Fiscal.ai Alternatives for Institutional Investors

For institutional teams that want AI built on their own data, with numbers they can trace to the filing, the best Fiscal.ai alternative is Kepler. Our engineers build your firm’s ontology from your own models, notes and documents, extraction models we train read every filing and transcript in full, and code computes every figure and cites it to the exact page, line or cell, as filed or as restated.

Fiscal.ai, formerly FinChat, built a strong product for individual and professional investors and a data API that other platforms build on. Institutional teams tend to outgrow it on three things: their own data, their own definitions and numbers they can defend in front of a PM or a committee. The alternatives below are compared on exactly those.

We build Kepler, so it’s first and labeled. Facts about other vendors come from their websites, documentation and public reporting as of November 2026, linked inline, with prices only where a vendor publishes them.

Why institutional teams look past Fiscal.ai

• KPI depth. Fiscal.ai’s API documentation lists segments and KPIs for the largest 2,300 companies by market capitalization. Small and mid-cap coverage lists run past that quickly.
• The firm’s own data. Internal models, research notes, CRM history and portfolio data hold the firm’s view, and a public-data terminal doesn’t see them.
• The firm’s own definitions. A standardized line item is someone else’s definition. Desks adjust EBITDA, free cash flow and segment mappings their own way.
• Traceable numbers. Source images are an add-on to the financials API. Teams that put numbers in front of a committee want every figure, including derived ones, to open its source and formula by default.

Fiscal.ai alternatives at a glance

Tool	Best for	Coverage	Your own data	How numbers are sourced
Kepler	Institutional research on your firm’s data and definitions	14,000+ companies, 120+ countries	Your firm’s ontology, every document read in full	Computed by code, cited to page, line or cell
Daloopa	Source-linked historicals for Excel models	6,000+ companies, up to 14 years	Your own Excel models	Extracted, each point linked to its document
AlphaSense	Broker research, expert calls and a large library	500M+ documents	Connectors to file stores	Standardized financials and Canalyst models; AI answers cite source passages
Koyfin	Fundamentals and charting at a published price	80,000+ companies	Portfolios and custom formulas	Standardized datasets
Hudson Labs	Guidance and forensic flags on US equities	Every US public filer	No	Links to filings and calls
Marvin Labs	AI research on a focused coverage list	300+ global companies on Standard	Uploaded notes and broker research	Numbered citations to documents
Bigdata.com	Licensed content inside your AI assistant	Filings for 90,000+ companies	Uploads	Excerpts with source, date and URL
Claude or ChatGPT for Financial Services	A general assistant on data you license	The providers you connect	Connectors to file stores	Model answers linked to sources

The best Fiscal.ai alternatives

1. Kepler (our pick)

We build Kepler.

Best for: hedge funds, equity research teams and asset managers that need answers on their own coverage and definitions, with every number provable.

A data terminal is built for one investor and one screen. An investment firm needs something different: the answer has to reflect what the firm already knows and how it defines things, it has to hold up when a PM clicks into it, and the work has to keep running when the analyst is in a meeting. That’s what we built Kepler for.

Our engineers built this layer before at Palantir. On site with your team, they build your firm’s ontology: each company, segment, peer set, holding and internal model resolves to one record, and terms like adjusted EBITDA, net revenue retention or organic growth carry one definition, yours. Public data is loaded before your first login: 26M+ SEC filings across 20+ form types with full text and XBRL, 40M+ IR documents and earnings call transcripts, market data and news, covering 14,000+ companies in 120+ countries and 30+ languages. Your SharePoint, Box, Egnyte, email, CRM and research notes connect read-only, and extraction models we train read every page of every document at ingest, footnotes, exhibits and scanned decks included.

No number in Kepler is written by a model. Statements assemble in code from each filer’s XBRL, as originally filed or as restated; KPIs from MD&A tables and earnings decks are stored with their scale, unit and passage; every derived figure runs as code over cited inputs. Click any cell and the exact line opens beside the answer, with the formula for anything calculated. Each citation is checked against its source before you see it, conflicts between sources are shown side by side, and the same question asked twice returns the same number.

We route each task to the model that does it best: small models we train do the reading, frontier models handle the question and the write-up, so every better model that ships makes Kepler better. Agents watch your coverage on schedules and on new filings, then send changes with the exact quote to Excel, PowerPoint, Slack, Claude or ChatGPT. Your engineers can build on the same data and engine through our API, MCP server and Python SDK.

Why it’s our pick for institutional teams

• Your firm’s ontology, so house definitions, coverage lists and internal models shape every answer (how it works).
• Every number provable: computed by code, cited to the page, line or cell, with formulas for derived figures, no add-on required.
• Coverage that keeps going: 14,000+ companies, with KPIs read from the filings and decks themselves.
• As filed or as restated, on any date, with recasts flagged.
• Agents you can leave alone, updating models and flagging guidance changes as companies file.
• Your playbooks, built in: the earnings update and the initiation template, saved once and run the same way every time.
• Firm-grade controls: one permission check on every read, every query logged with its lineage, SOC 2 Type II, our cloud or yours (security).

Questions research teams bring to Kepler

• “For our 40 mid-cap industrials, organic revenue growth on our definition for the last eight quarters, every input cited.”
• “Which software names in our coverage changed how they define ARR or net retention in the last two years? Quote both versions.”
• “When any holding files a 10-Q, update the model, flag changes to guidance and send me a summary before the morning meeting.”

Not the right fit if: you’re a single investor who wants a low-cost terminal for your own account. We deploy Kepler for investment firms, with our engineers on site.

See it on a company you cover at app.kepler.ai, or book a demo with your coverage list and definitions.

2. Daloopa

Best for: analysts who maintain their own Excel models and want source-linked historicals refreshed after every print.

Daloopa covers 6,000+ public companies with up to 14 years of history and links every data point to the filing or presentation behind it. An automated tagger captures values soon after a filing and analysts review them later; when a company restates, Daloopa keeps both the original and the restated value (docs). Data reaches Excel through its add-in and Scout agent, and AI assistants through its MCP connector.

Trade-offs: prices aren’t published, and the analysis and your firm’s adjustments stay in your spreadsheet. See Kepler vs Daloopa and Daloopa alternatives.

3. AlphaSense

Best for: teams whose research centers on broker research, expert calls and qualitative search.

AlphaSense searches more than 500 million documents, including research from 1,500 broker partners and 300,000+ Tegus expert transcripts, plus Canalyst models and standardized financials in its financial data module. Its SuperAnalyst agent, launched on October 6, 2026, plans multi-step work, runs code and produces memos, models and dashboards.

What users say: Advanced ratios “can be inconsistent and depends on the ticker,” a G2 reviewer wrote of AlphaSense’s financial data in August 2024 (AWS Marketplace, G2 reviews).

Trade-offs: an enterprise contract; SpendHound reports average spend of $10,131 a year for smaller customers and $139,455 for enterprise customers. See AlphaSense alternatives.

4. Koyfin

Best for: individual analysts and small teams who want the closest like-for-like terminal at a published price.

Koyfin lists a free tier, Plus at $39 a month and Premium at $79 a month on annual billing. Plus brings 10 years of financials and estimates, unlimited screens, watchlists and dashboards, filings, transcripts and premium news; Premium adds custom formulas, custom financial templates and portfolio analytics. Coverage spans 80,000+ companies.

Trade-offs: built for one person’s research, like Fiscal.ai’s terminal, with no firm deployment or house definitions.

5. Hudson Labs

Best for: guidance tracking and forensic red flags on US equities.

Hudson Labs covers every US public filer with market-wide AI search, restatement-adjusted numbers, a guidance model and pre-built and custom agents. Core is listed at $99 a month billed annually, and its Institutional plan adds forensic risk analysis, red flags and team sharing. Its No Hallucination Guarantee refunds $50 for each confirmed fabricated figure from management disclosures.

Trade-offs: US only, and focused on guidance and filings analysis more than full model-ready statements.

6. Marvin Labs

Best for: analysts who want an AI research workspace on a focused coverage list.

Marvin Labs combines AI Analyst Chat, Deep Research Agents that run on a schedule or on each earnings release, and a guidance scorecard. Standard is listed at $89 a month per user and covers 300+ global companies with 20 quarters of financials; Pro, at $399, adds custom coverage for up to 50 more companies and custom agents. Users can upload broker research and internal notes as private documents.

Trade-offs: coverage is set by plan, so long coverage lists need custom additions.

7. Bigdata.com

Best for: teams that want licensed text content inside Claude, ChatGPT or their own agents.

Bigdata.com, from RavenPack, supplies news, filings, transcripts, estimates and expert interviews through an API, an MCP server and connectors for Claude, ChatGPT and Microsoft Copilot. Its published plans list Pro at $40 a month and Max at $160 a month with the yearly discount, with point-in-time financials on Pro.

Trade-offs: a content layer: the analysis happens in whichever assistant you connect.

8. Claude for Financial Services or ChatGPT for Financial Services

Best for: firms that want a general AI assistant over data they already license.

Claude for Financial Services connects to providers including FactSet, Daloopa and Morningstar, with add-ins for Excel and PowerPoint. ChatGPT for Financial Services includes built-in data from providers including Daloopa, PitchBook and LSEG News; OpenAI’s help center notes that included datasets provide selected coverage. Both link answers to sources.

Trade-offs: each is a frontier model on its own, learning your definitions from what you paste or connect. Kepler runs inside both, so analysts can reach Kepler from the assistant they already use. See ChatGPT vs Claude for financial analysis.

What Fiscal.ai does well

Fiscal.ai packs a lot into a clean product: standardized and as-reported financials side by side, segment and KPI data for the largest companies, its own Agentic Consensus built from guidance and a statistical model, and parsed hedge fund letters. Its API and MCP server make the same data easy to use inside other tools.

Kepler vs Fiscal.ai

	Kepler	Fiscal.ai
Built for	Investment firms, deployed by our engineers	Individual and professional investors; developers by API
Where each number comes from	Pulled from the filing and computed by code	Fiscal.ai’s standardized and as-reported datasets
What a citation opens	The exact page, line or cell, with formulas for calculations	Source table image and PDF (API add-on)
KPI coverage	KPIs read from filings and decks across 14,000+ companies	Segments and KPIs for the largest 2,300 companies
Your firm’s definitions	Built into your ontology, applied every time	Standardized metrics plus company-reported adjusted figures
Your own documents	Models, notes, CRM and data rooms in your ontology	Public-company data
As filed vs as restated	Either, on any date	As-reported and standardized views
Work that runs on its own	Agents on schedules and filings, acting as their owner	Webhooks in the API
Where it works	App, Excel, PowerPoint, Word, Claude, ChatGPT, Slack, API	Fiscal.ai terminal, API, MCP

How to choose

• Model-ready historicals for Excel: Daloopa.
• Broker research and expert calls: AlphaSense.
• A low-cost terminal for one analyst: Koyfin.
• US guidance and red flags: Hudson Labs.
• An AI workspace on a short coverage list: Marvin Labs.
• Licensed content inside your own assistant: Bigdata.com, or Claude or ChatGPT for Financial Services.
• Research on your firm’s data and definitions, with every number provable: Kepler.

Then test with a company you know. Ask for a KPI the company redefined, as originally reported and as restated, and click every number back to its source. Count the results when you ask for every filing in your coverage that mentions a specific customer. Our guide to checking AI numbers has the full routine, and best AI for SEC filing analysis and best AI tools for equity research cover the wider field.

Kepler is live at app.kepler.ai. Run a company you cover and click any cell, or book a demo to see it on your coverage list.

Frequently asked questions

What is the best Fiscal.ai alternative for institutional investors?

Our pick is Kepler. Our engineers build your firm's ontology from your own models, notes and documents, extraction models read every filing and transcript in full, and code computes every number and cites it to the exact page, line or cell. Statements come from each filer's XBRL as filed or as restated, KPIs cover 14,000+ companies, and agents update your work when a company files.

Is Fiscal.ai the same as FinChat?

Yes. FinChat renamed itself Fiscal.ai in 2025. It sells a research terminal for individual and professional investors and a financial data API with an MCP server, which other platforms and AI assistants use as a data source. The API covers standardized and as-reported financials, segments and KPIs, estimates, ownership, transcripts and fund letters.

What data does Fiscal.ai cover?

Its API documentation lists financials and ratios with 20+ years of annual and 40+ quarters of history, segments and KPIs for the largest 2,300 companies globally by market capitalization, Street and its own Agentic Consensus estimates, insider and 13F ownership for US-listed companies, English-language earnings transcripts and parsed fund letters. A Sourcing to Filing add-on links each financial data point to an image and PDF of its source table.

Which Fiscal.ai alternatives publish their prices?

Koyfin lists Plus at $39 a month and Premium at $79 a month on annual billing. Bigdata.com lists Pro at $40 a month and Max at $160 a month on yearly billing. Marvin Labs lists Standard at $89 a month per user and Pro at $399. Hudson Labs lists Core at $99 a month billed annually. Daloopa and AlphaSense quote prices privately.

How is Kepler different from Fiscal.ai?

Fiscal.ai supplies standardized data and a terminal around it. Kepler is deployed for an investment firm: our engineers build the firm's ontology, so its own definitions, coverage lists and internal documents shape every answer, and every number is computed in code from the filing and cited to the line. Agents, playbooks and firm permissions run on the same platform.