AI Tools for Equity Research: Complete Platform Comparison
This guide compares the AI tools for equity research an analyst can actually buy in 2026: what each platform is built for, what it costs, and where it falls short. We last rewrote it in June 2026 to reflect a year of rapid change, including FinChat's rebrand to Fiscal.ai, Rogo's $160M Series D, and Daloopa's move into AI data infrastructure.
One disclosure up front: we build Marvin Labs, one of the platforms in this comparison. We have kept the assessments factual and we name the situations where a competitor is the better choice. If you only take one thing from this page, take the table below.
Quick picks:
- Best for equity analysts covering public companies: Marvin Labs ($89/month, free evaluation)
- Best for broker research and expert calls: AlphaSense (enterprise)
- Best for deal teams in banking and PE: Rogo or Hebbia (enterprise)
- Best for financial model automation: Daloopa (free tier available)
- Best for live earnings calls and transcripts: Quartr (per seat)
- Best on a personal budget: Fiscal.ai (free tier available)
How we evaluated: hands-on product use where evaluation tiers exist, vendor documentation and published research for the rest, with every funding, pricing, and customer figure verified against a dated public source in June 2026. Platforms we could not verify to that standard were left out.
AI Tools for Equity Research at a Glance
| Tool | Built for | Core strength | Source-linked answers | Pricing |
|---|---|---|---|---|
| Marvin Labs | Buy-side and sell-side equity analysts | Research automation on filings, press releases, and earnings calls | Yes, every insight links to the source passage | Free evaluation, $89/month Standard |
| AlphaSense | Enterprise research teams | Search across broker research, expert calls, and filings | Yes, snippet-level citations | Custom enterprise, per seat |
| Hebbia | Private equity, credit, banking | Matrix grid analysis across large document sets | Yes, cell-level citations | Custom enterprise |
| Rogo | Investment banks, PE, asset managers | Agentic workflows for deal work (CIMs, comps, memos) | Yes | Custom enterprise |
| Daloopa | Modeling-focused analysts | Audited fundamental data into Excel models | Yes, every datapoint links to the filing | Free tier, custom paid plans |
| Quartr Pro | Analysts focused on earnings events | Live calls, transcripts, and slides across 65+ markets | Document-level | Per seat, mid three figures/month |
| Fiscal.ai | Fundamental investors, retail to institutional | Financial data terminal with AI copilot | Partial | Free tier, paid from low two figures/month |
| Aiera | Event-driven research teams | Live transcription and event monitoring | Document-level | Custom enterprise |
| Fintool | Hedge funds, institutional research | Chat over SEC filings and transcripts | Yes | Custom, per seat |
| Bloomberg / FactSet / LSEG | Market data, terminal workflows | Breadth and real-time data, AI added on top | Varies by module | Roughly $12K-30K/seat/year |
| ChatGPT / Claude | General productivity | Reasoning and writing, no research infrastructure | No | $20-200/month |
The rest of this guide explains each row, then compares the tools head to head for the decisions analysts actually face.
How to Choose an AI Tool for Equity Research
Three questions sort the field faster than any feature list:
Who is the tool built for? Rogo and Hebbia grew up serving bankers and private equity. Their workflows center on deals: a CIM to digest, a dataroom to mine, comps to assemble. Marvin Labs, AlphaSense, Quartr, Fintool, and Fiscal.ai are built around ongoing public-company coverage, where an analyst follows 40-60 names through every filing, press release, and earnings call.
Numbers or narrative? Daloopa extracts clean fundamental data for models. It will not tell you why management's tone on pricing changed between Q3 and Q4. Research platforms do the opposite: they synthesize the narrative but do not maintain your model. Most teams that automate seriously end up with one of each.
Self-serve or enterprise sale? Marvin Labs, Quartr, Fiscal.ai, and Daloopa's free tier can be evaluated today without talking to sales. AlphaSense, Hebbia, Rogo, Aiera, and Fintool are bought through enterprise sales cycles with negotiated contracts. That distinction matters more than any single feature if you are an individual analyst or a small team.
The Best AI Tools for Equity Research, Reviewed
AlphaSense
AlphaSense is the largest player in AI-assisted market intelligence. Its core is search: broker research, expert call transcripts (it acquired Tegus in 2024, adding 250,000+ proprietary expert interviews), filings, news, and internal documents in one index. In 2025 it added Deep Research, an agent that assembles primers and competitive landscapes from that corpus, and in March 2026 it introduced custom, scheduled AI agents that deliver recurring research tasks without manual prompting. It has also begun running AI-led expert calls, where an autonomous agent interviews channel-check respondents.
Where it wins: no other platform matches the breadth of premium content, especially sell-side research and expert transcripts. For idea generation across unfamiliar sectors it is the strongest tool on this list.
Where it falls short: it is an enterprise product with unpublished per-seat pricing and a procurement cycle to match. The workflow is search-first rather than coverage-first. Tracking one company's guidance discipline quarter after quarter is possible but not what the interface is optimized for.
Hebbia
Hebbia's Matrix product runs structured queries across thousands of documents at once: each row a document, each column a question, each cell a sourced answer. It raised $130M at a $700M valuation in 2024 and has since added integrations with PitchBook, FactSet, BlackRock Aladdin, and Fitch credit research. Its customer base concentrates in private equity, private credit, and advisory firms such as Centerview Partners.
Where it wins: bulk document interrogation. Screening 200 datarooms or extracting a covenant term from every credit agreement in a portfolio is exactly what Matrix was built for.
Where it falls short: public equity coverage is not the design center. There is no continuous monitoring of new filings for a coverage list, no sentiment trail across quarters, and the contract size assumes an institution, not an analyst.
Rogo
Rogo is the fastest-growing platform in the deal-side category. Its agent, Felix, drafts CIMs, builds comparable transaction sets, and assembles diligence memos. The company raised a $160M Series D in April 2026 led by Kleiner Perkins and reports 35,000 professionals at more than 250 institutions, including Rothschild & Co, Jefferies, Lazard, and Moelis.
Where it wins: investment banking and private equity execution work. If the job is producing deal materials faster, Rogo is the category leader.
Where it falls short: the same place as Hebbia for an equity analyst. Continuous public-company coverage, guidance tracking, and earnings-season monitoring are not deal workflows, and Rogo is priced and sold for institutions running deal teams.
Daloopa
Daloopa solves a narrower problem completely: getting accurate historical fundamentals into your model. It maintains audited datasets on 5,500+ public companies, every datapoint linked back to its source filing, delivered through an Excel plugin, an API, and since 2026 through MCP connectors into ChatGPT, Claude, Perplexity, and Rogo. It raised a $47M Series C in May 2026 and publishes a benchmark showing agent accuracy improves sharply when grounded in its structured data rather than web retrieval.
Where it wins: model updates during earnings season. Daloopa cites an average saving of two hours per ticker per quarter, and the source-linking on every number satisfies auditability requirements that generic extraction cannot.
Where it falls short: it is deliberately not a research platform. No narrative synthesis, no tone tracking, no document Q&A. Daloopa plus a research platform is a stack, not a redundancy.
Quartr
Quartr comes at research from the events side: live earnings calls, transcripts, slide decks, and investor materials across 65+ markets, used by more than 700 financial institutions. Quartr Pro is the analyst-facing product. The Quartr API supplies the same data as infrastructure, and several AI research products (including Fintool) build on it.
Where it wins: speed and coverage on earnings events, especially outside the US, where transcript availability from other vendors thins out. The mobile experience for listening to calls is the best in the category.
Where it falls short: Quartr is primarily a data and access layer. Its AI summaries are improving, but deep document interrogation, cross-quarter synthesis, and guidance reconciliation sit outside its scope.
Fiscal.ai
FinChat rebranded to Fiscal.ai in 2025 alongside a $10M Series A led by Social Leverage. The product pairs a clean fundamentals terminal (segment-level KPIs are a particular strength) with an AI copilot, and the company increasingly positions its data API as infrastructure for other fintech products. It reports over 350,000 registered users spanning retail investors and institutions.
Where it wins: price-to-capability for fundamental data. For an individual investor or a lean team that wants charts, segment KPIs, and a competent AI copilot at self-serve pricing, it is hard to beat.
Where it falls short: institutional research workflow depth. Document-level analysis, guidance tracking, and the audit-trail rigor institutional compliance expects are thinner than on analyst-focused platforms.
Aiera
Aiera focuses on live events: real-time transcription of earnings calls, conferences, and investor days, with monitoring and alerting layered on top. It sells to event-driven and broad-coverage institutional teams at custom enterprise pricing.
Where it wins: being in the call as it happens, across a very wide event calendar.
Where it falls short: post-event depth. Aiera tells you what was said the moment it is said. Understanding what it means against three years of prior commentary is work it leaves to you or to another platform.
Fintool
Fintool, a Y Combinator-backed entrant, applies LLM search and chat to SEC filings and earnings transcripts (ingested via the Quartr API) for hedge funds, consultancies, and banks. Think of it as a focused, source-linked chat layer over US public-company documents.
Where it wins: fast, cited answers from filings with minimal setup, sold to institutions that want exactly that.
Where it falls short: the surrounding research workflow. Coverage monitoring, sentiment history, and structured outputs beyond chat are limited relative to broader platforms.
Marvin Labs
This is our product, so judge this section with that in mind. Marvin Labs is built for one user: the institutional equity analyst covering 40-60 companies. It ingests filings, press releases, and earnings calls within seconds of publication and turns them into Material Summaries that surface only new and material information, AI Analyst Chat with answers grounded in the underlying documents, Guidance Tracking that reconciles what management promised against what it delivered, and a 0-100 sentiment score updated daily and comparable across peers. Deep Research Agents run longer tasks in the background: earnings reviews, company primers, cross-company comparisons. Every output links back to its source passage. Analysts using it save up to 40% of routine research time.
Where it wins: continuous coverage workflow at self-serve pricing. The free Evaluation plan covers 15 companies with no registration, and the Standard plan is $89/month for 300+ global companies. None of the enterprise platforms above let an analyst verify claims against real workflows before a contract.
Where it falls short: we do not provide real-time market data, consensus estimates, or expert-call networks, so we complement a terminal rather than replace it. Coverage is 300+ companies on Standard rather than the full listed universe (we add companies on request). Teams wanting white-glove enterprise deployment with custom integrations will find AlphaSense's model a better fit today.
Head-to-Head: Rogo vs Hebbia, AlphaSense vs Marvin Labs, and More
Rogo vs Hebbia
The two are compared constantly because both sell enterprise AI to financial institutions, but they solve different problems. Rogo automates the production of deal work: its Felix agent drafts CIMs, comps, and memos inside banking and PE workflows, and its customer list reads like a league table of advisory firms. Hebbia automates the interrogation of documents: Matrix answers structured questions across thousands of files at once, which is why its strongest adoption is in private credit and PE diligence. A bank automating pitch production should look at Rogo first. A credit fund extracting terms from 500 agreements should look at Hebbia first. An equity analyst maintaining coverage of public companies is, frankly, not the core user either was built for.
AlphaSense vs Marvin Labs
AlphaSense is a content platform with AI on top: its moat is licensed broker research and 250,000+ expert transcripts that exist nowhere else. Marvin Labs is a workflow platform on primary sources: filings, press releases, and earnings calls, processed into summaries, guidance reconciliation, and sentiment history for the companies you cover. The honest division: if your research depends on sell-side reports and expert networks, you need AlphaSense and should budget for an enterprise contract. If your work runs on primary financial content and you want automation you can evaluate today at $89/month, that is the gap Marvin Labs exists to fill. Larger teams sometimes run both: AlphaSense for discovery, Marvin Labs for coverage.
Daloopa vs Marvin Labs
This one is not a versus. Daloopa feeds your model precise, source-linked numbers. Marvin Labs tells you what changed in the story: strategy shifts, guidance revisions, tone moves. An analyst using Daloopa to update the model and Marvin Labs to write the earnings review is using each tool exactly as designed, and the combined cost still sits far below a single legacy terminal seat.
ChatGPT or Claude vs Purpose-Built Platforms
General assistants are genuinely useful and genuinely insufficient. The gaps that matter for institutional research: no persistent document library (every quarter starts from scratch), no monitoring (nothing tells you a 10-Q dropped), context limits that force chunking a 200-page filing, and citations that point at best to a document rather than a passage, which fails compliance review. They remain excellent for drafting, brainstorming, and general questions, and at $20/month they belong in the stack. They do not replace the research layer. Our deeper analysis of this gap: the prompt problem with generic AI tools.
Adjacent Categories, Briefly
Legacy terminals (Bloomberg, FactSet, LSEG Workspace). Still the system of record for market data, estimates, and news, at roughly $12K-30K per seat per year. All three have added AI assistants, but the workflow remains terminal-centric. The practical pattern in 2026 is unchanged: keep the terminal for data, add an AI platform for documents.
Financial data APIs (Polygon.io, Financial Modeling Prep, OpenBB). Raw market and fundamentals data from free to a few hundred dollars per month. The right choice when you have engineers building proprietary tools, and the wrong one when you need analyst-ready workflows this quarter.
Web scraping tools (Firecrawl, Bright Data). Useful for channel checks and niche data projects. They extract text without financial context or compliance features, so they supplement research stacks rather than anchor them.
Building the Equity Research AI Stack
For most institutional equity analysts in 2026, the stack that covers the workflow without redundant spend:
- Market data foundation: one terminal seat (Bloomberg or FactSet) where compliance or workflow requires it
- Research automation: Marvin Labs for filings, press releases, and earnings calls across your coverage
- Model automation (optional): Daloopa if model maintenance consumes serious hours
- General assistant (optional): ChatGPT or Claude for drafting, where compliance permits
Teams whose research depends on expert networks add AlphaSense. Deal teams add Rogo or Hebbia. Event-heavy strategies add Quartr or Aiera.
For how these tools fit into day-to-day workflows, see our guides to automated equity research workflows and rolling out AI across a research team, or the full equity research automation guide.
