The short answer: an AI investment research platform is a governed system in which AI agents read licensed market data, filings, transcripts and a firm's own files under each user's entitlements, returning cited answers, tables, monitors and drafts the firm can audit. Three parts make it a platform: an entitled data layer, a reasoning layer that runs multi-step work, and a governance layer that records who asked what and where each number came from. A terminal with a chat window has the first and last parts; a chatbot with a pasted PDF has only the middle one. AllMind AI, AlphaSense, Hebbia and Rogo each have all three on different foundations.

Who this is for: heads of research and COOs at hedge funds, asset managers and banks writing a 2026 tooling budget, and analysts asked to evaluate a vendor.

Published August 28, 2026. Last reviewed August 28, 2026. Written by the AllMind AI research team. Reviewed by Anwaar Malik, founder of AllMind AI.

Disclosure: AllMind AI builds one of the platforms defined here. We name the cases where a terminal, a search tool or a general assistant is the better answer, and no placement was paid for. Product facts carry the date we checked them; seat prices are third-party estimates unless a vendor publishes them.

Key takeaways

• The definition has three parts. Entitled data, a reasoning layer that runs multi-step work, and a governance layer with lineage and logs. Missing any one, the product is a terminal, a data feed or a chatbot.
• The category boundary moved in 2026. Bloomberg's ASKB entered beta with roughly a third of 375,000 Terminal users (Wired, April 28, 2026), and Hebbia shipped Matrix 2.0 on August 26, 2026.
• Seven tool types share the label. Terminal assistants, entitled search, document grids, deal-deliverable engines, data layers, general assistants with connectors, and ontology-based research systems.
• An AI financial analyst is a feature. It is the agent that runs one defined task; the platform decides what the agent can see and whether the run is logged.
• Six demo tests settle most evaluations. Entitlements, lineage, internal data, run length, audit log and export, each under ten minutes, each with a pass condition below.

What is an AI investment research platform?

AllMind AI defines an AI investment research platform as a governed system where AI agents work across a firm's licensed external data and its internal files under each user's entitlements, with every answer carrying the passage or calculation it came from. A tool answers one question type; a platform holds the data, the agents and the record of what they did in one place, so the second question can build on the first and a compliance officer can reconstruct both.

The three layers, in the order a buyer should check them:

1. Data layer. What the system is entitled to read: market and fundamentals data, filings, transcripts, broker notes, expert-call transcripts, and the firm's own notes, models and warehouse tables.
2. Reasoning layer. What the agents can do with it: single answers, grids across a coverage list, scheduled monitors, and drafts of memos, models and decks.
3. Governance layer. What is recorded: each user's entitlement, the source behind each figure, and a log of every query and export that the firm and an examiner can read later.

Inside AllMind AI the data layer is a financial ontology, a maintained map in which companies, suppliers, estimates, filings, transcript passages and the firm's own research are objects with relationships; the ontology page describes it as encoding the entities, relationships, evidence and entitlements of the market. A new 10-Q attaches to the company it covers and to the margin thesis an analyst already holds on it, so an agent traverses from filing to thesis without a search step. The mechanism is explained in what a financial ontology is.

How is an AI investment research platform different from a Bloomberg terminal or AlphaSense?

A Bloomberg terminal keeps its assistant inside Bloomberg's data and screens; AlphaSense runs generative search over a licensed document library plus what a firm uploads; a research platform in the sense above adds the firm's own systems and runs multi-step work across all of it under entitlements. Those are three architectures, and each is the right answer for a different desk.

The distinction is easiest to see by asking what each system can read. Bloomberg's ASKB reads Terminal content, including Third Bridge expert transcripts since April 16, 2026 (company-stated). AlphaSense's enterprise page names SharePoint, Box, Google Drive and Egnyte connectors plus uploads and email forwarding, with no Snowflake or Databricks connector named (checked August 28, 2026). Hebbia made Snowflake available on July 8, 2026. AllMind AI queries Snowflake, Databricks and S3 in place through scoped access, so positions, models and notes sit in the same graph as the filings.

The seven tool types that get called an AI research platform:

How we evaluated: every dated fact above was checked against the vendor's own page or a named journalist source between August 24 and August 28, 2026. Seat prices appear only where a vendor publishes them or a named third party has estimated them.

AllMind AI

AllMind AI is an ontology-based research system for institutional investors, launched publicly on July 13, 2025, and bought by banks, hedge funds and large corporates for multi-source work that runs for minutes or hours over many data points.

Where it wins: the breadth, in one sentence: S&P, FactSet, LSEG and MSCI data, broker research, Expert Insights, investor-relations material, live earnings and alternative data. Each class resolves onto the same company objects as the firm's own notes, models and warehouse tables, which is what lets an agent lay the firm's Services-margin assumption beside the number Apple just printed. Agent Studio schedules recurring runs and monitors, so a morning brief or a thesis check arrives without anyone opening the platform. Entitlements are enforced in the graph: an agent inherits the role of whoever ran it, cannot widen it, and every access is logged.

Where it falls short: there is no self-serve checkout and no monthly plan, so a retail or non-institutional user is better served by a self-serve tool. Onboarding real depth means connecting the firm's warehouse and mapping its entitlements with its data and compliance owners, a project measured in weeks. Live embargoed broker research needs the firm's own RMS entitlement; without it, aftermarket research arrives on a delay that varies by broker. SEDAR and EDGAR are covered, TSXV is partial and CSE is not (stated on the asset management solutions page). The fuller description is in what AllMind AI is.

Bloomberg Terminal and ASKB

The Bloomberg Terminal is a market-data, news and messaging terminal, and ASKB is the conversational assistant Bloomberg has been adding to it through 2026.

Where it wins: live pricing across asset classes, the chat network, execution and Bloomberg Intelligence research in one seat. ASKB reads that content in place, and on August 18, 2026 Bloomberg extended it to the mobile app for enabled Bloomberg Anywhere subscribers, with conversations continuing across desktop and phone. AI earnings-call summaries have run on the Terminal since January 22, 2024. For a trader or a macro PM, the Terminal is the research platform.

Where it falls short: the assistant stays inside the Terminal. It does not read the firm's Snowflake tables, its research management system or its own models, and it cannot run a scheduled multi-source job over a coverage list. As of April 28, 2026 ASKB was a beta open to roughly a third of the Terminal's 375,000 users, per Wired as quoted by Slashdot, with no full release date given. Bloomberg publishes no pricing; the seat is publicly reported at roughly $30,000 to $32,000 per year. The direct comparison is on AllMind AI vs Bloomberg ASKB.

AlphaSense

AlphaSense is a market-intelligence search platform that runs generative search and summaries over a licensed document library and a firm's uploaded content.

Where it wins: the library. AlphaSense states 500 million+ premium documents on its homepage (August 28, 2026) and 300,000+ expert-call transcripts on its Expert Insights page, the result of buying Tegus for a company-stated $930 million in 2024. Generative Grid runs one question set across up to 400 documents and 12 prompts per grid, per its help center (updated August 21, 2026). Work Products, launched July 14, 2026, produce PowerPoint and Excel deliverables from search results.

Where it falls short: internal content is indexed as documents. Its help-center Integration Center adds OneDrive, Dropbox and Amazon S3 to the four enterprise connectors above, and none of them is a warehouse, so positions and model outputs live outside the index unless exported as files. Pricing is quote-only; Vendr's third-party estimate is a median contract of $17,500 per year across 38 deals (February 2026).

Hebbia

Hebbia is a document-analysis platform whose Matrix product runs prompts as columns across documents as rows, with agents layered on top through 2026.

Where it wins: private-markets, credit and banking work over documents the firm loads. Hebbia says over 40% of the largest asset managers by AUM use it (first stated October 21, 2025). Preqin data became available inside Hebbia on December 16, 2025, Snowflake on July 8, 2026, and Max, an agent that returns finished slides, reports and models, on July 30, 2026 to a small set of firms first. Matrix 2.0, announced August 26, 2026, extends Matrix to multi-source workflows with human-approval checkpoints before each step.

Where it falls short: Hebbia brings little market data of its own, so the research universe is whatever the firm loads or connects; a public-equity desk that wants consensus, transcripts and broker notes for 300 names still has to source them. Pricing is unpublished; Metronome's third-party estimate is about $10,000 per seat per year for Professional (January 2026).

Rogo

Rogo is an AI analyst for investment banking and private-equity deliverables: pitch materials, comps, profiles and, since August 2026, deal execution.

Where it wins: the deliverable. Rogo's product page lists LSEG, Dow Jones, FactSet, Capital IQ, PitchBook and Preqin as data providers (August 28, 2026), and rogo.com names Truist Securities, Nomura and Baird as customers with 50,000+ users at 350+ institutions. The 2026 cadence is heavy: Rogo Intelligence on July 22, Claude Opus 5 inside the platform on July 24, Deal Room on August 6, and the Rivanna diligence acquisition on August 11. For a coverage group that measures output in decks and CIMs, this is the shape of tool to test.

Where it falls short: the product is shaped around transactions, so living coverage of a public-equity book, earnings-season monitors and thesis tracking sit outside its center. Pricing is unpublished and Sacra's roughly $3,300 per seat figure is a directional third-party estimate.

Daloopa

Daloopa is a fundamental-data layer that delivers source-linked historical financials and KPIs into Excel models and, since 2025, into AI assistants over MCP.

Where it wins: the numbers and their links. Daloopa states 6,000+ public companies with 14 years of history on daloopa.com (August 25, 2026), each figure linked to the filing page it came from, with a Free plan capped at three data sheets. Its MCP connectors reached ChatGPT in December 2025, Perplexity in April 2026 and Microsoft 365 Copilot on June 25, 2026, so an analyst can pull a clean segment history into a general assistant.

Where it falls short: it is a data layer, and an honest one about it. There is no workspace, no transcripts, no broker research and no place to run a multi-step job, so Daloopa is usually a complement to a platform on this page. Its company-stated saving of an average of 2 hours per ticker when updating models in earnings season carries no methodology, so test it on your own models. Paid tiers are quote-only.

ChatGPT and Claude for Financial Services

General assistants with financial connectors are the fastest-moving entrant in the category, and for some desks they are enough.

Where it wins: model quality, price and reach. Claude for Financial Services launched July 15, 2025 with connectors including Box, Daloopa, Databricks, Morningstar, PitchBook and Snowflake, and on May 5, 2026 shipped ten finance agent templates (earnings reviewer, model builder, valuation reviewer among them), with Claude for Excel added on October 27, 2025. OpenAI's Deep Research (February 2, 2025) reaches S&P Global, Moody's and PitchBook through its financial connectors. Perplexity's Enterprise Pro is reported at $40 per seat per month.

Where it falls short: entitlements, lineage and records. A connector exposes a dataset to everyone in the workspace who holds it, which differs from the per-user broker-research entitlements a compliance officer has to enforce. In a July 16, 2026 survey of 35 large asset managers by Substantive Research and Aiera, 69% named broker and data licensing the top barrier to AI adoption. The full comparison is in ChatGPT, Claude and Perplexity vs institutional research platforms.

What is an AI financial analyst?

An AI financial analyst is a software agent that runs a defined analytical task from input to cited output: reading a new 10-Q, pulling the segment margins, comparing them with consensus and the firm's own model, and drafting the variance paragraph. It is a feature of a platform; the platform sets what the agent can read, how long it may run and whether the run is logged.

Three things separate a real one from a summarizer:

• Task length. A summarizer returns in seconds from one document. An analyst agent on AllMind AI or Hebbia Max runs a sequence (retrieve, calculate, compare, draft, check) over minutes or hours and across many sources.
• Source-of-record. Each number in the output links to the passage or the calculation. Without that, the output is a draft someone has to re-derive.
• Scope control. The agent runs as the user who launched it and cannot widen its own access. Home-built stacks that run agents under a service account fail this test.

Vendors apply the phrase to all three tiers, so ask which tasks the agent is certified for, how long a run may take, and where the firm can read what it did. How a run is checked before it ships is covered in what an AI research agent is.

AI research platform vs terminal: six tests you can run in a demo

A terminal passes the entitlement and audit tests and fails the internal-data and run-length tests; a general assistant passes run length and fails the other five; a research platform as defined above should pass all six. Run them in this order, because a fail on the first two ends most evaluations early.

Bloomberg ASKB clears tests 1 and 5 inside its own walls and does not attempt 3. AlphaSense clears 1, 2 and 5 over its library and uploaded files, its Work Products cover the Excel and PowerPoint half of 6, and it fails 3 for warehouse tables. Hebbia clears 3 for Snowflake tables since July 8, 2026, and Matrix 2.0 (announced August 26, 2026) is aimed at 4. AllMind AI is built to clear all six, with the caveat that test 3 assumes the warehouse connection is already done, the part of onboarding that takes weeks. The seat-by-seat trade-offs are in Bloomberg Terminal alternatives for research teams.

Worked example: one Apple question through three architectures

The question asked: "How did Apple's Services gross margin move from fiscal 2025 into the June 2026 quarter, and what did management attribute the change to?"

The public record answers it. Apple's Form 10-K for fiscal 2025, filed October 31, 2025 for the year ended September 27, 2025, reports Services net sales of $109,158 million on total net sales of $416,161 million. Services gross margin was $82,314 million, or 75.4%, up from 73.9% in fiscal 2024. Management attributed the Services sales increase "primarily to higher net sales from advertising, the App Store and cloud services" and the margin gain "primarily to a different mix of services, partially offset by higher costs" (Apple 10-K, FY2025).

The Form 10-Q for the quarter ended June 27, 2026, filed July 31, 2026, reports Services net sales of $30,739 million, up from $27,423 million a year earlier, on total net sales of $109,417 million. Services gross margin was $23,245 million at 75.6%, flat against the June 2025 quarter, while the nine-month figure rose to 76.3% from 75.5%. Management again cited advertising and cloud services for the growth and a different mix of services, partially offset by higher costs, for the nine-month margin gain (Apple 10-Q, Q3 FY2026).

What each architecture is built to return for that question:

• Terminal with an assistant. The FA screens hold every figure above, and the assistant can summarize the call remarks on Services. It cannot set the 75.6% beside the Services margin the firm's own Apple model carries for the September 2026 quarter, because that model lives outside the Terminal.
• Entitled search. A generative search returns the two MD&A passages with citations, plus the broker notes on Services mix if the firm's entitlement covers them. The figures arrive as passages; the analyst still keys them into the model.
• Ontology-based system. Services gross margin is an object on the Apple company node, connected to the 10-K and 10-Q passages, the consensus estimate for the line, and the firm's own model in its warehouse. An agent can return the printed 75.6%, the firm's assumption, the gap and the management sentence in one cited table. A hedge fund runs that configuration across a coverage list on an earnings-day schedule, which is why internal data is half of the definition above.

Everything in the two filing paragraphs is Apple's disclosure and no vendor's output; the bullets describe what each design can and cannot join.

What an AI investment research platform is not, and when to stay with something else

Four products are sold under the label and fail the definition, and each has a desk for which it is still the right buy.

• A terminal with a chat window. Stay with the Terminal if the desk trades, needs live cross-asset pricing or lives in Bloomberg chat; ASKB will keep improving inside those walls.
• A general assistant with connectors. Stay with ChatGPT Enterprise or Claude if the firm holds no licensed broker research and drafts from public filings; a $20 to $40 monthly seat covers that desk.
• A data feed with an API. Daloopa or an S&P data portal is the right buy when the deliverable is an Excel model and the firm already owns the workspace to run it in.
• A self-serve fundamentals terminal. Koyfin at $39 to $299 per month (published August 2026) or Fiscal.ai fits an individual investor, a small RIA without entitled content, or a student.

The case for a platform starts when the same question has to be answered across a coverage list, joined to the firm's own numbers, and reconstructed by compliance a year later. That is the case AllMind AI is built for, and a two-analyst fund with a warehouse can have it as readily as a bank.

Frequently Asked Questions

What is an AI investment research platform?

An AI investment research platform is a governed system where AI agents read licensed market data, filings, transcripts and the firm's own files under each user's entitlements, and return cited answers, tables, monitors and drafts. AllMind AI is one example built around a financial ontology; AlphaSense, Hebbia and Rogo are built around search, document grids and deal deliverables. A terminal with a chat window and a chatbot with pasted PDFs fall outside the label, because neither carries entitlements, lineage and an audit log together.

What is an AI financial analyst?

An AI financial analyst is a software agent that runs a defined analytical task end to end: reading a 10-Q, pulling three years of segment margins, comparing them with consensus and drafting the variance paragraph with citations. It is a feature of a platform, and the platform decides what the agent can read and whether its work is logged. Vendors apply the phrase to everything from a transcript summarizer to a multi-hour agent, so ask for the task list, the run length and the source-of-record first.

Is ChatGPT an AI investment research platform?

Not on its own. ChatGPT Enterprise with the financial connectors OpenAI lists (S&P Global, Moody's and PitchBook among them) can reach some licensed data. It has no per-user entitlement model for broker research, no source-of-record for last quarter's answers, and no archive built for an examiner. It belongs beside a research platform for drafting and code, and it is enough for a macro desk that holds no licensed research.

Does an AI investment research platform replace a Bloomberg terminal?

For research work it can; for execution, chat and live pricing it does not. Bloomberg's own ASKB assistant, in beta with roughly a third of Terminal users as of April 2026 per Wired, keeps AI inside the Terminal and its data. A research platform sits on top of the terminals and data vendors a firm already licenses and adds the firm's internal files. The usual outcome at a hedge fund is fewer terminal seats for pure research analysts, and rarely zero.

How long does it take to set up an AI investment research platform?

Days for the external corpus, weeks for the internal one. On AllMind AI the licensed data, filings, transcripts and Expert Insights are available from the first day of a contract. Connecting a warehouse such as Snowflake or Databricks through scoped access and mapping the firm's entitlements is a project with its data and compliance owners, and there is no self-serve checkout. Hebbia and AlphaSense carry similar internal-content projects; a self-serve tool such as Koyfin is live in an hour because it holds no entitled content.

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.