Daloopa Alternatives for Analysts and Investment Firms

The best Daloopa alternative is Kepler, for teams that want source-linked historicals plus everything that comes after them. We build statements from each filing’s XBRL with every cell cited, as filed or as restated, and put your own documents, your firm’s definitions, the analysis and the agents on the same platform, so a number never has to be retyped from one tool into the next.

Kepler is ours, so it’s first and labeled. Vendor facts link to their own pages, press releases and public reporting as of November 2026, and prices appear only where a vendor publishes them or a reputable source reports them.

Why teams look for Daloopa alternatives

• Coverage. 6,000+ companies covers most active coverage lists. Teams with a long small-cap tail, frontier markets or private companies need more.
• Their own documents. Research notes, internal models, CRM, data rooms and portfolio data hold the context a firm cares most about, and a public-data feed doesn’t include them.
• The analysis on top. Historicals feed a model. Screens, earnings notes, comps on the firm’s definitions and thesis tracking need another tool, and every hop between tools is a chance to retype a number. A Wall Street Oasis thread on finance AI put the choice plainly, asking whether firms prefer “a solution that can do just one thing (ie Daloopa is just Excel tool) vs a full stack solution that can act as a true co-pilot throughout the work journey.”
• Firm definitions. Each firm adjusts EBITDA, free cash flow and segment mappings its own way, and those rules live in each analyst’s spreadsheet.
• Pricing visibility. Daloopa’s plans page lists a free tier and paid plans without published prices.

Daloopa alternatives at a glance

Tool	Best for	Coverage	Your own data	How it shows sources
Kepler	Statements cited to the line, plus your documents, definitions and analysis	14,000+ companies, 120+ countries	Ontology built from your notes, models, CRM and documents	Exact page, line or cell; formula for each calculation
S&P Capital IQ / Visible Alpha	Consensus with driver-level detail	8,000+ companies in Visible Alpha	Through S&P’s AI tools and your uploads	Citations in ChatIQ document answers
FactSet	Firms on FactSet for estimates and fundamentals	Global	Through your AI assistant	Depends on the workflow
Fiscal.ai	One analyst on a budget	Segments and KPIs for the largest 2,300 companies	No	Source table image and PDF (add-on)
AlphaSense (Canalyst Models)	Pre-built drivable models plus broker research	4,000+ models	Connectors to SharePoint, Box and other file stores	Exact text snippet in search
Bigdata.com	Licensed text content inside AI tools	Filings for 90,000+ companies	Uploads	Excerpts with source, date and URL
Hudson Labs	Guidance and red flags on US equities	All US issuers and ADRs	No	Links to filings and calls

The best Daloopa alternatives

1. Kepler (our pick)

We build Kepler.

Best for: teams that want source-linked historicals plus everything that comes after them: their own documents, their own definitions, the analysis, agents and every surface their analysts work in.

Daloopa answers one question well: what did the company report, and where? Most teams then need a second and third tool for the rest of the job: reading the call, checking the footnote, applying the firm’s adjusted EBITDA, writing the note. We built Kepler so all of it happens on one platform, built around your firm.

Statements come straight from the filer’s XBRL, assembled in code with no model in the path. Every cell opens to the page and line it came from, and you choose whether a figure appears as originally filed, as restated, or as it stood on a given date. That matters more than most teams expect: in our paper Reclassified to Conform, 194 of 200 companies reshaped a statement in ways XBRL tags can’t follow, and we rebuild each history so every line keeps one meaning across years. KPIs and segment data that live outside the statements, in MD&A tables, footnotes and earnings decks, are read by extraction models we train and stored with their scale, unit and passage. Coverage spans 14,000+ companies in 120+ countries, with 26M+ SEC filings and 40M+ transcripts and IR documents read in full at ingest. (New to the tagging behind it? Read what XBRL is.)

Beyond the data, our forward-deployed engineers build your firm’s ontology: your coverage list, your KPI names, your definition of each adjusted metric and your private-company and portfolio data, each resolved to one record per entity. Analysts ask questions in plain English and get computed answers with each number cited to the line. Our agents refresh your models and comps when a company files and send the changes with sources. It runs in Excel, Word, PowerPoint, Slack, Claude and ChatGPT, and your engineers build on the same data, ontology and engine through our API, MCP and Python SDK. Our engineers built this layer before at Palantir, and they stand up your ontology on site, with your analysts, in the first week.

Why teams pick Kepler as their Daloopa alternative

• Statements and KPIs with every cell cited, as filed or as restated, on any date.
• Your own documents, models, CRM and notes alongside public data, in one ontology.
• Your firm’s definitions applied to every company, every time.
• Answers, notes and comps in your house format, from saved playbooks.
• Agents that update models when a company files, with every change cited.
• 14,000+ companies across 120+ countries, private companies included through your own data.
• Your firm’s permissions on every read, a lineage record for every query, calculation and agent run, SOC 2 Type II, and deployment in our cloud or yours.
• We route each step to the model best suited to it, so each new frontier model makes the platform better.

Questions teams ask Kepler

• “Build a three-statement model for Snowflake from its filings, every cell cited, with restated years flagged.”
• “Add the KPIs we track for each software name in our coverage, on our definitions, back eight quarters.”
• “Refresh the comps sheet when each company in the peer set files its 10-Q, and tell me what moved.”

“I can see exactly where every number and conclusion comes from, which means I can defend the work.” (Vice President, Global Investment Bank)

Not the right fit if: you’re an individual investor who only needs a low-cost historicals feed for a personal spreadsheet.

Try it on a company you model at app.kepler.ai, or book a demo.

2. S&P Capital IQ Pro and Visible Alpha

Best for: teams that want consensus estimates with driver-level detail, plus S&P’s standardized data.

S&P Global completed its purchase of Visible Alpha in May 2024. Visible Alpha’s consensus covers more than 8,000 companies across 170+ industries from 250+ contributing sell-side brokers, with driver-level metrics, contributor counts, dispersion, standardized line items and segment data, according to S&P’s October 2026 release. S&P added it to its AI Data Portal that month; the portal works with MCP-enabled assistants such as Claude, ChatGPT, Gemini and Copilot, and S&P also connects Capital IQ data to Claude and Microsoft 365 Copilot. Inside Capital IQ Pro, ChatIQ Spreadsheets, added in October 2026, turns a request into an Excel model wired with refreshable Capital IQ formulas, and multi-document ChatIQ answers across filings and transcripts with citations. On October 27, 2026, S&P announced a strategic review of Capital IQ and its associated data businesses.

Versus Daloopa: consensus and segment estimates, which a historicals feed doesn’t supply, from one large vendor.

Trade-offs: an enterprise contract with S&P, and standardized data follows S&P’s templates, so your own adjustments still live in your models.

What users say: “Financial reporting can be sometimes a bit inconsistent, partly because of changing reporting standards from a Reporting Company,” a business analyst wrote in a four-star Capterra review of Capital IQ in April 2026.

3. FactSet

Best for: firms that already run on FactSet and want its data inside their AI tools.

FactSet’s MCP server launched in December 2025 with nine datasets (Fundamentals, Consensus Estimates, Ownership, M&A, Pricing, People, Events, Supply Chain and Geographic Revenue) and has since added fixed income, private company and macroeconomic data. A Claude connector followed in February 2026 and a Gemini Enterprise integration in August, after a June partnership with Google Cloud to put Gemini into FactSet Workstation.

Versus Daloopa: broader datasets (estimates, ownership, supply chain) from a single licensed source.

Trade-offs: contract pricing, and FactSet supplies the data while the reasoning happens in whichever assistant you connect.

4. Fiscal.ai

Best for: an individual analyst or small team that wants standardized financials and KPIs at a low price.

Fiscal.ai, formerly FinChat, carries financials back 20+ years and segment and KPI data for the largest 2,300 companies globally, with 12+ annual and 24+ quarterly periods. A Sourcing to Filing add-on to its financials API attaches a source image and PDF to each figure, the MCP server plugs into the major AI assistants, and API and MCP access are sold separately from Terminal Pro, which was reported in October 2026 at $39 a month on an annual plan.

Versus Daloopa: published, self-serve pricing and a full research terminal around the data.

Trade-offs: KPI depth stops at the largest 2,300 companies, and there’s no deployment on a firm’s own documents or definitions.

5. AlphaSense (Canalyst Models)

Best for: analysts who want pre-built, drivable company models next to broker research and expert calls.

Where Daloopa delivers data points, AlphaSense sells finished models. Canalyst Models, which came with the 2024 Tegus acquisition, sit inside its Industry Comps and Models module: more than 4,000 pre-built models, drivable in Excel through a Modeling Tools add-in for Premium Models subscribers. SuperAnalyst, an agent launched on October 6, 2026, produces financial models and memos, and Work Products, added in July 2026, put financials and comps into Excel with source links.

Versus Daloopa: complete drivable models, plus the research library around them.

Trade-offs: the models follow Canalyst’s structure, and AlphaSense quotes prices privately; SpendHound reports average spend of $10,131 a year for smaller customers and $139,455 for enterprise customers. More options are in our AlphaSense alternatives guide, and Daloopa vs Canalyst compares the two model sources directly.

What users say: “I still find errors or misinterpretations in outputs,” a G2 reviewer wrote of AlphaSense in October 2026 (AWS Marketplace, G2 reviews).

6. Bigdata.com

Best for: teams building their own AI agents that need licensed text content with citations.

Bigdata.com, from RavenPack, supplies filings for 90,000+ companies in 50+ countries, full transcript history, research from 100+ providers and 200+ licensed news sources back to 2000. It works through an API, an MCP server with more than 20 tools, and as a connector in Claude, ChatGPT and Microsoft Copilot, with Grok agents supported through the xAI API. Published plans include Pro at $40 a month and Max at $160 a month, billed annually.

Versus Daloopa: far wider company coverage and the text around the numbers.

Trade-offs: it’s built around documents and excerpts, so teams that need model-ready historicals pair it with a data source or a platform that computes them.

7. Hudson Labs

Best for: guidance and forensic flags on US equities, at a published price.

Hudson Labs covers all US issuers and ADRs, captures guidance automatically, tracks operating metrics and catalysts and scores forensic risk. Core is published at $99 a month billed annually, and its No Hallucination Guarantee refunds $50 per confirmed fabricated figure from management disclosures.

Versus Daloopa: a cheaper way to track guidance and red flags across a US list.

Trade-offs: US only, and it doesn’t deliver full historical statements for a model.

What Daloopa does well

Daloopa’s historicals cover 6,000+ public companies, reach back up to 14 years across fundamentals, KPIs and guidance, and link every figure to the filing or deck it came from, with data that starts updating within minutes of an SEC filing on average, Daloopa says. Scout, its Excel agent, opened to general use in September 2026, and its MCP connector puts the same data inside Claude, ChatGPT, Microsoft 365 Copilot, Gemini Enterprise and Perplexity.

Kepler vs Daloopa

	Kepler	Daloopa
What you get	The full platform: data, your firm’s ontology, a deterministic engine, cited answers, agents and every surface	Source-linked historical data for models, plus the Scout Excel agent
Where each number comes from	Pulled from the filing and computed by code; statements from XBRL with no model in the path	Extracted from filings and presentations, each point linked to its document
What the citation points to	The exact page, line or cell, with the formula for anything calculated	The source document behind each data point
As filed vs as restated	Either, on any date	Keeps both the original and the restated value (Daloopa docs)
Coverage	14,000+ companies in 120+ countries; 26M+ SEC filings, 40M+ transcripts and IR documents	6,000+ public companies
Your own documents	Notes, models, CRM, data rooms and portfolio data in your ontology	Outside its scope
Your definitions	Built into your ontology, applied to every company	Kept in your own spreadsheet formulas
Analysis and writing	Questions, screens, comps, notes and memos, every number cited	In Excel or the AI assistant you connect
Work that runs on its own	Agents update models and send changes when a company files	Data updates after filings; Scout updates models
Where it works	App, Excel, Word, PowerPoint, Claude, ChatGPT, Slack, API, MCP, Python SDK	Excel, API, MCP connectors in AI assistants
Rules and audit	One permission check on every read, every query logged with its lineage, SOC 2 Type II	Data access under your Daloopa plan

Choose Daloopa when the job is a fast, source-linked historicals feed into existing Excel models and the rest of the workflow is settled.

Choose Kepler when you want those historicals cited to the line and the rest of the job on the same platform: your documents, your definitions, the analysis, the note and agents that keep it current. Our Kepler vs Daloopa page walks through a worked example.

How to choose

Start with the gap you’re trying to close:

• Consensus and driver-level estimates: S&P Capital IQ with Visible Alpha, or FactSet.
• Pre-built drivable models: AlphaSense’s Canalyst Models.
• One analyst, low cost: Fiscal.ai or Hudson Labs.
• Licensed content inside your own agents: Bigdata.com.
• Historicals cited to the line, plus your documents, definitions, analysis and agents: Kepler.

Then test on a company you model. Ask each tool for a segment that was reorganized, both as originally filed and as restated, and click every cell. Our guide on how to check AI-generated numbers has the full routine, and the best AI for SEC filing analysis and best AI tools for equity research cover the wider field.

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

Frequently asked questions

What is the best Daloopa alternative?

Kepler, for teams that want source-linked historicals and the rest of the job on one platform. We build statements from each filing's XBRL with every cell cited, as filed or as restated, and our forward-deployed engineers build your firm's ontology from its own notes, models, CRM and documents, so your definitions and private data sit in the same answers. Code computes every figure, and agents update models when a company files.

What does Daloopa do?

Daloopa is a source-linked historicals provider: fundamentals, KPIs and guidance on more than 6,000 public companies, reaching back as far as 14 years, with each data point tied to the filing or presentation it came from. Analysts use it to fill and update Excel models, and its MCP connector puts the same data inside Claude, ChatGPT, Microsoft 365 Copilot and other AI tools. Scout, its Excel agent, builds and updates models.

Who are Daloopa's main competitors?

For model-ready historicals and estimates: S&P Capital IQ with Visible Alpha, FactSet, AlphaSense's Canalyst Models and Fiscal.ai. For licensed content inside AI tools: Bigdata.com. For guidance tracking on US equities: Hudson Labs. For a full platform built around the firm, with statements from filings, the firm's own documents and definitions, and analysis on top: Kepler.

How much does Daloopa cost?

Daloopa doesn't publish prices. Its plans page lists a free tier with up to three Data Sheets, Core and Premium plans (Premium adds API access) and a separate Fundamentals API plan. ChatGPT for Financial Services includes Daloopa data on a 24-hour delay, capped at 3,000 data points per user a month, according to OpenAI's help center.

What happened to Canalyst?

Canalyst's company models came to AlphaSense with its 2024 acquisition of Tegus. They're now sold as Canalyst Models inside AlphaSense's Industry Comps and Models module, with more than 4,000 pre-built models and a Modeling Tools Excel add-in for Premium Models subscribers. Canalyst.com still lists the coverage and sends demo requests to AlphaSense.