Data Pipelines & RAG: Turning Fragmented Enterprise Knowledge into an AI-Ready Knowledge Base

The Solution: An Automated Multi-Source Data Pipeline with Production RAG

Spundan designed and deployed a production-grade data ingestion pipeline and RAG architecture that continuously pulls, processes, embeds, and retrieves knowledge across all enterprise sources. Key strategic components included:

1. Multi-Source Data Connectors: Built automated connectors for SharePoint, S3, Confluence, SQL databases, and email archives — continuously ingesting new and updated documents without manual intervention.
2. Intelligent Document Processing: Deployed a multi-format parsing pipeline handling PDFs, DOCX, XLSX, HTML, and scanned images (via OCR) with layout-aware extraction that preserves tables, headers, and section structure.
3. Smart Chunking Strategy: Implemented semantic and hierarchical chunking — splitting documents by meaning rather than fixed token counts — to preserve context across chunk boundaries and improve retrieval accuracy.
4. Embedding & Vector Store: Generated dense embeddings using domain-tuned embedding models and indexed them into a Qdrant vector store, enabling fast, accurate semantic similarity search across millions of document chunks.
5. Hybrid Retrieval: Combined dense vector search with BM25 keyword search in a hybrid retrieval layer, ensuring both semantic relevance and exact-term matching for regulatory and technical queries.
6. RAG Orchestration & Citation: Built a LangChain-based RAG orchestration layer that retrieves the top-k most relevant chunks, constructs grounded prompts, and returns answers with source citations — enabling full auditability of every AI response.
7. Continuous Pipeline Monitoring: Implemented pipeline health dashboards tracking ingestion lag, embedding freshness, retrieval relevance scores, and answer quality metrics to ensure the knowledge base stays current and accurate.

Implementation Steps

The platform was built iteratively, beginning with the highest-value document sources and expanding to full enterprise coverage:

• Knowledge Audit & Source Mapping: Catalogued all 15+ data sources, classified document types, volumes, and update frequencies, and prioritized sources by business criticality for phased onboarding.
• Connector Development: Built and tested custom ingestion connectors for each source system using Apache Airflow-orchestrated DAGs, with incremental sync logic to process only new or modified documents on each run.
• Document Processing Pipeline: Deployed Unstructured.io for multi-format parsing, integrated Azure Form Recognizer for scanned PDFs and complex table extraction, and built a cleaning and normalization layer to standardize text quality.
• Chunking & Embedding: Designed and benchmarked multiple chunking strategies (fixed, sentence, semantic, and hierarchical) against retrieval quality metrics; deployed the winning strategy with sentence-transformers embedding on GPU-backed inference.
• Vector Store Deployment: Deployed Qdrant on Kubernetes with collection namespacing per document category, enabling filtered search by source, date range, document type, and regulatory domain.
• RAG Chain Construction: Built the full RAG chain — query rewriting, hybrid retrieval, context assembly, LLM generation, and citation formatting — with configurable retrieval parameters tunable per use case.
• Evaluation & Quality Tuning: Used RAGAS framework to evaluate retrieval precision, answer faithfulness, and context relevance; iteratively tuned chunk size, overlap, top-k, and reranking strategy to maximize scores.
• Deployment & User Rollout: Launched a chat interface integrated into the internal portal, onboarded research and compliance teams with guided demos, and deployed pipeline monitoring dashboards for the data engineering team.

Results

The Data Pipelines & RAG platform delivered a step-change in how the organization accesses, queries, and acts on its internal knowledge:

• Faster Knowledge Retrieval: Average time to locate and retrieve relevant documents dropped from 3–4 hours to under 30 seconds for the majority of research and compliance queries.
• Retrieval Accuracy: Hybrid retrieval achieved 89% answer faithfulness and 92% context precision on the RAGAS evaluation benchmark against the pharmaceutical domain test set.
• Knowledge Base Coverage: Successfully ingested and indexed over 2.3 million document chunks from all 15 source systems within the first 10 weeks of deployment.
• Always-Current Data: Automated incremental ingestion pipelines keep the knowledge base updated within under 4 hours of any document creation or update across all connected sources.
• Researcher Productivity: Research teams reported saving an average of 2.5 hours per day previously spent on manual document search and cross-referencing.
• Audit Response Time: Compliance teams reduced regulatory audit response preparation time by 65%, with full citation trails enabling instant verification of every AI-generated answer.
• Hallucination Eliminated: Grounded RAG responses with source citations reduced unverifiable AI hallucinations to near zero on internal knowledge queries, compared to 40%+ hallucination rate from vanilla LLM without retrieval.