Daloopa

What it is

Daloopa turns public-company disclosure into model-ready rows. It reads SEC filings, press releases, investor presentations, supplementals and selected transcripts, extracts the reported financials plus the segment breakdowns, operating KPIs, guidance and GAAP-to-non-GAAP bridges around them, and delivers the result where an analyst already works — an Excel model, an API, a warehouse table or an LLM session, each figure hyperlinked to the page it came from.

The vendor's headline numbers, as published today: 6,000+ tickers globally, about 14 years of history per company, 4–10x more data points per company than conventional providers (its API and MCP pages say 5–10x for the same claim), and accuracy above 99%.

Pricing

Quote only. The plans page carries four columns — Free, Daloopa Core, Daloopa Premium and Daloopa Fundamentals API — and not one price. All three paid plans have a "Speak with Sales" button; existing customers are told to contact their account owner, and the demo form asks for firm type before anything else.

The free account gives you one data sheet download inside a two-week window and nothing else: no Excel add-in, no Scout, no MCP, no API. Core adds data sheets, the add-in, Scout and MCP with monthly limits; Premium adds the API on top; the Fundamentals API plan is the programmatic product alone. Contract length and minimum seat count are published nowhere public.

Data & coverage

Equities only, and the geography is uneven. The fundamental dataset is global — the FAQ says most major international companies are covered, including filers reporting in other languages — but daily OHLCV price data is US-listed only, 2,700+ names against the 6,000+ with fundamentals.

Standardisation covers less than extraction does. The taxonomy documentation, updated August 2026, reports 2,696 canonical metrics across 3,488 companies, 68 standardised metrics per company on average, and 77 industry templates. Everything outside that mapping is still there, but under the company's own series names — which is the work the taxonomy exists to avoid.

Integrations

The Excel add-in ships only through the Microsoft add-in stores, so a locked-down firm needs its Microsoft 365 admin to deploy it. It writes hard-coded values rather than live formulas — a recipient without the add-in still sees numbers, and nothing #REFs out — and reconciles units and signs against how you model rather than how the company disclosed. Scout, the AI agent in the same task pane, builds and updates models from Daloopa data only.

API v3 covers companies, series, fundamentals, documents, taxonomy, market data and consumption, rate limited to 120 requests per minute per key, with four webhook event types for new and restated data; exports are Parquet by default, CSV on request. The dataset is also on the Snowflake and Databricks marketplaces, or dropped as Parquet into a dedicated S3 bucket. The MCP server is remote, read-only and OAuth-protected — an unauthenticated call returned 401 on 13 September 2026.

Limitations

• No public pricing, no self-serve upgrade, no published minimum. An individual investor cannot buy this; the free account exists to show a firm what the data looks like.
• Extraction is machine-first. The docs describe an autotagger capture available within minutes of a release and an analyst-reviewed capture that arrives later as a separate record with a different fundamental_id, so a number pulled at 4:05pm is not necessarily the one a human signs off on. Checking the figures your thesis rests on stays your job.
• The audit trail points at the source document, not at a reconciliation: it says where a figure came from, not whether Daloopa read it the way you would have.
• Price data is US-only and daily. This is not a market data feed and does not pretend to be.
• Excel and Microsoft 365 only for the add-in and Scout — no Google Sheets, no runs with Excel closed, and transcripts are keyword-searchable but not retrievable whole.
• Consumption is metered: monthly datapoint ceilings, per-company series limits and warehouse subscription slots, all set by your contract.

Alternatives

Fiscal.ai covers similar ground at a price a person can see, with a terminal and an MCP server, but far less depth on company-specific KPIs. Intrinio and Financial Modeling Prep sell standardised statement data by API for a published monthly figure — fine for the income statement, useless for the slide-16 metric. Visible Alpha and Tegus sit closest on the institutional side. Otherwise the competition is a junior analyst with a spreadsheet.