Daloopa launches Scout, an Excel-native AI agent for financial modelling
Daloopa has released Scout, an AI agent embedded directly in Microsoft Excel that the company says can build financial models from scratch, update them after earnings releases, and run cross-company comparisons, all driven by plain-language prompts. The product is now generally available and is positioned by the company as a direct replacement for the manual data-pulling work that occupies a significant share of a buy-side or sell-side analyst's day.
Scout is built on Daloopa's proprietary structured database of financial data sourced from public filings, which the company says covers more than 6,000 companies globally. Every figure inside a Scout-generated model is hyperlinked back to the original source document, allowing analysts to audit the output without leaving the spreadsheet environment. Daloopa says this traceability is what differentiates Scout from generic large-language-model assistants that generate financial tables without grounding them in verified primary data.
What Scout does
The agent supports a set of workflow features designed around existing analyst habits: quick-start prompt templates, user-defined custom skills for saving frequently used commands, configurable default formatting, and a persistent chat history for returning to prior sessions. Daloopa says the product was designed by former analysts, a claim that implies familiarity with the nuances of earnings model maintenance, notably the repetitive but precision-sensitive process of updating line items after a company reports quarterly figures.
Thomas Li, chief executive of Daloopa, said analysts should spend their time applying judgement rather than on "the manual, error-prone work of building and updating models." Scout, he added, combines AI with verified data directly in Excel, giving analysts "greater speed without sacrificing the rigor their work demands."
Market context and competitive landscape
The market for AI-assisted financial research tooling has become crowded rapidly. A number of well-funded startups are competing to automate the modelling and data-aggregation layer of equity research, sitting alongside incumbent financial-data platforms that are adding AI features to their existing products. The key competitive battleground is data quality and auditability: institutional analysts are constrained by internal compliance frameworks and cannot rely on AI output that cannot be traced to a named source document.
Daloopa's approach of anchoring outputs to a structured, source-linked data layer addresses that constraint directly. The Excel-native delivery channel is also strategically significant. Rather than asking analysts to migrate to a new interface, Scout meets them in the tool they already use, reducing adoption friction and avoiding the integration and security-review overhead that a standalone web application would typically require in a regulated financial-services environment.
The enterprise software category is also seeing broader pressure from Microsoft's own Copilot integrations in Office 365, which include Excel-specific AI features. Daloopa's differentiation rests on the depth and verification of its underlying financial dataset rather than the interface layer alone. Whether that data moat is durable will depend on the pace at which larger incumbents can match the coverage and source-linking capabilities Scout is built on.
Daloopa did not disclose pricing, the number of customers currently using Scout, or any revenue figures in its announcement. The company said analysts at "the world's top financial institutions" trust its platform but did not name any specific clients. A demo is available via the company's website.