RAG Platform

The Challenge

Enterprise teams deploying off-the-shelf generative AI models struggle with hallucinated citations, lack of source attribution, stale knowledge indexing, and poor handling of tabular or semi-structured data in PDFs and quarterly reports. Without deterministic evaluation frameworks and granular source tracking, mission-critical decisions in legal, finance, and technical operations cannot rely on generated outputs.

System Architecture & Design

A production-grade retrieval pipeline utilizing layout-aware document parsers, hierarchical parent-child chunking, pgvector HNSW indexing, reciprocal rank fusion (dense vector + sparse BM25), and cross-encoder reranking to ensure optimal context retrieval for generative inference.

[Ingestion Pipeline: PDFs, HTML, Docs, DBs]
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    [Layout-Aware Parser & Table Extractor]
                     │
     (Hierarchical Parent-Child Chunking)
                     │
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           [PostgreSQL + pgvector]
                     │
     ┌───────────────┴───────────────┐
     ▼                               ▼
[Dense Vector Embeddings]     [Sparse BM25 Index]
     │                               │
     └───────────────┬───────────────┘
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     [Reciprocal Rank Fusion (RRF)]
                     │
                     ▼
         [Cross-Encoder Reranker]
                     │
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   [Dynamic Context Window Optimization]
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  [Generation with Token Source Attribution]
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        [Automated Faithfulness Eval]

Implementation Details

Document ingestion extracts markdown-formatted tables and headers to preserve structural context. Hierarchical parent-child indexing stores small 256-token chunks for vector similarity precision while providing 1024-token parent contexts to the LLM for coherent answering. Dense embeddings from OpenAI text-embedding-3-large are indexed alongside BM25 tokens, merged through Reciprocal Rank Fusion, and passed through a cross-encoder reranker. Output synthesis enforces token-level attribution, and automated test suites monitor retrieval precision and faithfulness metrics continuously.

Our Engineering Approach

We build a modular retrieval layer that indexes your complex document repositories, selects the exact relevant excerpts for each question, and returns answers accompanied by transparent source citations and direct page references.

Operational & Business Impact

Teams leverage AI for rigorous research and decision support while verifying the underlying evidence themselves. It establishes consistency across operations, customer success, and leadership reporting with verifiable proof for every statement.