Meet the Best Financial AI Agent -
Designed for Use Directly In Excel
Build, analyze and update your models with Scout – the only AI Excel agent with data infrastructure directly built-in.
Officially Launching Early 2026
The agent that lives where you analyze
Financial AI agents currently operate outside of the analyst workflow, with no way to translate outputs to the format that matters most: Excel.
Scout was built by former financial analysts, to deeply understand the use cases, features and customizability that are required for actionable outputs, directly in Excel.
Scout ensures:
Zero hallucination:
Your models are precious – Scout is the only Excel agent with data built directly in, ensuring that each data point used in building analyses is credible and accurate.
Maximum transparency:
Each data point referenced has a link to the source filing, and all calculations, adjustments or forecasts are clearly shown in the agent, ensuring 100% confidence in the outputs.
Intelligent interpretation:
If reliable data or context are missing, Scout will identify gaps, disclose them and proactively provide solutions or models to get to the intended end result.
Why native data layers matter for finance agents
Agents are only as smart as their data — built-in context makes them powerful.
When tasked with financial retrieval, LLM’s without built in data can hallucinate up to 70% of data points, making outputs unusable for rigorous and precise action.
Daloopa’s native data layer minimizes hallucinations in model outputs to less than 5% by providing:
- 4-10x more historical data points per ticker than any other fundamental data providers in the space
- Data parsed from all public filings: SEC filings, investor presentations, press releases, transcripts
- Actionable data points like GAAP-non-GAAP, guidance, KPIs and segmentals
- Proprietary AI parsing and QA workflow for all data referenced with a 99.5% accuracy rate
The Unparalleled Power of Scout
Build models and complex analyses from scratch
Initiate coverage on a name or multiple names instantly by creating models from scratch with a single prompt. Specify key assumptions, time frames or projections with calculations and adjustments clearly broken out in Scout.
Prompt Examples:
“Using all available historicals, build me a three statement model for CMG”
“For RBLX, pull customer growth, ASPs, ARPU and interpret against their guidance. Help me understand the probability of beating guidance.”
Populate existing models with all historical data
Use your own templates and builds, and instantly update new fields or periods with data, matching your native formatting. Keeps your unique modeling preferences and ensures that your model is always suited to your workflow.
Prompt Examples:
“Use my template for PYPL to build a model for SQ”
“Fill my model for CVNA with the latest updates from the most recent earnings print. Add any new disclosures that have come out that I may have missed”
Create comprehensive comparative industry models
Aggregate comparable metrics across tickers and industries with zero manual work, and analyze key trends among players in an instant.
Prompt Examples:
“Benchmark marketing spend across the major Quick Service Restaurants (QSRs) operating in the US”
“For the major semiconductor players, show their revenue and compare rev growth and market share over the last five quarters. Provide an analysis of which player is the winner in the next 5 years.”
Sharper analysis starts with complete data
Scout aims to equip analysts to fully explore all financial perspectives with the best data, leaving the professionals to make the most informed call possible. We’re finalizing Scout to ensure analysts spend time on the things that matter – actioning, strategizing and positioning at scale.