E

Evidence

Data Infrastructure and Analytics

Data & AnalyticsNo-CodeOpen SourceDatabaseMarkdown
Function:Product & Engineering
Subfunction:Data & Analytics
Loading versions...
Founded
2021
Employees
6 employees
Funding
$2.1M
Stage
Seed Stage
Report version: Oct 20, 2025

1. Products/Services & Features

  • Main Offerings:

    • Open-source code-based BI platform
    • Evidence Cloud (managed hosting service)
    • SQL and Markdown-based dashboard builder
  • Feature Breakdown: SQL query editor, Markdown-based reporting, GitHub integration, Row-level security, Custom charts, Data source connectors, Version control support, Two-way GitHub sync, Reusable SQL models (Departments: Product & Engineering, Data & Analytics, Growth/Marketing)

  • Business Industry Gearing: High - Targets technical data teams and analytics engineers

2. Security & Compliance

  • Certifications: SOC2 Compliant, MIT License (Open Source)

  • Vendors/Tools: Not specified

  • Risk Profile:

    • Breaches: No known breaches disclosed
    • Features: Data encryption at rest and in transit, Access controls, IP whitelisting, Activity logging and audit trails

3. User Feedback & Adoption

  • Aggregated Reviews: Generally positive on Capterra for social proof tool (Note: Search results conflated Evidence BI with Evidence social proof tool)

    • Pros: Code-based approach appeals to technical teams, Open source flexibility, Fast iteration and deployment, GitHub integration, Customizable charts and styling
    • Cons: Steep learning curve for non-technical users, Limited drag-and-drop interface, Requires SQL knowledge, Small team may limit support resources
  • Adoption Insights:

    • Adoption Ease: Moderate - Requires SQL and Markdown knowledge; easier for technical teams, harder for non-technical users
    • Adoption Cultural Fit: High for data-forward organizations with technical analytics teams; lower for traditional BI-focused enterprises
  • Metrics:

  • Barriers: Requires SQL proficiency, Learning curve for Markdown-based reporting, Limited enterprise support features, Small company size

4. Monetization & Business Model

  • Revenue Model: Freemium (Open Source + Paid Cloud Hosting)

  • Pricing: Free (self-hosted), Evidence Cloud (pricing not publicly disclosed) (Sources: https://evidence.dev/pricing)

  • Market Context:

    • TAM: Global BI market estimated at $20B+; code-based BI segment growing
    • Growth Stage: Early Growth - Emerging segment within BI market

5. Leadership & Recent Developments

Name Description LinkedIn X Account
Sean Hughes Co-founder and COO at Evidence. Former Director and Analyst at Birch Hill Equity Partners. Led data science team at major Canadian private equity fund. https://ca.linkedin.com/in/hughessean
Archie Sarre Wood Head of Growth at Evidence. Former BI Team Manager & Chief of Staff at Patch Plants. Strategy background from OC&C Strategy Consultants. Handles customer success, social media, docs, sales, and product. https://ca.linkedin.com/in/archiesarrewood
Adam McAskill Co-founder at Evidence. Previously led data science team at Birch Hill Equity Partners alongside Sean Hughes.
  • Key Metrics Update:

    • Funding: Seed Round: $2.1M (September 2023)
    • Employee Growth: 6 employees (as of latest data)
  • News/Trends:

    • News Launch: Evidence Cloud launched Fall 2024
    • News Partnerships: Integrations with MotherDuck, dbt, DuckDB, GitHub
    • News Funding: $2.1M Seed Round (September 2023) from Y Combinator, A.Capital Ventures, and other investors
    • News Challenges: Competing with established BI vendors (Tableau, Looker, Mode); building enterprise features while maintaining open-source ethos

6. Target Audience & Use Cases

  • Target Market: Technical data teams, Analytics engineers, Data analysts, Product-focused developers, Mid-sized to large organizations with modern data stacks

  • Target Users & Personas: Data analysts, Analytics engineers, BI teams, Technical product managers, Data-focused developers

  • User Experience Level: Intermediate to Advanced (requires SQL and coding knowledge)

  • Key Use Cases:

    • Building version-controlled, reproducible data dashboards and reports
    • Creating self-serve analytics tools for business stakeholders
    • Embedding analytical reporting in custom applications and workflows

7. Impact & Recommendations

  • Measurable Outcomes:

    • Workflow Improvements: Faster dashboard iteration through code-based approach, Version control for analytics, Reduced time to insight, Better collaboration through GitHub integration
    • ROI Examples: Reduced dashboard maintenance time, Faster time-to-insight, Improved data product quality through code review processes
  • Fit Assessment: Excellent fit for technical organizations with strong data engineering practices; poor fit for non-technical BI teams

  • Custom Rec Flags:

    • Priority ICP: Mid-market to enterprise tech companies with dedicated analytics engineering teams
    • Short Term Goals: Expand Evidence Cloud adoption, Build enterprise features, Grow community contributions

8. Data Sourcing Notes

Need help evaluating and implementing AI tools?

ChiriBrain orchestrates your entire AI stack — connecting tools, teams, and workflows into one governed platform.