Log questions and outputs for research that reaches a client or committee. Institutional platforms do this by design; with a consumer assistant the firm builds it.
• Record human review. Who read it, what changed, when it was approved.
• Describe the AI accurately in Form ADV, marketing and client communications. The 2024 cases were about saying more than the tool did.
• Get the vendor's data terms in writing. No training on client data, encryption in transit and at rest, an audit report on request. AllMind AI has held SOC 2 Type II certification since November 2025 and sets those terms out on its security page; ask every vendor for the equivalent.

Frequently Asked Questions

What are the best AI tools for RIAs and wealth managers doing equity research?

For a firm whose own people write the stock research that lands in client files, AllMind AI is the fit, because a write-up there runs off filings, LSEG estimates, entitled broker research and the firm's own frameworks at once, with every figure opening its source. AlphaSense is the alternative when broker notes and expert calls are the point of the purchase. A firm running ETF models with a few single names, and writing no research of its own, needs none of that and is better served by Koyfin or YCharts for data with Fiscal.ai as a cited copilot on filings.

Is AllMind AI a fit for a small RIA?

It depends on whether the firm writes its own research, not on how many people it has. AllMind AI is built for institutional teams and its value shows up in work that repeats: know-your-product write-ups, scheduled sector briefings, earnings reviews every quarter. A three-person research team producing that cadence is a strong fit, and since there is no self-serve tier, it starts with a scoping conversation. An advisor running ETF models with a handful of single names gets better value from a self-serve terminal and a copilot.

Can an RIA use ChatGPT for stock research on client accounts?

For drafting and explanation, yes; as the source of the numbers in a client file, no. ChatGPT holds no licensed market data or filings, its figures do not trace to a document unless the document is pasted in, and consumer plans keep no firm-side record of what was asked. Treat AI-drafted text that reaches a client as a firm record to capture and to have a person review before it goes out, and confirm that treatment with your own compliance counsel.

How much do AI equity research tools cost a wealth management firm?

The spread is wide at the low end and opaque at the high end. Koyfin's published plans ran $39 to $299 a month as of August 2026, a third-party review put YCharts Professional near $6,000 per seat a year in April 2025, and FactSet publishes nothing, leaving procurement trackers to estimate a seat at anywhere from about $4,000 to $50,000 a year. Institutional AI research platforms including AllMind AI quote by seat count and data entitlements, so the comparison there is against analyst hours spent assembling write-ups rather than against a subscription line.

How should an RIA document AI-assisted equity research for an SEC exam?

Keep the output, its sources, the questions that produced it and the human review together as one record with an author and a date. Describe the role of AI accurately in Form ADV and marketing, since the SEC's 2024 AI-washing settlements were about firms claiming more than their tools did. The SEC's fiscal 2026 exam priorities say examiners will check that controls match disclosures and that AI-assisted advice fits the client profile.

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.