Best AI Tools for Investment Research in 2026

The best AI tool for investment research in 2026 is Kepler, because it knows your firm and proves every number. Our engineers build your firm’s ontology, every filing and document is read in full before anyone asks, and code computes and cites every number to the exact page, line or cell, so an answer can go straight into a model, an IC memo or an LP report.

This list is for the institutional buy side: analysts, portfolio managers and investment teams at hedge funds, private equity firms, asset managers, allocators and fund-of-funds, where an answer ends up in a model, an investment committee memo or an LP report. Retail screeners and trading bots aren’t on it.

We build Kepler, so it’s listed first and labeled. Facts about other tools come from their own websites, press releases and public reporting as of November 2026, linked inline. Pricing appears only where it is published or publicly reported, with the source named.

How we picked

We asked the same seven questions of every tool:

• Where does each number come from? Pulled from a source and computed in code, or written by a model.
• What does the citation point to? The exact page, line or cell, or a document in general.
• Does it know your firm? Your funds, holdings, managers and deals, and your own definitions of adjusted EBITDA, net IRR or exposure.
• Does it find everything? A complete list across every document, or the top results of a search.
• Does work run on its own? Agents on schedules and events, or a prompt every time (what counts as an AI agent in finance).
• Where does it work? The app, Excel, PowerPoint, Claude, ChatGPT, Slack, an API.
• Will it pass security review? Permissions that mirror your firm’s, a written policy on training with your data, and a current SOC 2 Type II report, which we hold.

For the tests behind the first two questions, see how to check AI-generated numbers in financial research.

Quick comparison

Tool	Best for	Core content	Your firm’s data	Where numbers come from
Kepler	AI built around your firm, every number provable	26M+ SEC filings, 40M+ IR documents and transcripts, market data, news, plus every document your firm connects	Resolved into your firm’s ontology, every document read in full	Pulled from source and computed by code, cited to the line
AlphaSense	Broker research and expert calls	500M+ documents, 300,000 expert interviews	Connectors in Enterprise Intelligence	Standardized financials and Canalyst models; AI answers cite text snippets
Hebbia	One set of questions across many documents	Global filings, transcripts; FactSet, Capital IQ, PitchBook via your licenses	SharePoint, Box, Egnyte, Datasite, CRM	Model answers per cell, with source links
Bloomberg ASKB	Teams that work in the Terminal	Bloomberg data, news, research from 800+ providers	Instant Bloomberg chats, Worksheets, Drive	Bloomberg data with linked sources
Rogo	Bank and deal teams producing files	PitchBook, Preqin, FactSet, Dow Jones, Daloopa	SharePoint, OneDrive, CRM, data rooms	Agent-built files from partner data
Claude for Financial Services	A frontier model on data you license	Connectors to FactSet, Capital IQ, PitchBook, LSEG, Moody’s	Box, Snowflake, Databricks	Model answers linked to sources
ChatGPT for Financial Services	Firms standardizing on OpenAI	SEC filings, Quartr, PitchBook Essentials, LSEG News	50+ connectors, custom MCP apps	Model answers with the passage highlighted
Daloopa	Source-linked historicals for models	Fundamentals and KPIs for 6,000+ companies	Not the focus	Extracted data points, each linked
Bigdata.com	Licensed content inside your own AI tools	News, transcripts, broker research, filings	Uploads	Excerpts with source, date and URL
V7 Go	Extraction from CIMs and data rooms	Your documents	Data rooms, uploads	Extracted fields with visual citations

The 10 best AI tools for investment research

1. Kepler (our pick)

We build Kepler.

Best for: buy-side research teams at hedge funds, private equity firms, asset managers and allocators whose answers end up in a model, an IC memo or an LP report, and who need AI that knows their firm and proves every figure.

Frontier models improve every quarter, and two problems survive every release. A model generates its numbers as text, so it can be wrong in ways nobody sees, and a citation attached afterward can show that a source exists without showing that the figure came from it. The model also knows nothing about your firm: your funds and portfolio companies, your definition of adjusted EBITDA, the way your PM wants an earnings update.

We built Kepler to close both gaps. Our forward-deployed engineers build your firm’s ontology, one living model of the firm in which every company, fund, manager, deal and document resolves to a single record and every house term carries one definition. Each link points to the sentence it came from, and the ontology knows time, so a figure is available as filed, as restated or as of any date. Before anyone asks, we read every page with extraction models we train: 26M+ SEC filings across 20+ form types and 40M+ IR documents and earnings call transcripts, covering 14,000+ companies in 120+ countries and 30+ languages, plus everything your firm connects from SharePoint, Box, Egnyte, Outlook, Slack, your CRM and your data rooms.

Code pulls and computes every number. Statements assemble from the filings’ own XBRL with no model in the path, and every ratio, comp and exposure runs as code over cited inputs with its formula shown. Click a figure and you land on the page, line or cell it came from. Citations are checked against their sources while the answer is written, and when two sources disagree we show both. We route each task to the model best suited to it, small models we train for reading and frontier models for the question and the write-up, so every better model makes Kepler better.

Agents run on schedules and on events, such as a new 10-Q, a guidance change or a capital call notice, acting exactly as the person who set them up and never taking instructions from what they read. Your playbooks run the same way: the IC memo in house style, the earnings update the way your PM wants it and comps on your own adjusted EBITDA are saved once and applied every time, and each correction makes the firm’s models better. Alerts, digests, updated models and drafted memos land in the Kepler app, Excel, PowerPoint, Word, Claude, ChatGPT, Slack or email, or reach your systems through our API, MCP server and Python SDK. Our engineers built this layer before at Palantir, and CEO Vinoo Ganesh spent seven years there before becoming Citadel’s first Head of Business Engineering. Public data is live on day one; in the first week we connect your sources and build your ontology.

Why it’s our pick for investment research

• Your firm’s ontology. Holdings, funds, managers and house definitions in one living model of the firm.
• Complete lists. “Every 10-K that disclosed a material weakness in the last three years” returns all of them.
• Reproducible numbers, as filed or as restated. The same question returns the same figure with the same sources. 194 of 200 companies we studied reshaped a statement in ways XBRL tags can’t follow; Reclassified to Conform shows how we recover it.
• Models we train for the reading. Anthropic’s case study reports that our models scored 94% on mapping financial statement labels to standardized taxonomy codes, against 38 to 46% for other models. Our taxonomy model cost us $9 to train.
• Agents you can leave alone. Overnight runs on every new 10-Q, guidance change or capital call notice, with the exact quote in the alert.
• Kepler everywhere. The app, Excel, PowerPoint, Word, Claude, ChatGPT, Slack, and your own systems through our API.
• Built for your rules. One access check on every read, walls between deals, every run recorded, SOC 2 Type II, our cloud or yours (security).

Questions investment teams ask Kepler

• “Semiconductor companies whose inventory days rose three quarters in a row, each quarter cited to its 10-Q.”
• “Every portfolio company where net debt to adjusted EBITDA, on our definition, went above 5x last quarter.”
• “Restatements and off-balance-sheet commitments in the last five 10-Ks for every company in our coverage.”

“Every other AI tool I’ve tried misses what’s buried in the footnotes. Kepler has read every filing, so nothing slips past it.” (Managing Director, multi-billion dollar fund)

Not the right fit if: you’re a retail investor picking stocks, or your desk mainly wants a terminal for live prices and chat.

Try it at app.kepler.ai, see how the platform works, or book a demo.

2. AlphaSense

Best for: one search across broker research, expert calls, filings and news.

AlphaSense is a research library first: it says it searches more than 500 million premium business documents, and the 2024 Tegus purchase brought in Canalyst models, BamSEC and an expert transcript library that AlphaSense now puts at 300,000 interviews. For an analyst, the draw is one search box across broker notes, expert calls, filings and news, with Generative Search citing the snippet behind each answer. Its agent, SuperAnalyst, opened in early access in June 2026 and launched on October 6, 2026; it plans multi-step work, runs code and keeps memos, models and dashboards refreshed. Excel and PowerPoint assistants arrived in July 2026, and Enterprise Intelligence connectors pull in SharePoint, Box, Google Drive, Egnyte and other internal systems.

AlphaSense doesn’t publish prices. Across the 160 AlphaSense customers it tracks, SpendHound reports average spend of $10,131 a year for smaller customers and $139,455 for enterprise ones.

What users say: AlphaSense is good for finding divergent views but “not useful for generating a synthesis that reads through these reports and summarizes what the sentiment actually is in a reliable way. Still need to manually go through to develop a reliable synthesis.” (a corporate strategist reviewing AlphaSense on TrustRadius, June 2025)

How it compares: AlphaSense’s AI works over the content it licenses and the documents you connect. Kepler builds your firm’s ontology, reads every document at ingest and computes every number in code, cited to the line. Expert-call transcripts and broker research are licensed content outside Kepler, so teams that read them keep a library for that. See AlphaSense alternatives and Kepler vs AlphaSense.

3. Hebbia

Best for: research and deal teams that want a grid of answers across a data room or a peer set’s filings.

Hebbia’s Matrix works like a spreadsheet of answers: each document is a row, each question a column, and a model fills every cell with a link to its source. Matrix 2.0 (August 2026) takes the grid through to a memo, deck or model, pausing at checkpoints for a person to review. Max, which Hebbia calls an AI team member, began reaching a small group of firms in July 2026, and on October 1 Headless Hebbia opened the platform through an API and MCP server. Content comes from filings, transcripts and partner feeds including FactSet, S&P Capital IQ, PitchBook, Preqin, Daloopa, Third Bridge and Guidepoint; firm content arrives from SharePoint, Box, Egnyte, Intralinks, Datasite, Salesforce, DealCloud, Snowflake and Databricks.

Sacra reports pricing of $10,000 per seat a year for Professional and $3,000 to $3,500 for Lite.

What users say: “It takes so long and you can’t even adjust the intelligence of the model even though it’s LLM-agnostic.” (an M&A analyst on Wall Street Oasis, 2026)

How it compares: Hebbia runs a model over the documents you select, question by question. We read every document once at ingest, resolve it into your ontology and compute numbers in code. More in AlphaSense vs Hebbia.

4. Bloomberg Terminal (ASKB)

Best for: teams whose day already runs through the Terminal.

ASKB, the Terminal’s conversational AI, entered beta on February 23, 2026 and became generally available on October 15. It runs agents across Bloomberg data, news, Bloomberg Intelligence and research from more than 800 providers, and writes BQL for Excel and BQuant. Clients can search up to three months of their own Instant Bloomberg chats, query Worksheets, and keep files in Bloomberg Drive, where answers cite the file and passage. Third Bridge expert transcripts have been available in ASKB since April 2026.

A single Terminal was reported in 2024 at $31,980 a year on two-year contracts starting in 2025.

What users say: “Huge learning curve and hard to find what you need without extensive training (which Bloomberg does provide to their credit).” (a corporate development associate on Capterra, January 2025)

How it compares: ASKB is built around Bloomberg’s content and the Terminal’s workflow, and your firm’s own data enters through chats, Worksheets and Drive files. Kepler is built around your firm’s ontology, with every number computed in code and cited to the line.

5. Rogo

Best for: investment banks and deal teams that want an agent to produce finished Excel, PowerPoint and Word files.

Felix, Rogo’s agent, turns a prompt into banker deliverables, and Rogo says it offers hundreds of purpose-built financial agents. It pulls market and deal data from partners including PitchBook, Preqin, FactSet, Dow Jones Newswires and Daloopa, and reads firm content in SharePoint, OneDrive, Salesforce, DealCloud, SS&C Intralinks and Datasite. In 2026 Rogo acquired Rivanna (diligence) and Arvo (a meeting assistant).

What users say: “Rogo demos like a dream and delivers like an intern on day one.” (an M&A associate whose firm uses Rogo, on Wall Street Oasis, March 2026)

How it compares: Rogo is built around producing banker deliverables from licensed data. We built Kepler around a firm’s own data model and provable numbers, for the buy side. See Rogo alternatives.

6. Claude for Financial Services

Best for: firms that want a frontier model working on the data licenses they already hold.

Claude for Financial Services, which Anthropic introduced in July 2025, connects Claude to FactSet, S&P Capital IQ, PitchBook, Morningstar, LSEG, Moody’s, Daloopa, Third Bridge and Guidepoint, plus firm data in Box, Snowflake and Databricks. Its Excel, PowerPoint and Word add-ins became generally available in May 2026. Anthropic says Claude links figures to the underlying data and to referenced cells in Excel. By default it doesn’t train on Team or Enterprise data.

How it compares: a frontier model on its own, with premium data through your own provider licenses. Kepler runs inside Claude through MCP, and Anthropic published a case study on how we built Kepler with Claude.

7. ChatGPT for Financial Services

Best for: banks and equity research teams that already run on ChatGPT Enterprise.

OpenAI launched ChatGPT for Financial Services on September 10, 2026, as a separate plan built on ChatGPT Enterprise for eligible institutions, with an initial focus on investment banking and equity research. Included data covers SEC filings, Quartr transcripts, PitchBook Essentials, Crunchbase, LSEG News and delayed Daloopa data, and firms can add more than 50 connectors and their own MCP apps. OpenAI says S&P Capital IQ, LSEG, MSCI, Factiva and Moody’s are working on sign-in integrations, and multiple workspaces can enforce information barriers.

How it compares: strong for drafting and analysis in a familiar interface. The model still writes the numbers and learns your definitions from what you connect. Kepler runs inside ChatGPT too. See ChatGPT vs Claude for financial analysis.

8. Daloopa

Best for: analysts building models who want every historical data point linked to its source.

Daloopa supplies fundamentals, KPIs and guidance for more than 6,000 public companies with up to 14 years of history, and every figure links to the document it came from. MCP connectors carry that data into Claude, ChatGPT, Microsoft 365 Copilot, Gemini Enterprise, Perplexity, Hebbia and Rogo, and Scout, its Excel agent, builds on the same data.

How it compares: a data layer for another tool to reason over, covering public companies only. We build statements from the filings’ XBRL with every cell cited, then add your firm’s ontology, agents and every surface. See Daloopa alternatives.

9. Bigdata.com

Best for: firms that want licensed content, with citations, inside their own AI agents or assistants.

Bigdata.com is RavenPack’s content service for AI: transcripts, 200+ premium news sources, research from 100+ broker providers, filings on 90,000+ companies and Economist Intelligence Unit analysis, available by API or as a connector in Claude, ChatGPT and Microsoft Copilot. Answers come back as ranked excerpts with source, date and URL, and files you upload are searched alongside the licensed content.

How it compares: a content layer, so modeling and analysis happen in whichever assistant you connect.

10. V7 Go

Best for: private markets teams extracting figures from CIMs and data rooms.

V7 Go is an agent platform for document-heavy work such as diligence, underwriting and portfolio reporting. Knowledge Hubs index a whole data room, and visual citations highlight the spot on the page each extracted figure came from. PitchBook data is available through your own subscription.

How it compares: extraction on the documents you load, with no public-market data of its own. For diligence specifically, see the best AI tools for private equity due diligence.

Also worth a look

• Datasite (Blueflame AI): acquired by Datasite in July 2025 and now Datasite’s AI agent inside the data room. Datasite also partners with Hebbia and Rogo.
• Hudson Labs: a US equity research co-analyst with guidance tracking and a “No Hallucination Guarantee” that refunds paying subscribers $50 per confirmed fabricated number, with exclusions.
• Fiscal.ai (formerly FinChat): an AI stock research platform for public companies that also supplies data extracted from filings to other platforms.
• Perplexity Enterprise: quick cited lookups across EDGAR, Quartr and news.
• FactSet and S&P Capital IQ Pro: both have added AI document search and MCP access for firms that already run on them. S&P Global announced a strategic review of Capital IQ in October 2026; see S&P Capital IQ alternatives.

Brightwave, on many older lists, now describes itself as an agent infrastructure company, so we left it off. See Brightwave alternatives.

How to choose an AI research tool

1. Start with the question you ask most. “What did management say about pricing?” is a search problem that a content library handles well. “Segment EBITDA margin over 12 quarters, on our definition, across the peer set” is a calculation over sourced inputs, and it needs a deterministic engine and your firm’s definitions.
2. Click ten citations. Check the figure, the period, the units and any calculation on the source page.
3. Ask the same question twice. Different numbers mean the model wrote them.
4. Test your firm’s definitions. Ask for adjusted EBITDA or net IRR as your team defines it, and for a portfolio company by its internal code name (how to get AI to use your firm’s definitions).
5. Ask for a complete list. “Every fund agreement with a key-person clause” shows whether a tool read everything or ranked the top hits.
6. Check where it works and what it costs in total. Connector-based tools need your FactSet, Capital IQ or PitchBook licenses. Count Excel, PowerPoint, Claude, ChatGPT and API access.
7. Read the security report. Get each vendor’s SOC 2 Type II report (ours is available through our security page), the training policy in writing, and how permissions carry over.

For the thinking behind these tests, read what enterprise intelligence is and what an ontology means for an investment firm. For specific desks, see our lists for hedge funds and asset managers.

Run your own coverage list through Kepler at app.kepler.ai, or book a demo and we’ll show it on your firm’s documents.

Frequently asked questions

What is the best AI tool for investment research?

Kepler is the best AI tool for investment research when the answer ends up in a model, an IC memo or an LP report. Our engineers build your firm's ontology, every filing and document is read in full before anyone asks, and code computes and cites every number to the exact page, line or cell. Agents watch new filings overnight, and the same work runs in Excel, PowerPoint, Slack and the AI assistants your team already uses.

Can ChatGPT or Claude do investment research on their own?

Both are excellent for reading, summarizing and drafting, and both now have finance editions with data connectors. On their own, the model still writes the numbers and learns your firm's definitions only from what you paste or connect. Kepler runs inside Claude and ChatGPT, so an analyst can stay in either assistant and get figures that are computed in code, cited to the line, and built on the firm's ontology.

What AI tools do hedge funds and private equity firms use for research?

Kepler brings filings, transcripts, market data and the firm's own documents into one ontology, with every number computed and cited, so the models, memos and screens a firm decides on come from one place. Around it, most firms also license a research library such as AlphaSense or Bloomberg for broker research and expert calls, and market data from FactSet, S&P Capital IQ or PitchBook.

How do I check whether an AI research tool's numbers are right?

Run the same test on every tool. Click ten citations and confirm the figure, period and units on the source page. Recompute any derived number. Ask the same question twice and compare. Ask for a metric on your firm's own definition, and for a complete list across a large document set. A tool that computes numbers in code and checks every citation before you see the answer passes all five.

What data does Kepler cover?

Public data arrives already loaded: 26M+ SEC filings across 20+ form types, 40M+ IR documents and earnings call transcripts, market data and news, for 14,000+ companies in 120+ countries and 30+ languages. Your firm's own sources connect read-only and stay in sync: SharePoint, Box, Egnyte, Outlook, Slack, your CRM, data rooms, research notes and models. All of it resolves into your firm's ontology.

Is my firm's data safe in an AI research tool?

Ask every vendor, us included, for a SOC 2 Type II report, a written commitment that your data never trains models other firms use, and proof that permissions carry over from your own systems. We hold SOC 2 Type II and comply with GDPR and CCPA, isolate each firm's data at the identity layer, run one access check on every read, log every query, and deploy in our cloud or yours.