Service
RAG Pipeline
Ask a question across 10,000 documents. Get a precise answer in seconds, with exact sources.
Overview
Retrieval-Augmented Generation connects your AI to your actual data: contracts, policies, knowledge bases, support archives, technical documentation. Instead of hallucinating answers, the system retrieves the relevant passages from your documents, generates a grounded response, and cites every source with page numbers. Automated ingestion keeps the index in sync as your data evolves, so the answers are always current. Your teams get a reliable research assistant that works across your entire document corpus.
Capabilities
Intelligent Chunking
Documents are split using context-aware strategies adapted to each content type. Narratives use semantic boundaries, tables and forms use structured extraction. Chunk sizes are tuned per corpus for optimal retrieval accuracy.
Hybrid Search
Vector embeddings capture meaning while keyword search catches exact terms. Combined with metadata filtering and cross-encoder reranking, the system surfaces the most relevant passages even in large, diverse document collections.
Multi-Modal Support
Text, images, tables, PDFs, and scanned documents are all processed. OCR and visual understanding handle complex layouts, extracting structured data from invoices, contracts, technical drawings, and forms.
Citations & Verification
Answers include source references with document names, page numbers, and relevance scores. Users verify claims against the original in one click. Faithfulness checks catch hallucinations before they reach end users.
Deliverables
- End-to-end RAG pipeline with ingestion and query API
- Vector store with automated sync from data sources
- Evaluation suite measuring precision, recall, faithfulness, and answer relevance
Tech Stack
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