Skip to content

Latest commit

 

History

History
129 lines (102 loc) · 3.71 KB

instructions.md

File metadata and controls

129 lines (102 loc) · 3.71 KB
Raw

AI Enterprise Case Study Analyzer

Build a Python application using Claude 3.5 Sonnet API to analyze and generate structured reports on enterprise AI adoption case studies.

INPUT REQUIREMENTS

  • CSV file containing URLs of potential AI case studies
  • The system should analyze and filter for enterprise-specific AI implementation cases
  • Whenever it finds an AI case study that is related with enterprise AI, it should be saved for further processing.

ANALYSIS CRITERIA

Enterprise Case Detection:

  • Must involve established companies (not startups)
  • Focus on business AI implementation
  • Clear business outcomes and metrics
  • Enterprise-scale deployment

Content Requirements:

  • AI/ML technology implementation details
  • Enterprise integration aspects
  • Business process transformation
  • ROI or business impact metrics
  • Change management approach

PROCESSING WORKFLOW

Phase 1: Case Study Filtering & Collection

  1. For each URL in CSV:
    • Scrape content using web_loader
    • Analyze with Claude to determine if it's an enterprise AI case study
    • Filter out non-enterprise or non-AI cases
    • Save qualified content for further processing

Phase 2: Detailed Analysis

For each qualified case study:

  1. Extract and analyze six key sections:

    • Company Context & AI Strategy
    • Business Challenge & Opportunity
    • AI Solution Architecture
    • Implementation & Integration
    • Change Management & Adoption
    • Business Impact & Lessons

  2. Generate structured insights:

    • AI technologies used
    • Integration patterns
    • Success metrics
    • Implementation challenges
    • Best practices identified

Phase 3: Report Generation

Generate three types of reports:

  1. Individual Case Study Reports (PDF)
  2. Cross-Case Analysis Report (PDF)
  3. Executive Insights Dashboard (JSON)

TECHNICAL SPECIFICATIONS

Claude Configuration:

  • Model: claude-3-5-sonnet-20241022
  • Temperature: 0.2
  • Output Context Window: 8192 tokens

Processing Pipeline:

  1. Web Content Extraction
  2. Enterprise AI Case Validation
  3. Structured Analysis
  4. Report Generation
  5. Cross-Case Pattern Analysis

OUTPUT REQUIREMENTS

Each qualified case study generates:

  1. Validation Report:

    • Enterprise AI qualification criteria met
    • Data quality assessment
    • Content completeness check

  2. Detailed Analysis Report:

    • Executive Summary
    • AI Strategy Analysis
    • Technical Implementation Details
    • Business Impact Assessment
    • Key Success Factors
    • Lessons Learned

  3. Cross-Case Insights:

    • Common patterns
    • Success factors
    • Implementation challenges
    • Technology trends
    • ROI patterns

ERROR HANDLING & LOGGING

Track and log:

  • Case study qualification decisions
  • Content extraction issues
  • Analysis completeness
  • Pattern detection confidence
  • Processing errors and retries

VALIDATION CRITERIA

Enterprise AI Case Validation:

  • Company size/maturity check
  • AI implementation scope
  • Business process integration
  • Measurable outcomes
  • Enterprise-scale considerations

Report Quality Validation:

  • Content completeness
  • Technical accuracy
  • Business impact quantification
  • Implementation detail sufficiency
  • Cross-reference verification

The system should focus on creating high-quality, business-focused analysis of enterprise AI implementations, highlighting patterns, best practices, and lessons learned across cases.

1-) Firecrawl extracts the content 2-) The text is sent to Claude 3-) Claude responses and IF it's an enterprise case study, creates an executive report. If not, passes and informs the user via terminal (print) 4-) The report is saved in the reports/individual folder 5-) The report is saved in the reports/cross_case_analysis folder 6-) The report is saved in the reports/executive_dashboard.json folder