Daloopa vs OnBrand by SlideSpeak: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Daloopa and OnBrand by SlideSpeak — 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
OnBrand by SlideSpeak
SlideSpeak
Brand MCP from SlideSpeak that gives AI agents your design context to create on-brand slides directly inside Claude.
Key features
- Brand MCP Server: Expose your brand and design context to AI agents over the Model Context Protocol
- On-Brand Slide Generation: Create slides that follow company styling and themes directly in Claude
- Claude Integration: Generate presentations inside the Claude chat workflow
- Powered by SlideSpeak: Built on SlideSpeak's AI presentation generation engine
- Document-to-Slides: Underlying platform turns prompts, PDFs, Word, Excel, and websites into slides
- API Access: SlideSpeak API integrates AI presentation generation into other applications
Best for
- A team generates on-brand presentations directly in Claude without leaving the chat
- A designer enforces brand styling on AI-generated slides via the Brand MCP
- A developer connects OnBrand to an AI agent so it produces branded decks automatically
- A marketer creates consistent, on-brand slides from a document inside Claude