Building a Production-Ready GenAI Framework for Document Intelligence

AI Summary

Details how to build a multi-layered document AI system that combines RAG, LexRank, and vector databases for accurate information retrieval
Provides complete Python implementations for hierarchical chunking, citation tracking, and fact verification
Explains why basic AI solutions fail for serious document work and how to engineer around those limitations
Introduces workflow orchestration concepts and hints at upcoming n8n pipeline integration for enterprise deployment

When Copilot Meets Reality (And Reality Wins)

Recently, a team of data architects tackled thousands of regulatory documents for a rate case using GitHub Copilot and some basic RAG techniques. The idea was solid - use AI to extract key information from decades of PUC filings, case law, and regulatory precedents. The execution? Let’s just say we had to provide significant support to help them build a more robust solution.

The problem wasn’t the ambition. It was the approach. Copilot excels at generating boilerplate code, but building document intelligence systems requires understanding the subtle interplay between retrieval strategies, summarization techniques, and domain-specific accuracy requirements. You can’t just prompt your way to production-ready infrastructure.

I don’t get to put my fingers on the keys much at work anymore, so I wanted to take a stab at solving this problem properly in my personal time. This is the approach I came up with - a modular, robust framework that handles real enterprise document complexity without the hallucinations and citation disasters that plague most AI document tools.

Here’s the complete technical implementation, along with some thoughts on where workflow orchestration tools like n8n fit into this picture.

Why Standard RAG Falls Apart Under Pressure

Before diving into the solution, let’s be clear about why simple Retrieval Augmented Generation doesn’t cut it for serious document work:

The Citation Nightmare: Basic RAG systems lose track of sources during the retrieval-generation process. When a regulatory attorney asks where specific information came from, “the AI said so” isn’t an acceptable answer.

Context Fragmentation: Real questions often require synthesizing information across multiple documents, sections, and time periods. Standard chunking strategies miss these connections entirely.

Authority Blind Spots: Not all documents carry equal weight. A Federal Register entry should override a random industry blog post, but vanilla vector similarity doesn’t understand that hierarchy.

Precision vs. Paraphrasing: In domains like utilities regulation, exact wording matters. AI-generated summaries can subtly change meaning in ways that create compliance issues.

The solution isn’t abandoning AI - it’s engineering systems that handle these complexities systematically.

Core Architecture: Beyond Simple RAG

The foundation of any robust document AI system starts with how you structure and process the content. Most implementations get this wrong by treating all text chunks equally and losing critical context in the process.

The hierarchical processing approach preserves document structure through multi-level chunking:

Key Innovation: Instead of chopping documents into random pieces, create multiple levels - document, section, paragraph, and sentence. This lets the system zoom in and out as needed while maintaining proper attribution.

@dataclass
class DocumentChunk:
    text: str
    embedding: np.ndarray
    document_id: str
    chunk_id: str
    chunk_type: str  # 'document', 'section', 'paragraph'
    metadata: Dict
    parent_chunk_id: Optional[str] = None
    authority_score: float = 0.0

The authority scoring is crucial here. Not all documents carry equal weight:

authority_weights = {
    'federal_register': 1.0,
    'puc_filing': 0.9,
    'court_decision': 0.85,
    'regulatory_guidance': 0.8,
    'industry_standard': 0.6,
    'company_document': 0.4,
    'blog_post': 0.1
}

This simple weighting system means a Federal Register entry will always outrank a blog post, even if the blog post has higher semantic similarity to the query.

Multi-Stage Retrieval: Where the Magic Happens

Simple vector similarity isn’t enough for enterprise document work. You need a retrieval system that understands authority, context, and the nuances of how information connects across documents.

The multi-stage approach works like this:

Query Expansion: Add domain-specific terms automatically
Initial Retrieval: Vector similarity across all chunks
Authority Weighting: Boost results from reliable sources
Multi-Signal Re-ranking: Combine similarity, keyword overlap, and context coherence
Context Expansion: Add parent sections for complete picture

Here’s the interesting part - the authority weighting:

# Authority-weighted score
authority_boost = 0.7 + 0.3 * chunk.authority_score
boosted_score = similarity_score * authority_boost

This means a Federal Register document (authority score 1.0) gets a 100% boost to its similarity score, while a blog post (authority score 0.1) gets only a 73% boost. The math ensures authoritative sources rise to the top even when semantic similarity is close.

The re-ranking stage combines multiple signals:

final_score = (
    authority_weighted_score * 0.6 +
    keyword_overlap_score * 0.3 +
    context_coherence_score * 0.1
)

Context coherence is subtle but powerful - it gives preference to chunks that come from the same document as other high-scoring results. This helps maintain topical consistency in the final answer.

LexRank: The Secret Weapon for Precise Summarization

One of the biggest problems with generated summaries is that they can subtly change meaning, especially in regulatory contexts where precise wording matters. LexRank solves this by extracting actual sentences from documents rather than generating new text.

The algorithm treats sentences like a network graph, finding the most “central” ones that best represent the core information:

# Create similarity matrix between sentences
similarity_matrix = cosine_similarity(sentence_embeddings)

# Apply threshold to create graph structure  
similarity_matrix[similarity_matrix < threshold] = 0

# Calculate LexRank scores (like PageRank for sentences)
scores = calculate_lexrank_scores(similarity_matrix)

Why this matters: Instead of paraphrasing “utilities shall calculate depreciation using the straight-line method,” LexRank extracts that exact sentence. In regulated industries, the difference between “shall” and “should” can be legally significant.

The power iteration method finds sentences that are:

Similar to many other sentences (high connectivity)
Connected to other important sentences (centrality)
Representative of the document’s core themes

This gives you the most important information in the exact words from the source documents.

Fact Verification: The Trust-But-Verify Layer

The final critical piece is verification. In regulated industries, you can’t just trust that the AI got it right. Every claim needs to be traceable back to source material, and contradictions need to be flagged immediately.

The verification engine extracts factual claims and cross-references them against source chunks:

def _is_factual_claim(self, sentence: str) -> bool:
    factual_indicators = [
        'according to', 'states that', 'requires', 'specifies',
        'shall', 'must', 'defined as', 'means', 'includes'
    ]
    return any(indicator in sentence.lower() for indicator in factual_indicators)

For each claim, it:

Finds the most similar source chunks
Checks for supporting evidence through keyword overlap
Flags potential contradictions using negation patterns
Calculates confidence scores based on evidence quality

The killer feature: Contradiction detection. If the system finds phrases like “however,” “but,” “except,” or explicit negations near similar content, it flags the response for human review.

This creates an audit trail that regulatory teams actually need:

return {
    'claim': "Electric utilities shall calculate depreciation...",
    'supported': True,
    'confidence': 0.92,
    'supporting_sources': ['puc_order_2024_001_sec_1_para_0'],
    'contradicting_sources': []
}

Any response with contradictions gets a confidence score near zero, regardless of how good the retrieval was.

Putting It All Together: Production Architecture

The complete system orchestrates all these components through clean interfaces. Here’s what makes it production-ready:

Modular Design: Each component can be swapped independently. Want to try a different embedding model? Change one line. Need custom citation formats? Modify just the citation generator.

Comprehensive Output: Every query returns not just an answer, but confidence scores, source summaries, citation maps, and verification results:

{
    'query': "What are the depreciation requirements?",
    'system_confidence': 0.87,
    'key_findings': [
        {
            'text': "Electric utilities shall calculate depreciation using the straight-line method",
            'confidence': 0.94,
            'source_id': 'puc_order_2024_001_sec_1_para_0'
        }
    ],
    'source_summary': [...],
    'detailed_citations': {...},
    'verification': {
        'overall_confidence': 0.89,
        'flags': []
    }
}

Authority-First Design: The system prioritizes reliable sources over semantic similarity, which is crucial for regulatory compliance.

Full Audit Trail: Every piece of information can be traced back to its exact source with proper citations.

The complete implementation is available on GitHub with examples, documentation, and customization guides for different domains.

Looking Ahead: Workflow Orchestration with n8n

While this system handles the core document intelligence capabilities beautifully, enterprise deployment requires thinking beyond just the AI components. That’s where workflow orchestration becomes critical, and it’s why I’ve been diving deep into n8n lately.

The potential for integrating document AI pipelines into broader automation workflows is compelling. Imagine triggering document processing automatically when new regulatory filings appear in an SFTP directory, routing different document types through specialized processing pipelines based on their metadata, and automatically notifying stakeholders when high-confidence answers are found for standing regulatory queries.

The modular architecture I’ve outlined here integrates perfectly with n8n’s node-based workflow design. Each component - document ingestion, hierarchical chunking, multi-stage retrieval, LexRank summarization - can become a custom node in a larger automation ecosystem.

Some workflow patterns I’m exploring:

Document intake automation: Monitor multiple sources (email attachments, SFTP, SharePoint) and route documents through appropriate processing pipelines
Continuous monitoring: Set up standing queries that automatically process new documents as they arrive, flagging significant changes in regulatory guidance
Quality assurance workflows: Automatically route low-confidence responses to human reviewers while letting high-confidence answers flow directly to requestors
Compliance reporting: Generate automated summaries when new regulations affect existing policies or procedures

I’ll be diving deeper into n8n integration patterns in an upcoming post, including custom node development for document AI components and enterprise deployment strategies. The combination of robust document intelligence with sophisticated workflow orchestration opens up possibilities that go far beyond simple question-answering systems.

The Bottom Line

Building document AI that works in enterprise environments requires going far beyond basic RAG implementations. The approach I’ve outlined here - hierarchical chunking, multi-stage retrieval, extractive summarization, and comprehensive fact verification - addresses the core challenges that cause simpler systems to fail when accuracy matters.

Key insights from building this across multiple regulated domains:

Authority trumps similarity: Your retrieval system needs to understand source reliability and regulatory hierarchy, not just semantic similarity.

Citations aren’t optional: Every piece of information needs a verifiable source with proper formatting for your domain’s standards.

Verification beats generation: When precision matters, extractive approaches often outperform generative ones for preserving exact meaning.

Modularity enables evolution: Building each component with clean interfaces allows you to improve individual pieces without rebuilding everything.

Context preservation is critical: The hierarchical chunking approach maintains document structure relationships that flat chunking destroys.

This isn’t the fanciest AI system you could build, but it’s one that actually works when the stakes are high. And in regulated industries like utilities, healthcare, or finance - that’s the only kind worth building.

The framework provides a solid foundation that you can adapt to your specific domain by adjusting the authority scoring, citation formats, and verification criteria. Whether you’re dealing with legal documents, medical literature, or financial regulations, the core principles remain the same: preserve context, track sources, verify claims, and never trust the AI to get it right without checking.