AllMind AI vs Daloopa
Both put AI to work in fundamental research, but they sit at different layers of the stack. AllMind AI is the AI-native research terminal analysts and PMs use to turn a thesis into a cited model, memo, or deck without leaving the platform. Daloopa is the AI fundamental-data layer that extracts granular, source-linked historicals and KPIs from filings and keeps your own Excel model current.
Reads and cites filings, earnings, broker research, and live market data, then builds the models, memos, and decks.
Extracts granular, source-linked historical financials and KPIs from filings into auto-updating Excel models and feeds.
If your edge is the deepest source-linked fundamentals feeding your own Excel model, choose Daloopa. If it's researching and building the finished, cited deliverable, choose AllMind.
Feature by feature
An honest, side-by-side view. Ties are shown as ties, and where Daloopa leads, it’s marked too.
Compiled from public sources and AllMind product documentation. Capabilities marked “Unclear” are not publicly documented by Daloopa at the time of writing.
See AllMind on your own coverage→Why research teams choose AllMind AI
AI that researches, then builds the deliverable
Daloopa gets clean, source-linked numbers into your model. AllMind starts a step earlier and finishes a step later: ask a question and it reads and cites the filings, earnings, and broker research, forms the analysis, then produces what comes next: a full equity model with comps and DCF in minutes, an investment memo, or a CIO-ready deck the same afternoon. Daloopa is the fundamental-data layer that feeds a model; AllMind is the terminal that researches the thesis and writes the finished, cited output around it.
One research stack, not a single data feed
Daloopa is world-class at one input: extracted fundamentals from filings and presentations. AllMind brings the rest of the desk into one terminal that reasons and cites across all of it: built-in sell-side broker research, Third Bridge expert-call transcripts, live trading-grade market data, supply-chain and alternative data, news, and your own data room. Daloopa focuses on the fundamental-data layer, so most firms still pair it with the terminals and research services that supply everything else; AllMind consolidates them.
Agents, workspaces, and your own data warehouse
AllMind runs research agents concurrently in dedicated workspaces, schedules recurring runs delivered as Word or PDF, and tracks event triggers across up to 200 companies in a single automation. It connects directly to your data warehouse or lakehouse (Snowflake, Databricks, S3) through scoped, role-based IAM, querying it in place so nothing leaves your environment, and reasons over it alongside grids, reports, and the data room. Daloopa also delivers its data cloud-natively into Snowflake, Databricks, and S3 and monitors filings to keep models current, but its agents build and refresh spreadsheets; they don't research a thesis or write the deliverable.
Where Daloopa is strong
Daloopa is genuinely best-in-class at the job it set out to do: turning filings, presentations, and transcripts into deep, auditable fundamental data. It covers 6,000+ global tickers with 14 years of history and captures 4–10x more data points per company than typical providers — guidance, KPIs, segments, and geographic detail — at a stated >99% accuracy, with every number source-linked back to the original filing. Its Excel Add-in refreshes an analyst’s existing model the moment earnings drop, and Scout, its AI Excel agent, builds and populates models from a prompt. That same structured data now feeds AI systems through an MCP layer used by Anthropic, OpenAI, Perplexity, and Microsoft 365 Copilot, and a benchmark it published showed AI-agent accuracy improving by up to 71 percentage points when grounded in its data versus web retrieval. Backed by a $47M Series C and trusted by 160+ hedge funds, mutual funds, and banks, Daloopa is the reference standard for the fundamental-data layer. If your bottleneck is getting the deepest, most trustworthy history into your own model, it’s exceptional. AllMind’s focus is the wider workflow around that data: researching the thesis and building the cited deliverable.
Two tools, two different jobs
A unified AI research terminal for buy-side and sell-side teams that acts on the information it surfaces. Run natural-language analysis over filings, earnings, broker research, live market data, and your own documents and data warehouse (Snowflake, Databricks, S3), then ship cited models, memos, and decks without switching tools.
Research-driven analysts and PMs who want one platform that turns sources into cited, firm-template deliverables across asset classes, instead of a separate data feed to stitch into their own tools.
- Acts end-to-end: cited models, memos & decks
- Live market, broker, expert & supply-chain data in one place
- Agent Studio, workspaces & event automation across up to 200 companies
- Your own data room and warehouse (Snowflake, Databricks, S3)
- Multi-asset: equities, credit, wealth, private markets
An AI-powered fundamental-data infrastructure that extracts granular, source-linked historical financials and KPIs from filings, presentations, and transcripts. Daloopa delivers that data into your Excel model through an Add-in, the Scout AI Excel agent, Data Sheets, an API, and an MCP layer that feeds AI systems and cloud warehouses.
Fundamental analysts and AI teams whose core need is the deepest, most auditable historical data flowing automatically into their own models and pipelines, kept current the instant a company reports.
- Deep source-linked fundamentals: 6,000+ tickers, 14 years
- 4–10x more data points per company at >99% accuracy
- Excel Add-in & Scout keep your own model current at earnings
AllMind AI and Daloopa reward different edges: researching and building the deliverable, or the depth of the data feeding your model.
If your edge is acting on the research, choose AllMind AI: it builds cited models, memos, and decks while consolidating broker, market, expert, and supply-chain data with your own documents and data warehouse on one platform. If your edge is the deepest, most auditable fundamental history flowing straight into your own Excel model and AI pipelines, Daloopa is the stronger fit — and the two can sit side by side.
AllMind AI vs Daloopa, answered
They overlap, but they solve different jobs, so many teams evaluate them together and some run both. Daloopa is an AI fundamental-data layer: it extracts granular, source-linked historical financials, KPIs, segments, and guidance from filings and presentations, then keeps your own Excel model current through an Add-in, its Scout AI Excel agent, an API, and an MCP feed. AllMind AI is an AI-native research terminal that researches a thesis and builds the finished deliverable: it reads and cites filings, earnings, and broker research, adds live market data, expert calls, supply-chain and alternative data, then produces cited models, memos, and decks. Teams that mainly need the deepest, most auditable data in their own model lean to Daloopa; teams that want to go from question to a cited deliverable on one platform choose AllMind AI.
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