AKEL — AI Knowledge Extraction Layer

AKEL is FactHarbor’s automated intelligence subsystem.  
Its purpose is to reduce human workload, enhance consistency, and enable scalable knowledge processing — without ever replacing human judgment.

All AKEL outputs are marked with AuthorType = AI and require human approval before publication.

AKEL operates in two modes:

• Single-node mode (POC & Beta 0)
• Federated multi-node mode (Release 1.0+)

Human reviewers, experts, and moderators always retain final authority.

Purpose and Role

AKEL transforms unstructured inputs into structured, review-ready drafts.

Core responsibilities:

• Claim extraction from arbitrary text
• Claim classification (domain, type, evaluability, safety)
• Scenario generation (definitions, boundaries, assumptions, methodology)
• Evidence summarization and metadata extraction
• Contradiction detection
• Re-evaluation proposal generation
• Cross-node embedding exchange (Release 1.0+)

Components

• AKEL Orchestrator – central coordinator
• Claim Extractor
• Claim Classifier
• Scenario Generator
• Evidence Summarizer
• Contradiction Detector
• Embedding Handler (Release 1.0+)
• Federation Sync Adapter (Release 1.0+)

Inputs and Outputs

Inputs

• User-submitted claims or evidence  
• Uploaded documents  
• URLs or citations  
• External LLM API (optional)  
• Embeddings (from local or federated peers)

Outputs (all require human approval)

• ClaimVersion (draft)  
• ScenarioVersion (draft)  
• EvidenceVersion (summary + metadata draft)  
• VerdictVersion (draft; internal only)  
• Contradiction alerts  
• Re-evaluation proposals  
• Updated embeddings

Architecture Overview

Triple-Path Pipeline Architecture

graph TB
    subgraph Input[User Input]
        URL[URL Input]
        TEXT[Text Input]
    end

    subgraph Shared[Shared Modules]
        CONTEXTS[analysis-contexts.ts Context Detection]
        AGG[aggregation.ts Verdict Aggregation]
        CLAIM_D[claim-decomposition.ts]
        EF[evidence-filter.ts ~330 lines]
        QG[quality-gates.ts ~410 lines]
        SR[source-reliability.ts ~620 lines]
        VC[verdict-corrections.ts ~310 lines]
        TS[truth-scale.ts ~280 lines]
        BU[budgets.ts ~250 lines]
    end

    subgraph Dispatch[Pipeline Dispatch]
        SELECT{Select Pipeline}
    end

    subgraph Pipelines[Pipeline Implementations]
        ORCH[Orchestrated Pipeline]
        CANON[Monolithic Canonical]
        DYN[Monolithic Dynamic]
    end

    subgraph LLM[LLM Layer]
        PROVIDER[AI SDK Provider]
    end

    subgraph Output[Result]
        RESULT[AnalysisResult JSON]
        REPORT[Markdown Report]
    end

    URL --> SELECT
    TEXT --> SELECT
    SELECT -->|orchestrated| ORCH
    SELECT -->|monolithic_canonical| CANON
    SELECT -->|monolithic_dynamic| DYN
    CONTEXTS --> ORCH
    CONTEXTS --> CANON
    AGG --> ORCH
    AGG --> CANON
    CLAIM_D --> ORCH
    CLAIM_D --> CANON
    EF --> ORCH
    QG --> ORCH
    SR --> ORCH
    SR --> CANON
    SR --> DYN
    VC --> ORCH
    TS --> CANON
    TS --> DYN
    BU --> ORCH
    BU --> CANON
    BU --> DYN
    ORCH --> PROVIDER
    CANON --> PROVIDER
    DYN --> PROVIDER
    ORCH --> RESULT
    CANON --> RESULT
    DYN --> RESULT
    RESULT --> REPORT

Pipeline Variants

Shared Modules

Orchestrated Pipeline Steps

1. Understand - Detect input type, extract claims, identify dependencies
   2. Research (iterative) - Generate queries, fetch sources, extract evidence
   3. Verdict Generation - Generate claim and article verdicts
   4. Summary - Build two-panel summary
   5. Report - Generate markdown report

Detailed Pipeline Diagrams

For internal implementation details of each pipeline variant:

• Orchestrated Pipeline Internal - 7-step staged workflow (13,300 lines)
• Monolithic Canonical Internal - Single-context canonical output (1,500 lines)
• Monolithic Dynamic Internal - Flexible experimental output (735 lines)

AKEL and Federation

In Release 1.0+, AKEL participates in cross-node knowledge alignment:

• Shares embeddings  
• Exchanges canonicalized claim forms  
• Exchanges scenario templates  
• Sends + receives contradiction alerts  
• Never shares model weights  
• Never overrides local governance

Nodes may choose trust levels for AKEL-related data:

• Trusted nodes: auto-merge embeddings + templates  
• Neutral nodes: require reviewer approval  
• Untrusted nodes: fully manual import

Human Approval Workflow

1. AKEL generates draft outputs (AuthorType = AI)  
   2. Reviewers inspect and approve/moderate the drafts  
   3. Experts validate high-risk or domain-specific outputs  
   4. Moderators finalize publication  
   5. Version numbers increment, history preserved

No AKEL output is ever published automatically.