POC Summary

# FactHarbor - Complete Analysis Summary
Consolidated Document - No Timelines
Date: December 19, 2025

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 1. POC Specification - DEFINITIVE

# POC Goal
Prove that AI can extract claims and determine verdicts automatically without human intervention.

# POC Output (4 Components Only)

• • 1. ANALYSIS SUMMARY
       - 3-5 sentences
       - How many claims found
       - Distribution of verdicts  
       - Overall assessment

2. CLAIMS IDENTIFICATION
- 3-5 numbered factual claims
- Extracted automatically by AI

3. CLAIMS VERDICTS
- Per claim: Verdict label + Confidence % + Brief reasoning (1-3 sentences)
- Verdict labels: WELL-SUPPORTED / PARTIALLY SUPPORTED / UNCERTAIN / REFUTED

4. ARTICLE SUMMARY (optional)
- 3-5 sentences
- Neutral summary of article content

Total output: 200-300 words

# What's NOT in POC

❌ Scenarios (multiple interpretations)  
❌ Evidence display (supporting/opposing lists)  
❌ Source links  
❌ Detailed reasoning chains  
❌ User accounts, history, search  
❌ Browser extensions, API  
❌ Accessibility, multilingual, mobile  
❌ Export, sharing features  
❌ Any other features

# Critical Requirement

FULLY AUTOMATED - NO MANUAL EDITING

This is non-negotiable. POC tests whether AI can do this without human intervention.

# POC Success Criteria

Passes if:
- ✅ AI extracts 3-5 factual claims automatically
- ✅ AI provides reasonable verdicts (≥70% make sense)
- ✅ Output is comprehensible
- ✅ Team agrees approach has merit
- ✅ Minimal or no manual editing needed

Fails if:
- ❌ Claim extraction poor (< 60% accuracy)
- ❌ Verdicts nonsensical (< 60% reasonable)
- ❌ Requires manual editing for most analyses (> 50%)
- ❌ Team loses confidence in approach

# POC Architecture

Frontend: Simple input form + results display  
Backend: Single API call to Claude (Sonnet 4.5)  
Processing: One prompt generates complete analysis  
Database: None required (stateless)

# POC Philosophy

 "Build less, learn more, decide faster. Test the hardest part first."

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 2. Gap Analysis - Strategic Framework

# Framework Definition

Importance = f(risk, impact, strategy)
- Risk: What breaks if we don't have this?
- Impact: How many users? How severe?
- Strategy: Does it advance FactHarbor's mission?

Urgency = f(fail fast and learn, legal, promises made)
- Fail fast: Do we need to test assumptions?
- Legal: External requirements/deadlines?
- Promises: Commitments to stakeholders?

# 18 Gaps Identified

Category 1: Accessibility & Inclusivity

1. WCAG 2.1 Compliance
   2. Multilingual Support

Category 2: Platform Integration
3. Browser Extensions
4. Embeddable Widgets
5. ClaimReview Schema

Category 3: Media Verification
6. Image/Video/Audio Verification

Category 4: Mobile & Offline
7. Mobile Apps / PWA
8. Offline Access

Category 5: Education & Media Literacy
9. Educational Resources
10. Media Literacy Integration

Category 6: Collaboration & Community

1. 1. Professional Collaboration Tools
      12. Community Discussion

Category 7: Export & Sharing
13. Export Capabilities (PDF, CSV)
14. Social Sharing Optimization

Category 8: Advanced Features
15. User Analytics
16. Personalization
17. Media Archiving
18. Advanced Search

# Importance/Urgency Analysis

VERY HIGH Importance + HIGH Urgency:

1. Accessibility (WCAG)
      - Risk: Legal liability, 15-20% users excluded
      - Urgency: European Accessibility Act (June 28, 2025)
      - Action: Must be built from start (retrofitting 100x more expensive)

2. Educational Resources
   - Risk: Platform fails if users can't understand
   - Urgency: Required for any adoption
   - Action: Basic onboarding essential

HIGH Importance + MEDIUM Urgency:
3. Browser Extensions - Standard user expectation, test demand first
4. Media Verification - Cannot address visual misinformation without it
5. Multilingual - Global mission requires it, plan early

HIGH Importance + LOW Urgency:
6. Mobile Apps - 90%+ users on mobile, but web-first viable
7. ClaimReview Schema - SEO/discoverability, can add anytime

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 1.7 POC Alignment with Full Specification

# POC Intentional Simplifications

POC1 tests core AI capability, not full architecture:

What POC Tests:
- Can AI extract claims from articles?
- Can AI evaluate claims with reasonable verdicts?
- Is fully automated approach viable?
- Is output comprehensible to users?

What POC Excludes (Intentionally):
- ❌ Scenarios (deferred to POC2 - open architectural questions remain)
- ❌ Evidence display (deferred to POC2)
- ❌ Multi-component AKEL pipeline (simplified to single API call)
- ❌ Quality gate infrastructure (simplified basic checks)
- ❌ Production data model (stateless POC)
- ❌ Review workflow system (no review queue)

Why Simplified:
- Fail fast: Test hardest part first (AI capability)
- Learn before building: POC1 informs architecture decisions
- Iterative: Add complexity based on POC1 learnings
- Risk management: Prove concept before major investment

# Full System Architecture (Future)

Workflow:
Claims → Scenarios → Evidence → Verdicts

AKEL Components:
- Orchestrator
- Claim Extractor & Classifier
- Scenario Generator
- Evidence Summarizer
- Contradiction Detector
- Quality Gate Validator
- Audit Sampling Scheduler

Publication Modes:
- Mode 1: Draft-Only
- Mode 2: AI-Generated (POC uses this)
- Mode 3: AKEL-Generated (Human-Reviewed)

# POC vs. Full System Summary

# Gap Between POC and Beta

Significant architectural expansion needed:

1. Scenario generation component design and implementation
   2. Evidence Model full structure
   3. Multi-phase workflow with gates
   4. Component-based AKEL architecture
   5. Production data model and storage
   6. Review workflow and audit systems

POC proves concept. Beta builds product.

MEDIUM Importance + LOW Urgency:
8-14. All other features - valuable but not urgent

Strategic Decisions Needed:
- Community discussion: Allow or stay evidence-focused?
- Personalization: How much without filter bubbles?
- Media verification: Partner with existing tools or build?

# Key Insight: Milestones Change Priorities

POC: Only educational resources urgent (basic explainer)  
Beta: Accessibility becomes urgent (test with diverse users)  
Release: Legal requirements become critical (WCAG, GDPR)

Importance/urgency are contextual, not absolute.

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 3. Key Strategic Recommendations

# Immediate Actions

For POC:

1. Focus on core functionality only (claims + verdicts)
   2. Create basic explainer (1 page)
   3. Test AI quality without manual editing
   4. Make GO/NO-GO decision

Planning:

1. Define accessibility strategy (when to build)
   2. Decide on multilingual priorities (which languages first)
   3. Research media verification options (partner vs build)
   4. Evaluate browser extension approach

# Testing Strategy

POC Tests: Can AI do this without humans?  
Beta Tests: What do users need? What works? What doesn't?  
Release Tests: Is it production-ready?

Key Principle: Test assumptions before building features.

# Build Sequence (Priority Order)

Must Build:

1. Core analysis (claims + verdicts) ← POC
   2. Educational resources (basic → comprehensive)
   3. Accessibility (WCAG 2.1 AA) ← Legal requirement

Should Build (Validate First):
4. Browser extensions ← Test demand
5. Media verification ← Pilot with existing tools
6. Multilingual ← Start with 2-3 languages

Can Build Later:
7. Mobile apps ← PWA first
8. ClaimReview schema ← After content library
9. Export features ← Based on user requests
10. Everything else ← Based on validation

# Decision Framework

For each feature, ask:

1. Importance: Risk + Impact + Strategy alignment?
   2. Urgency: Fail fast + Legal + Promises?
   3. Validation: Do we know users want this?
   4. Priority: When should we build it?

Don't build anything without answering these questions.

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 4. Critical Principles

# Automation First
- AI makes content decisions
- Humans improve algorithms
- Scale through code, not people

# Fail Fast
- Test assumptions quickly
- Don't build unvalidated features
- Accept that experiments may fail
- Learn from failures

# Evidence Over Authority
- Transparent reasoning visible
- No single "true/false" verdicts
- Multiple scenarios shown
- Assumptions made explicit

# User Focus
- Serve users' needs first
- Build what's actually useful
- Don't build what's just "cool"
- Measure and iterate

# Honest Assessment
- Don't cherry-pick examples
- Document failures openly
- Accept limitations
- No overpromising

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 5. POC Decision Gate

# After POC, Choose:

GO (Proceed to Beta):
- AI quality ≥70% without editing
- Approach validated
- Team confident
- Clear path to improvement

NO-GO (Pivot or Stop):
- AI quality < 60%
- Requires manual editing for most
- Fundamental flaws identified
- Not feasible with current technology

ITERATE (Improve & Retry):
- Concept has merit
- Specific improvements identified
- Addressable with better prompts
- Test again after changes

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 6. Key Risks & Mitigations

# Risk 1: AI Quality Not Good Enough
Mitigation: Extensive prompt testing, use best models  
Acceptance: POC might fail - that's what testing reveals

# Risk 2: Users Don't Understand Output
Mitigation: Create clear explainer, test with real users  
Acceptance: Iterate on explanation until comprehensible

# Risk 3: Approach Doesn't Scale
Mitigation: Start simple, add complexity only when proven  
Acceptance: POC proves concept, beta proves scale

# Risk 4: Legal/Compliance Issues
Mitigation: Plan accessibility early, consult legal experts  
Acceptance: Can't launch publicly without compliance

# Risk 5: Feature Creep
Mitigation: Strict scope discipline, say NO to additions  
Acceptance: POC is minimal by design

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 7. Success Metrics

# POC Success
- AI output quality ≥70%
- Manual editing needed < 30% of time
- Team confidence: High
- Decision: GO to beta

# Platform Success (Later)
- User comprehension ≥80%
- Return user rate ≥30%
- Flag rate (user corrections) < 10%
- Processing time < 30 seconds
- Error rate < 1%

# Mission Success (Long-term)
- Users make better-informed decisions
- Misinformation spread reduced
- Public discourse improves
- Trust in evidence increases

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 8. What Makes FactHarbor Different

# Not Traditional Fact-Checking
- ❌ No simple "true/false" verdicts
- ✅ Multiple scenarios with context
- ✅ Transparent reasoning chains
- ✅ Explicit assumptions shown

# Not AI Chatbot
- ❌ Not conversational
- ✅ Structured Evidence Models
- ✅ Reproducible analysis
- ✅ Verifiable sources

# Not Just Automation
- ❌ Not replacing human judgment
- ✅ Augmenting human reasoning
- ✅ Making process transparent
- ✅ Enabling informed decisions

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 9. Core Philosophy

Three Pillars:

• • 1. Scenarios Over Verdicts
       - Show multiple interpretations
       - Make context explicit
       - Acknowledge uncertainty
       - Avoid false certainty

2. Transparency Over Authority
- Show reasoning, not just conclusions
- Make assumptions explicit
- Link to evidence
- Enable verification

3. Evidence Over Opinions
- Ground claims in sources
- Show supporting AND opposing evidence
- Evaluate source quality
- Avoid cherry-picking

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 10. Next Actions

# Immediate
□ Review this consolidated summary  
□ Confirm POC scope agreement  
□ Make strategic decisions on key questions  
□ Begin POC development  

# Strategic Planning
□ Define accessibility approach  
□ Select initial languages for multilingual  
□ Research media verification partners  
□ Evaluate browser extension frameworks  

# Continuous
□ Test assumptions before building  
□ Measure everything  
□ Learn from failures  
□ Stay focused on mission  

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 Summary of Summaries

POC Goal: Prove AI can do this automatically  
POC Scope: 4 simple components, 200-300 words  
POC Critical: Fully automated, no manual editing  
POC Success: ≥70% quality without human correction  

Gap Analysis: 18 gaps identified, 2 critical (Accessibility + Education)  
Framework: Importance (risk + impact + strategy) + Urgency (fail fast + legal + promises)  
Key Insight: Context matters - urgency changes with milestones  

Strategy: Test first, build second. Fail fast. Stay focused.  
Philosophy: Scenarios, transparency, evidence. No false certainty.  

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 Document Status

This document supersedes all previous analysis documents.

All gap analysis, POC specifications, and strategic frameworks are consolidated here without timeline references.

For detailed specifications, refer to:
- User Needs document (in project knowledge)
- Requirements document (in project knowledge)
- This summary (comprehensive overview)

Previous documents are archived for reference but this is the authoritative summary.

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End of Consolidated Summary