Daloopa
Daloopa is an AI-powered fundamental data platform for public equity professionals. It covers 6,000+ global tickers with fourteen years of history, hyperlinks every figure to its source filing, and delivers data through Excel, API, MCP and cloud warehouses.
What is Daloopa?
Daloopa is an AI-powered fundamental data platform aimed at professionals who research listed equities. It positions itself as the data infrastructure sitting underneath both traditional analyst workflows and the newer wave of AI agents in finance, and claims to be the first AI-powered fundamental and historical data service of its kind.
The dataset covers more than 6,000 listed companies worldwide with fourteen years of history, and Daloopa says it captures four to ten times more data points per company than rival providers. Figures are extracted from SEC filings, investor presentations, supplementals, footnotes, press releases and selected transcripts. They span reported financials, company-specific KPIs, management guidance, GAAP-to-non-GAAP reconciliations, segment detail and OHLCV market data for 2,700+ companies refreshed daily. Accuracy is advertised above 99% across millions of data points, with 99.5% quoted on the Scout page.
What sets the product apart is auditability: every number is hyperlinked back to the source document it came from, so any figure can be checked in one click rather than trusted blindly. Daloopa credits this to a collection of small, specialised models rather than one large language model, an architecture it says lets it swap modules as the technology moves.
The same data layer is delivered six ways: downloadable Data Sheets, an Excel Add-In that updates existing models in one click, Scout (an AI Excel agent currently in beta with a limited set of customers), a REST API with fifteen endpoint groups and thirty-seven endpoints, a read-only remote MCP server secured with OAuth, and cloud-native delivery into Snowflake, Databricks and Amazon S3.
Daloopa argues its case with numbers. It says it cuts 70% of the time spent building a new model when initiating coverage and saves roughly two hours per ticker during earnings season, across a thirteen-week research cycle. It also claims LLM agents without an embedded data layer hallucinate up to 70% of data points, a rate its own layer brings below 5%.
The company reports 185+ of the largest hedge funds, mutual funds and bulge-bracket banks among its users, alongside Anthropic, OpenAI, Perplexity and Microsoft. It is headquartered in New York and led by co-founder and CEO Thomas Li.
What it does
- Download ready-to-model data sheets covering 6,000+ listed companies worldwide
- Refresh an existing Excel model with new quarterly figures in a single click
- Verify any number by jumping straight to the original filing it was extracted from
- Build a three-statement model from scratch by prompting the Scout Excel agent
- Query fundamentals from Claude, ChatGPT, Perplexity or Microsoft 365 Copilot over MCP
- Pull 1,300+ standardised metrics programmatically to power analytics or AI agents
- Deliver the dataset natively into Snowflake, Databricks or Amazon S3 with no ETL
When to use Daloopa / When not to
A quick filter to help you decide if Daloopa is the right fit.
When to use Daloopa
- Equity analysts at hedge funds, mutual funds and investment banks who build and refresh financial models in Excel
- Buy-side and sell-side teams under time pressure during earnings season, updating dozens of tickers at once
- Quantitative researchers and data scientists who need deep, standardised fundamentals for backtesting and factor work
- Data and platform engineers piping fundamentals into Snowflake, Databricks or Amazon S3 without building an ETL stack
- Teams grounding LLMs and AI agents in auditable financial data through the Model Context Protocol
When not to use Daloopa
- Retail investors: the platform is sold to institutions and publishes no prices at all
- Anyone researching private companies, since coverage stops at roughly 6,000 listed tickers
- Users looking for trade signals, recommendations or order execution, because Daloopa supplies data, not advice
- Analysts who work mainly on mobile, as there is no iOS or Android application
- Teams intending to train their own AI models on the content, which the terms of use expressly forbid
How to use Daloopa
A typical end-to-end flow, from setup to results.
- Create a free account on the sign-up page for a two-week trial with up to three data sheets
- Or request a demo instead, since the three paid tiers are only sold through the sales team
- Sign in to the Daloopa Hub to reach the data once your account is open
- Download a full data sheet for a ticker, or drop it in as an extra tab in a model you already have
- Install the Excel Add-In to refresh your models with one click during earnings season
- Let the Add-In reconcile unit and sign differences between the company's disclosure and your model
- Click any figure to open the filing it was extracted from and check it yourself
- Prompt Scout inside Excel to build or extend a model, if you are part of the beta
- Authenticate against the API using the developer documentation, then poll the endpoints or subscribe to the four webhooks
- Connect the read-only MCP server over OAuth, or have cloud delivery provisioned into Snowflake, Databricks or Amazon S3
Pros & Cons
Pros
- Every figure is traceable to the document it came from, which makes the output defensible
- Unusual depth: four to ten times more data points per company than rivals, across fourteen years
- Lives inside Excel, so analysts keep the workflow they already have
- Six delivery modes (sheets, add-in, agent, API, MCP, cloud) without having to commit to one
- Hard-coded values mean a model does not break for a colleague who lacks the plug-in
- Official MCP connectors for Claude, ChatGPT, Perplexity, Microsoft 365 Copilot, Glean and Rogo
- A free account lets you test the data without going through a salesperson first
Cons
- No public pricing whatsoever: the three paid tiers each route to a sales conversation
- The free offer is a two-week trial capped at three data sheets, not a documented permanent free plan
- Coverage stops at listed companies, and the announced ticker count varies between pages and press releases
- Hosting and processing are United States only, with no EU data residency option
- No subprocessor list, no published customer DPA and no visible SOC 2 or ISO 27001 certification
- Scout, the agent that headlines the product suite, is still in beta with a limited set of customers
- Restrictive terms: mandatory arbitration, a class-action waiver, a USD 100 liability floor and a ban on training AI with the content
Pricing & Plans
Daloopa publishes no prices. The Plans page lists three paid tiers, Daloopa Core, Daloopa Premium and Daloopa Fundamentals API, and each of them routes to Speak with Sales, so no entry price is public. A free option does exist: an account giving access to up to three data sheets, described on the sign-up page as a two-week trial. Existing customers are directed to their account contact to discuss their subscription. Where charges apply, billing is by invoice settled through bank transfer rather than by card, and the terms of use cap Daloopa's liability at the greater of USD 100 or the amounts paid over the previous twelve months.
- Free — up to 3 Data Sheets
- presented on the sign-up page as a two-week trial
- with access to the 6
- 000+ ticker universe
- Daloopa Core — Data Sheets
- Excel Add-In
- Scout and MCP
- positioned as full access to the data across delivery methods
- with fast updates. Price on request
- Daloopa Premium — everything in Core plus API access and enhanced entitlements. Price on request
- Daloopa Fundamentals API — programmatic access alone
- aimed at client deliverables and analyst benchmarking. Price on request
Data, GDPR & hosting
A consolidated view of how Daloopa handles your data.
GDPR overview
GDPR is addressed explicitly, even though the site never writes the words GDPR compliant. The privacy policy, effective 28 May 2026, carries a dedicated section for residents of the EU, the United Kingdom, Liechtenstein, Norway and Iceland. Daloopa states it is the controller of personal data processed under that policy, and a processor when it handles data belonging to its customers' employees or end users. It lists its lawful bases, namely consent, contractual necessity, legitimate interests, legal obligation, vital interests and public interest, and grants access, rectification, erasure, withdrawal of consent, portability, objection and restriction, all exercised through hello@daloopa.com. Two Article 27 representatives are named: DP-Dock GmbH in Hamburg for the EU, DP Data Protection Services UK Ltd in London for the UK. Transfers to the United States may rely on standard data protection clauses.
Who owns the data?
Daloopa owns the Services outright. Under the terms of use, all content shown through the platform, including financial data, reports and information, is the intellectual property of Daloopa or its licensors, and using the Services grants no ownership rights whatsoever. Subscribers receive only a limited, non-exclusive, non-transferable, non-sublicensable and revocable licence for internal, personal use. Content a customer contributes may be used and modified internally by Daloopa, without disclosure, to run the Services and to generate what it calls Aggregated Anonymous Data; that anonymised, aggregated output is then used freely for Daloopa's own commercial purposes. Closing an account may destroy the content attached to it.
Reuse rights
Reuse is tightly restricted. The licence covers internal, personal use of the content while you are using the Services, and anything beyond that needs prior written consent. Customers may not copy, redistribute, modify, translate, publish, reproduce, broadcast, license, sell or otherwise commercialise the content, nor store a significant portion of it, nor use it to build a product that competes with Daloopa. Crawling, scraping, spidering and automated access outside the API are prohibited, as is reverse-engineering the Services. The terms also forbid using any content to train artificial intelligence, large language or machine-based models, forbid exposing it to open or public AI systems, and require that a customer's own third-party providers be contractually barred from training on Daloopa data or retaining it. Download features exist, but every restriction still applies to whatever has been downloaded.
Data retention & training
Hosting summary
Daloopa's privacy policy is unambiguous on jurisdiction: the Services are hosted and operated in the United States, by Daloopa and its service providers, and personal data is held on US servers. Users outside the United States are told that by using the Services they authorise the transfer, storage and processing of their data in the US and possibly other countries. Where a transfer needs a legal basis, the policy says it may rely on a data processing agreement incorporating standard data protection clauses. No European or regional data residency option is offered, and no hosting provider or subprocessor is named anywhere on the site. One technical caveat is worth stating: at the time of collection the domain resolved to an Amazon CloudFront edge node geolocated in Zurich. That describes the content delivery network serving the website, not where customer data lives; the policy's United States statement is the one that governs.
Where Daloopa works
Country-level availability.
Not available in
Things to keep in mind
Risks and trade-offs to weigh before adopting Daloopa.
- Hard-coded figures are convenient, but they freeze a snapshot: a model left unrefreshed goes quietly stale, with no broken formula to warn you
- One-click updates and a prompt-driven agent make it easy to stop reading the filings yourself, and the judgement that came from that reading goes with them
- Daloopa disclaims all warranties on accuracy, reliability and completeness, and states plainly that AI outputs may be wrong: human review is your responsibility, not the vendor's
- Contractual liability is capped at the greater of USD 100 or twelve months of fees, which is very little against a decision made on a bad number
- Disputes go to individual binding arbitration with a class-action waiver; opting out means posting a written notice within thirty days of first accepting the terms
- Personal data is hosted and processed in the United States, with possible transfers elsewhere, and no subprocessor list is published to say who else touches it
- The terms forbid training AI on the content or exposing it to public AI systems, and extend that duty to your own third-party providers: check this before wiring the data into internal tooling
Setup & Integrations
Technical difficulty
It depends entirely on the delivery mode. Data Sheets need nothing beyond a download, and the free account is self-service. The Excel Add-In is a standard add-in install and then works without formulas. The MCP server is remote and connects over OAuth, so there is nothing to host. The API asks for real engineering: authentication, REST endpoints returning JSON or CSV, and four webhooks to implement. Cloud delivery is provisioned by Daloopa with no ETL or pipeline work, and a standardised taxonomy removes manual mapping. All paid tiers require a sales conversation and an order form.
Deployment
Integrations
Behind Daloopa
Fundraising
Social
Resources
All the official URLs gathered for verification and reference.
Frequently asked questions
How many companies does Daloopa cover?
Do I have to use Daloopa's data sheets to get one-click updates?
Are international companies covered?
Will my model still work offline?
Is there an API?
Can I plug Daloopa into an LLM?
How much does it cost?
Is there a free trial?
Where is the data hosted?
Can I use Daloopa's content to train an AI model?
Should you pick Daloopa?
Daloopa is a specialist tool that knows exactly what it is: fundamental data for people who research listed equities, delivered with an unusual obsession for traceability. The feature that matters most is the hyperlink on every figure, which turns a spreadsheet from something you have to trust into something you can check. Combined with coverage of 6,000+ tickers, fourteen years of history and four to ten times the depth of rival datasets, that makes a credible case for the analysts, portfolio managers and valuation teams it targets.
Its second act is arguably more interesting. By publishing a read-only MCP server and a documented API, Daloopa has positioned itself as the grounding layer for financial AI agents rather than as one more data vendor, a bet that Anthropic, OpenAI, Perplexity and Microsoft appear to have taken alongside 185+ institutional users, and that a USD 47 million Series C in May 2026 has funded.
The reservations are real. Nothing about pricing is public: all three paid tiers route to a salesperson, billing is by invoice and bank transfer, and the free option turns out to be a two-week trial rather than a lasting free plan. Scout, the Excel agent that headlines the suite, is still in beta with a limited set of customers. And for a vendor selling to regulated financial institutions, the compliance surface is thin: hosting is United States only, no subprocessor list is published, no customer DPA is offered, and no SOC 2 or ISO 27001 certification appears anywhere on the site.
Worth a serious look if you build or maintain models on public companies, or if you are grounding an AI agent in financial data. Budget a conversation with sales, and put security and data residency on the agenda early.
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