Daloopa vs Staats: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Daloopa and Staats — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Daloopa
Daloopa
A financial-modeling copilot and fundamental-data provider that populates Excel and LLM workflows with structured public-company financials and KPIs.
Key features
- Structured Fundamentals Extraction: Automatically extracts and normalizes financial statements and operational KPIs (income statement, balance sheet, cash flow, and custom metrics) from SEC filings, investor presentations, and PDFs into structured time-series formats.
- Excel Model Integration: Populates and updates users' native Excel models with source-linked fundamental data and formulas, enabling one-click refreshes of models after earnings or data updates while preserving model structure.
- LLM & MCP Connectors: Provides connectors and an HTTP API (used as an MCP/resource in platforms like Claude) so LLMs can query high-quality fundamentals and KPIs with source citations and integrate data into natural-language workflows.
- Large Coverage Universe: Maintains coverage of thousands of public companies (noted as 3,500+ in partner docs), including historical quarter and fiscal-year time series and specialized operational metrics for sector-specific analysis.
- Document-to-Spreadsheet Automation: Converts data from unstructured documents (CIMs, pitchbooks, investor decks) into clean Excel tables and time series to accelerate due diligence and model-building.
- Embeddable Widget & Developer Tools: Offers embeddable demo widgets and developer examples (GitHub repos) to streamline integration into internal apps, portals, or research tools for interactive data access.
- Auditability & Source Linking: Every data point links back to the original filing or document, enabling verification, transparent audit trails, and defensible research outputs.
- Programmatic access to fundamentals, financial statements, and operational KPIs (cited to SEC filings and investor materials)
- Coverage of thousands of public companies (documented as 3,500+ in integrated product docs)
- Model Context Protocol (MCP) / HTTP connector support for integration with LLMs (example: Claude MCP HTTP transport)
- Embeddable widget with demo code (GitHub repo) for web embedding
- Excel integration to push/update data directly into user spreadsheets and models
- Document extraction: parse PDFs, CIMs, investor decks into structured Excel-compatible outputs
- Time series data and quarter-level KPI histories for multi-period analysis
- Server-side authentication token handling recommended for embed/API usage
- Provides audit trails and citation metadata for sourced data
- Non-real-time (post-earnings) data updates; not positioned as intraday real-time feed
Best for
- Automated Model Refreshes: Updating multi-sheet Excel financial models automatically after quarterly earnings releases, preserving formulas and assumptions while refreshing underlying fundamentals.
- LLM-Powered Financial Queries: Connecting Daloopa to LLMs (via MCP or API) so analysts can ask natural-language questions about KPIs, run time-series comparisons, and receive answers with source citations.
- Due Diligence & Document Extraction: Extracting financial schedules and metrics from acquisition CIMs, investor decks, or PDFs into structured spreadsheets to accelerate M&A or credit diligence.
- Peer Benchmarking and Screening: Pulling standardized metrics across a set of 3–10 peer companies to compute relative performance, growth rates, and operational efficiency comparisons for investment memos.
- Portfolio Monitoring & Reporting: Feeding up-to-date fundamentals into portfolio dashboards to monitor positions, calculate valuation metrics (DCF inputs, multiples), and generate audit-ready reports.
- Model Building & Validation: Generating clean starter models (DCF, LBO, comparables) from extracted data and validating assumptions by comparing extracted time series against user models.
- Automate updating and ramping of Excel financial models with authoritative fundamentals
- Enable LLMs to query structured financial data and KPIs with source citations
- Extract structured financial tables from SEC filings and offering documents into Excel
- Benchmark and time-series analysis across peer companies for investment research
- Due diligence workflows that require consolidated, cited company financials
- Generate spreadsheet formulas and rebuild model structure from raw filings
Staats
Staats
Agent-native, cookieless website analytics delivered through MCP, so your coding agent measures deploys and reports results in chat.
Key features
- Native MCP Support: Built on the open Model Context Protocol so Claude Code, Cursor, Windsurf and Codex can query and configure analytics out of the box.
- Autonomous Instrumentation: The agent adds tracking while writing features, needing only one HTML data attribute per button click and no extra JavaScript.
- Ship & Measure: Every deploy is tagged automatically, then before-and-after metrics are compared so you can tell whether a change moved the needle.
- Zero-Cookie Tracker: A ~1.5KB script with no cookies and no IP logging, so no cookie banner is required and tracking works the moment it is dropped in.
- Drop-Off Funnels: Maps visitor journeys from landing page to checkout, pinpoints where users leak out, and suggests which step to fix next.
- Anomaly Alerts: Traffic surges, viral social spikes and referrer anomalies are traced to their source and surfaced with context rather than raw numbers.
- In-Chat Intelligence: Ask about visitors, top referrers and conversions inside your editor chat instead of opening a separate analytics tab.
- Portfolio Overview: One account key covers every side project, letting you compare sites side by side or spin up tracking for a new app from chat.
Best for
- Deploy Verification: Tag a release and have the agent compare traffic and conversion metrics before and after to confirm the change helped.
- Launch Monitoring: Ask the agent how a Product Hunt or Hacker News launch is performing and get referrer-level attribution without opening a dashboard.
- Funnel Debugging: Map a signup or checkout flow, find the step where visitors drop off, and get a concrete suggestion for what to fix.
- Privacy-First Analytics: Replace cookie-based analytics on an EU-facing site with a cookieless tracker that avoids consent banners entirely.
- Indie Portfolio Management: Track a dozen side projects under a single key and compare their traffic side by side from one chat session.
- Agent-Driven Instrumentation: Let a coding agent add click tracking to new features as it writes them, so instrumentation never lags behind the code.