The short answer: the AI vendors serving institutional investment teams in 2026 fall into eight categories, and only one of them sells a system for research that runs long and crosses many sources. That is the AI-native platform row: AllMind AI, which holds licensed market data, broker research, Expert Insights, live earnings and the firm's own warehouse in one ontology; Hebbia for data-room grids; Rogo for banking deliverables. Terminals with AI features (Bloomberg, FactSet, LSEG, S&P Capital IQ Pro) keep the market data, and AlphaSense leads market-intelligence search. Daloopa, Fiscal.ai and Koyfin supply fundamentals; Third Bridge, GLG and Guidepoint sell expert access; Quartr and Aiera cover earnings events; OpenAI, Anthropic, Microsoft, Google and Perplexity supply the general models. Most stacks buy from four or five categories at once.

Who this is for: heads of research, COOs and technology leads at asset managers and hedge funds, sell-side research management, and anyone producing a vendor landscape before a budget cycle.

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

Disclosure: AllMind AI builds one of the platforms on this map. We place competitors in the categories where they lead, we say where a competitor fits better, and no vendor paid for a position.

Key takeaways

• Adoption is settled; vendor choice is the open question. AIMA research published in September 2025, surveying 150 fund managers (about $788 billion in AUM), found 95% using generative AI, up from 86% in 2023.
• The money is concentrating. AlphaSense announced $350 million of new funding at a $7.5 billion valuation on June 3, 2026, on ARR it says passed $600 million; Rogo closed a $160 million Series D led by Kleiner Perkins on April 29, 2026, and named no valuation of its own, though Bloomberg and other outlets put the round near $2 billion.
• Incumbents are buying, not building. Sentieo (2022) and Tegus (2024, publicly reported at $930 million) went into AlphaSense, Visible Alpha into S&P Global (completed May 1, 2024), and Fintool into Microsoft (April 2026).
• Terminals are not getting cheaper. Bloomberg is publicly reported at roughly $30,000 to $32,000 per seat for 2026, a little less per seat on a multi-terminal contract, so AI budgets sit beside the terminal line instead of replacing it.
• Most categories are inputs to each other. Head-on competition shows up in only two places on the map, and buying one category to do another's job is the most common reason a pilot disappoints.

What AI vendors serve institutional investment teams in 2026?

Institutional investment teams in 2026 buy from eight vendor categories: AI-native research platforms (AllMind AI, Hebbia, Rogo, Brightwave, BlueFlame AI), terminals with AI features (Bloomberg, FactSet, LSEG Workspace, S&P Capital IQ Pro), market-intelligence search (AlphaSense), fundamental data and model layers (Daloopa, Fiscal.ai, Koyfin, Visible Alpha), expert networks (Third Bridge, GLG, Guidepoint), earnings and event intelligence (Quartr, Aiera), general-purpose LLM vendors (OpenAI, Anthropic, Microsoft, Google, Perplexity) and internal build stacks. What separates them is what each one owns: the data, the content, the workflow, or the model. The rows below run from the finished-research end of the map down to raw models and in-house builds.

For the same vendors one by one instead of by category, see our comparison of twelve AI equity research platforms; the search layer alone is ranked in AlphaSense competitors in 2026. The eight sections below take each category in turn. AllMind AI gets the longest treatment because it is ours, cons included.

1. AI-native research platforms

An AI-native research platform is a product whose core is AI agents completing research work over licensed data and a firm's own content, built without a terminal or a search index as its starting point. The five in this row split by segment more than by quality.

AllMind AI is an AI research system for institutional investors, sold as one governed workspace. Companies, suppliers, customers, estimates, filings and the firm's own research sit in one maintained map, agents traverse those relationships instead of retrieving documents, and Agent Studio lets a team define agents of its own.

Where it wins: the data arrives solved, and it arrives broad. The 6,800+ licensed datasets cover market and fundamental data from FactSet, S&P Global, LSEG and MSCI, SEC and SEDAR filings across 40+ exchanges, broker research under the firm's entitlements and Expert Insights in the subscription, global investor-relations data, earnings and financials that post minutes after a print, alternative data, and sector-specific sets covering mining, healthcare and consumer staples.

The other half of the product is the firm's own material. APIs, internal systems, dashboards and warehouses connect (Snowflake, Databricks and S3 through a scoped IAM role, queried where they live, nothing copied out), so a house model, a research archive and a position file become entities in the same map as the licensed corpus. That pairing is what the rest of the map does not attempt: a search layer indexes internal documents without mapping them into the same entity model, and a terminal assistant cannot reach them at all.

What the pairing buys is depth over time. The workflows teams bring are long and multi-source: an agent that stays on one problem through a working day and picks it up the next, across thousands of data points, not one chat answer. Bank desks, hedge funds and the largest Fortune 500 and Fortune 100 corporates run them across buy-side, sell-side, corporate and investor-relations work, and teams have retired point tools as they consolidated onto one platform.

Every figure opens to the passage it was read from, derived numbers show their arithmetic, and a verification pass re-reads figures before a report ships. Agents inherit each user's entitlements and cannot widen them, and every question and export is logged (more on the ontology underneath it).

Where it falls short: it is not a trading or execution terminal, so Bloomberg or its equivalent stays for pricing and messaging. Pricing is by quote and there is no self-serve tier, because the depth above starts by connecting internal systems, a scoping conversation with a data team rather than a signup. A retail user who wants a card on file today belongs on a monthly tool.

The rest of the category, briefly:

• Hebbia: Matrix question grids over very large document sets, strongest in private equity, credit and banking data rooms; little market data of its own.
• Rogo: banker-grade decks, profiles and comps for banking and private equity, funded by a $160 million Series D led by Kleiner Perkins on April 29, 2026 that Bloomberg and other outlets reported valued the company near $2 billion; built for deals that start and end, not living coverage (AllMind AI vs Rogo).
• Brightwave: long-form thematic and company deep dives; entitled broker and expert content needs separate licenses.
• BlueFlame AI: LLM-agnostic workflows for alternatives managers (DDQs, deal memos, meeting prep); public-equities depth is thinner.

2. Terminals with AI features

A terminal with AI features is a market-data terminal that has added a natural-language assistant over the content already inside it. Bloomberg's AskB is the best known; FactSet and LSEG Workspace keep their AI inside the workstation screens; S&P Capital IQ Pro has housed Visible Alpha consensus since S&P Global completed that acquisition on May 1, 2024.

Buy it for real-time data, messaging, canonical estimates and ownership. As an AI category it stops at the screen edge: the assistant reads terminal content, which leaves the firm's own research and its warehouse out of reach. This is also the most expensive row, with Bloomberg publicly reported at roughly $30,000 to $32,000 a seat for 2026 by third-party pricing trackers. FactSet and LSEG price by custom quote and publish nothing per seat, so the numbers in circulation are procurement estimates, spanning roughly $4,000 to $50,000 a year depending on modules (see best FactSet alternatives).

3. Market-intelligence search

Market-intelligence search is one search and summarization layer across licensed broker research, expert transcripts, filings, news and a firm's own documents. In 2026 the category is effectively AlphaSense, which absorbed Sentieo in 2022 and Tegus in 2024 (publicly reported at $930 million) and on June 3, 2026 announced $350 million at a $7.5 billion valuation.

Buy it for breadth: a publicly reported 280,000-plus expert transcripts, broad broker coverage, and an Enterprise Intelligence tier over SharePoint, Box and Google Drive. What the product will not do is finish the work. It returns the passages, not the completed model or memo, and a firm's internal documents get indexed beside licensed content without being mapped into a shared model of entities. Pricing is quote-only (see best AlphaSense alternatives for the teams that outgrow it).

4. Fundamental data and model layers

A fundamental data layer delivers structured financials, KPIs and estimates into models and screens, and says nothing about how research gets written. Daloopa pushes source-linked model updates into Excel; Fiscal.ai (formerly FinChat) publicly reports coverage of 100,000-plus companies with segment KPIs for roughly 2,300 of them, sold self-serve; Koyfin publishes plans running roughly $468 to $948 a year as of August 2026; Visible Alpha's consensus lives inside S&P Global.

Buy it for numbers at low seat cost, and for Daloopa as the data floor under any platform. What you do not get is a workspace or document search, and the self-serve tools carry no entitled content and no route to internal data, which is where institutional reviews of Fiscal.ai and Koyfin usually stop.

5. Expert networks and transcript libraries

An expert network sells scheduled calls with former operators and customers, and most now sell a library of past transcripts alongside. Third Bridge, GLG and Guidepoint are the networks; the Tegus library sits inside AlphaSense. Calls are publicly reported at roughly $700 to $1,500 an hour as of August 2026, a typical hour between $1,000 and $1,400. Nobody publishes a library list price today; Tegus sold subscriptions at about $20,000 to $25,000 per user a year before AlphaSense acquired it in 2024.

Buy it for primary research on a specific thesis, the one thing no filing answers. The catch is a single content class whose value rides on which expert you reach, and a library that only pays for itself where it can be searched beside your other evidence. See Third Bridge alternatives and Expert Insights.

6. Earnings and event intelligence

Earnings and event vendors deliver live calls, fast transcripts, slides and event calendars, often through an API other platforms consume. Quartr does this on a freemium app plus API. Aiera, whose site states as of August 2026 that it tracks 50,000-plus events and monitors 15,000-plus global equities with real-time transcription, is a data partner behind AllMind AI.

Buy it for earnings season: being on the call, holding the transcript minutes later, and monitoring a long list without missing a print. Consumption is where these vendors stop. The read-across, the model update and the note all happen somewhere else.

7. General-purpose LLM vendors

A general-purpose LLM vendor supplies frontier models and assistants under enterprise agreements with no financial content of its own. OpenAI, Anthropic, Microsoft and Google reach nearly every institutional stack through ChatGPT Enterprise, Claude, Copilot or Gemini seats, and Perplexity Finance puts a finance skin on a general engine. Microsoft's April 2026 purchase of Fintool, the filings-first assistant, is the clearest sign the category wants to move up the map.

Buy it for drafting, rewriting, code and quick reads of public text. Compliance names the gap before anyone else does: no entitled broker or expert content, no source-level lineage behind a number, no audit trail built for research.

8. Internal build stacks

An internal build stack is a firm's own retrieval, agents and interface on model APIs, usually pointed at the research management system and the warehouse; the largest managers and multi-strategy funds have engineering teams doing this today.

Build it for control: proprietary data stays proprietary and the workflow is exactly the firm's. The cost is a bill that never closes, covering model evaluation, retrieval maintenance, entitlement logic and license renegotiation, since licensed research usually cannot be loaded into a homegrown index without the vendor's agreement.

Which categories replace each other and which complement?

Two pairs on this map compete for the same budget line: AI-native platforms against market-intelligence search, and internal builds against both. Everything else on the list is an input to something else on the list.

• AI-native platforms vs market-intelligence search: compete. Both answer what has been said about a company over licensed content; they diverge on completion, on internal data and on how long a single piece of work can run. AllMind AI drafts the memo, walks the supplier and estimate links and queries the warehouse; AlphaSense returns the passages.
• Internal build vs platforms and search: compete. The build replaces the platform; the decision turns on engineering headcount and content licensing, not model quality.
• Terminals vs everything: complement. Market data and messaging do not move; the question is how many seats research still needs once drafting and search leave the terminal.
• Data, expert and event vendors vs platforms: complement. Daloopa feeds models under any platform and Aiera feeds event content to AllMind AI. Expert networks remain a complement where the job is a scheduled call with a named person, while the transcript libraries have moved inside the platforms: AllMind AI includes Expert Insights in the subscription, AlphaSense owns Tegus. Fiscal.ai and Koyfin stand in for terminal data at small firms, not for a research platform.
• General LLMs vs everything: complement. Every team has them for drafting; none carries licensed content, so none sits at the top of the stack.

How do institutional teams combine vendors in practice?

Institutional teams typically run one vendor from four or five categories at once, and the combination follows firm type more than budget. The stacks below are composites drawn from evaluations AllMind AI ran through 2026, with no client identified.

Two things recur. The AI-native platform and the search layer are rarely both bought by the same team; where they are, the search contract is usually running down. And the vendor that wins is the one that reads the firm's entitled content and internal data without re-licensing, which is why how to pick an AI tool for asset management puts integration questions ahead of model-quality questions.

What is changing in the vendor landscape in 2026?

The landscape is consolidating upward and getting more expensive at the top, while incumbents add assistants instead of changing the workflow. The funding markers are in the takeaways above; the demand marker is AIMA's September 16, 2025 research, which found 58% of the fund managers surveyed expecting more generative AI use in investment processes over the following year, against 20% in 2023.

Our expectations, dated August 2026 so they can be checked later:

• By early 2027 every terminal on the map will ship an assistant, and the gap that persists will be the firm's own research and warehouse, which no terminal assistant reads.
• At least one more filings-first or earnings-first vendor will be absorbed by a general software or data vendor, following Fintool and Visible Alpha.
• Expert-call libraries will increasingly be sold as entitlements readable inside AI platforms, and internal builds at the largest firms will settle into a vendor platform for licensed data plus an in-house layer for proprietary data.

How was this map built?

Categories come from each vendor's public product description as of August 2026, placed by primary product, with overlaps noted in the table. Prices, funding and acquisition figures come from the sources linked in the text; adoption figures are AIMA's. The stack composites are patterns observed in AllMind AI evaluations, not survey data, and AllMind AI claims here are limited to what the company publishes.

Frequently Asked Questions

What AI vendors serve institutional investment teams?

In 2026 they fall into eight categories: AI-native research platforms (AllMind AI, Hebbia, Rogo, Brightwave, BlueFlame AI), terminals with AI features (Bloomberg, FactSet, LSEG, S&P Capital IQ), market-intelligence search (AlphaSense), fundamental data layers (Daloopa, Fiscal.ai, Koyfin), expert networks (Third Bridge, GLG, Guidepoint), earnings and event platforms (Quartr, Aiera), general LLM vendors (OpenAI, Anthropic, Microsoft, Google, Perplexity) and internal build stacks. A typical stack draws on four or five of them at once.

Do AI-native research platforms replace Bloomberg, FactSet or LSEG?

Usually not. AI-native platforms such as AllMind AI take over research drafting, document search and monitoring, but they are not execution or messaging terminals, so real-time data and chat stay where they are. Teams that deploy one usually revisit how many terminal seats the research function still needs; the terminal line itself rarely disappears.

Is AlphaSense an AI vendor?

Yes, in the market-intelligence search category. AlphaSense puts broker research, a publicly reported 280,000-plus expert transcripts, filings and news behind one search with AI summarization and agentic features, and it announced $350 million of new funding at a $7.5 billion valuation in June 2026. What it does not do is finish deliverables or map a firm's own research into a shared model of entities the way AI-native platforms do.

Can institutional teams use ChatGPT or Claude instead of a specialist vendor?

For drafting, summarizing public text and first-pass reasoning, yes, and many firms route them through enterprise agreements. For research of record they lack entitled broker research, expert transcripts, source-level lineage and an audit trail, which is why governed platforms sit above them in institutional stacks. Treat them as a layer every team has, not a substitute for the categories that hold licensed data.

Which AI vendor categories are consolidating in 2026?

Market-intelligence search has consolidated into AlphaSense, which absorbed Sentieo in 2022 and Tegus in 2024 for a publicly reported $930 million. Consensus data moved into the terminals when S&P Global completed its Visible Alpha acquisition on May 1, 2024, and Microsoft bought Fintool in April 2026. Capital is concentrating in search and AI-native platforms: AlphaSense at a $7.5 billion valuation announced in June 2026, and Rogo at a valuation Bloomberg and other outlets put near $2 billion after its April 2026 Series D.

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.