Changelog - 2025-11-26 (#7)

Phase 3: Data Synthesis & Cross-Reference

Overview

Added the Synthesizer module to cross-reference data across all extractors, detect conflicts, identify data gaps, and generate a unified view of company metrics with confidence scores.

New Feature: Dataroom Synthesizer

Purpose

Cross-reference extracted data to identify inconsistencies, missing information, and build a unified, confidence-scored view of company metrics.

Implementation

Created src/agents/dataroom/synthesizer.py (~450 lines):

def synthesize_dataroom(analysis: DataroomAnalysis) -> Dict[str, Any]:
    """Main entry point for dataroom synthesis."""

def detect_conflicts(analysis: DataroomAnalysis) -> List[DataConflict]:
    """Detect conflicts between data extracted from different sources."""

def identify_data_gaps(analysis: DataroomAnalysis) -> List[Dict[str, Any]]:
    """Identify missing critical information for investment analysis."""

def cross_reference_data(analysis: DataroomAnalysis) -> Dict[str, Any]:
    """Build cross-referenced unified view of company data."""

def generate_synthesis_report(...) -> str:
    """Generate a markdown synthesis report."""

Conflict Detection

Checks for inconsistencies between data sources:

Hydden Results

• ARR Conflict (High): $3M (pitch deck) vs $58M (financial projections) - 94.8% diff

• Customer Count (Low): 3 reported vs 4 named

Data Gap Identification

Identifies missing critical data for investment memos:

Critical (High Priority)

• ARR/Revenue

• Monthly Burn Rate

• Runway (months)

• Cap Table / Ownership

• Customer Count

• Current ARR

• Founder Backgrounds

Important (Medium Priority)

• Gross Margin

• Option Pool Size

• Customer Retention Rate

• Competitive Landscape

• Key Differentiators

Hydden Results

• Critical Gaps: Burn rate, Runway

• Medium Gaps: Gross margin, Retention rate

Cross-Reference Engine

Builds unified metrics with confidence scores:

{
  "company_metrics": {
    "arr": 57953000,
    "headcount": 17,
    "customer_count": 3,
    "founder_ownership_pct": 54.8,
    "competitor_count": 8
  },
  "confidence_scores": {
    "arr": "high",
    "headcount": "high",
    "customer_count": "high",
    "founder_ownership_pct": "high",
    "competitor_count": "medium"
  },
  "data_sources": {
    "arr": ["Financial Model"],
    "headcount": ["Team Document"],
    ...
  }
}

Confidence Levels:

• 🟢 High: Primary source available (financial model, cap table)

• 🟡 Medium: Secondary source only (pitch deck, battlecards)

• 🔴 Low/None: No reliable source

Output Structure Update

The dataroom analyzer now outputs 7 numbered artifact files:

output/Company-v0.0.1/
├── 0-dataroom-inventory.json/md    # Document inventory
├── 1-competitive-analysis.json/md  # Competitive landscape
├── 2-cap-table.json/md             # Ownership structure
├── 3-financial-analysis.json/md    # Financial projections
├── 4-traction-analysis.json/md     # Customers & metrics
├── 5-team-analysis.json/md         # Team profiles
└── 6-synthesis-report.json/md      # Cross-reference & gaps

Synthesis Report Format

The markdown report includes:

1. Executive Summary

   • Unified metrics table with confidence indicators

   • Data quality summary (gap/conflict counts)

2. Data Conflicts

   • Each conflict with sources, values, and resolution

   • Severity indicators (🔴 high, 🟡 medium, 🟢 low)

3. Data Gaps

   • Grouped by priority (high, medium, low)

   • Recommendations for filling gaps

4. Cross-Reference Insights

   • Financial & traction alignment check

   • Headcount consistency check

   • Cap table completeness check

Integration Updates

analyze_dataroom() Updated

Now includes Phase 3 synthesis step:

1. Scan documents

2. Classify documents

3. Run extractors

4. Run synthesis (NEW)

5. Save artifacts

save_dataroom_analysis_artifacts() Updated

• Added synthesis_results parameter

• Saves 6-synthesis-report.json with structured data

• Saves 6-synthesis-report.md with human-readable report

Files Changed Summary

New Files (1)

Modified Files (1)

Phase 3 Complete

All Synthesis Features Done

• Conflict detection (ARR, headcount, customers, ownership, team)

• Data gap identification (critical + medium priority)

• Cross-reference engine (unified metrics + confidence)

• Synthesis report generation (markdown + JSON)

Full Hydden Test Results

Complete analysis in 281.0 seconds:

Usage

# Activate virtual environment
source .venv/bin/activate

# Run dataroom analyzer (now includes synthesis)
python -m src.agents.dataroom.analyzer "data/Secure-Inputs/Hydden Dataroom" "Hydden"

# Output includes:
# 🔄 Synthesizing dataroom data...
#    Checking for data conflicts...
#    Found 2 conflicts
#    Identifying data gaps...
#    Found 4 gaps (2 critical)
#    Cross-referencing data sources...
#    Unified 5 metrics
#    Generating synthesis report...

Next Steps: Phase 4 - Memo Integration

• Generate memo section drafts from extracted data

• Map extractor outputs to memo template sections

• Integrate with main investment memo orchestrator

• Auto-populate sections with dataroom findings

Contributors

Implementation by Claude Code (Anthropic) with user guidance.