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RAG Architecture
Semantic chunking, metadata injection, and vector search retrievers.
Published: 2026-08-01•Last Updated: 2026-08-07
What is this service?
Construct high-precision Retrieval-Augmented Generation systems using semantic chunking and advanced hybrid search.
Who is this for?
Product companies and enterprise teams with large unstructured document corpuses needing accurate question answering.
Pricing & Engagement
- Starting from: ₹18,00,000
- Typical range: ₹18,00,000 - ₹40,00,000
- Pricing Model: Phase-based implementation
- Engagement timeline: 4 to 8 weeks
Typical Problems We Resolve
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Naïve text chunking splitting critical paragraphs and losing context.
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Irrelevant documents retrieved by vector search polluting the LLM context window.
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Out-of-date vector databases missing latest document updates.
Key Deliverables
Custom Document Parsing & Semantic Chunking Pipeline
Hybrid Search Retrieval Engine (BM25 + Dense Embeddings)
Automated Index Ingestion & Syncer Daemon
Technology Stack
PythonQdrantpgvectorCohere RerankLlamaIndexDocker
Expected Outcomes
✓
Retrieval accuracy (MRR) improved from 60% to 92%.
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LLM context token waste reduced by 40%.
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Near real-time vector database updates.
Next Steps & Engagement
Set up a deep-dive call to inspect sample document structures and define parsing constraints.