AllMind vs Daloopa

They do mostly different jobs, which is why some desks run both. Daloopa is a fundamental-data layer: it parses filings, presentations and transcripts into hyperlinked line-item history, delivered through an Excel add-in, Data Sheets, an API, warehouse shares and a read-only MCP server. AllMind is an AI research system: it reads filings, live earnings, broker research and Expert Insights over a financial ontology, joins them to the firm’s own warehouse data queried in place, and produces the memo, deck or model.

If the unmet need is keeping Excel models current, Daloopa is the better buy. If it is multi-source work across a coverage list, Daloopa alone does not reach.

Daloopa does, if the model is already yours. Its Excel Add-In refreshes a workbook the analyst built, hard-coding the values so colleagues without the plug-in can still open the file, and Daloopa has stated key data lands within minutes of the release. Scout also builds a three-statement model from a prompt, though Daloopa’s product page still called Scout a beta with a limited number of customers on August 30 2026.

AllMind builds Excel models with live formulas and edits existing XLSX files, and its agents can run the earnings-day analysis unattended. It documents no add-in that rolls a quarter into a model your team already built.

Neither publishes a list price. Daloopa’s plans page shows a Free tier capped at three Data Sheets and three sales-gated tiers with no dollar figure anywhere: Core (Data Sheets, Excel Add-In, Scout, MCP), Premium (Core plus API) and Fundamentals API. No outside estimate of a Daloopa contract is traceable, so any dollar figure quoted elsewhere is unsourced. Cost is driven by tier, seats and, since Scout, AI credits metered against a monthly allowance.

AllMind is quote-based and arranged through a sales conversation. Nothing about it is self-serve: no card checkout, no monthly tier, so a buyer outside an institution is better off with a self-serve tool.

They are deep in different directions. Daloopa states 6,000+ global tickers, 14 years of history and 4 to 10 times more data points per company than other providers, plus daily OHLCV prices for 2,700+ companies added in May 2026. On segment, geography, guidance and KPI series inside one company, it leads.

AllMind’s claim is breadth across classes: 6,800+ premium data sources taking in S&P, FactSet, LSEG and MSCI data, broker research, Expert Insights transcripts, live news, newswires and press releases from thousands of sources worldwide, alternatives, global IR, live earnings and financials, and sector collections, across 40+ exchange and venue feeds. Live markets cover North America and Europe, with Asia and LATAM on the roadmap.

No, and Daloopa does not claim it can. Its Excel agent, Scout, builds and updates models inside a workbook, and Daloopa’s documentation states that Scout “cannot save files to your machine, so it does not export PDFs or PowerPoint files”, and works only in Excel with Daloopa data and no external connectors.

AllMind generates Word memos and research notes, PowerPoint decks off 20+ investment-bank templates, and Excel models carrying live formulas, and it edits PPTX, DOCX and XLSX files you already have. The caveat is that AllMind’s output comes out in whichever template it was given, so a house format of your own still costs a formatting pass.

Daloopa, clearly, if the goal is to feed an assistant your firm already runs. It publishes a read-only MCP server secured with OAuth and is available inside Claude and Claude for Excel, ChatGPT, Perplexity, Microsoft 365 Copilot and Google Gemini Enterprise, alongside a 37-endpoint API and native Snowflake, Databricks and S3-Parquet delivery.

AllMind publishes no MCP server and no public fundamentals API. Its agents run inside AllMind, where they inherit per-user entitlements they can never widen and every access is logged. So the choice is a governed data source under someone else’s assistant, or a governed workspace where the agent, the entitlements and the audit trail sit together.

It shows a real effect, though it is Daloopa’s own study, not proof. Across 500 retrieval questions, three frontier agent frameworks scored 20% to 71% on web search alone and converged at 89.2% to 90.8% once Daloopa’s data was connected over MCP.

The quoted “71 percentage point” gap is on the page, but it is one model: Claude Opus 4.5 at 91% with MCP against 20% on web search alone. Daloopa attributes that 20% to the tool, not the model: Claude’s web search returns snippets, not full pages or PDFs. Only about 7% of residual errors trace to Daloopa’s own data, and its structured data dates the study to February 2026.

Both is common, and often correct. Daloopa keeps the fundamentals substrate current and can sit underneath AllMind’s work as an existing license. What usually consolidates onto AllMind is the layer above: reading across broker research, expert transcripts, filings and internal files, the scheduled and event-triggered monitoring, and the deliverables that used to be assembled by hand.

Teams have retired point tools when they consolidated, and AllMind states automations covering up to 200 companies in a single run. What does not consolidate is the earnings-night refresh of a house model in Excel, because AllMind documents no add-in that writes into a workbook a team already built.