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RAG Knowledge Systems
Grounded retrieval for massive data stores.
Connecting LLMs to your private data safely is the most valuable step in modern enterprise tech. We construct resilient Retrieval-Augmented Generation (RAG) pipelines that accurately navigate gigabytes of unstructured internal knowledge.
By combining dense vector search, sparse keyword matching, and cross-encoder re-ranking, we achieve near perfect retrieval accuracy, effectively eliminating hallucinations in mission-critical environments.
Key Capabilities
- ✓Hybrid search architecture (BM25 + Vectors)
- ✓Automated data ingestion from cloud drives
- ✓Semantic chunking and embedding logic
- ✓Real-time sync capabilities
- ✓Accurate inline citations
TL;DR / Key Takeaways
- →Raw vector search is often inadequate for domain-specific acronyms.
- →We employ hybrid methodologies to fetch EXACT matches where context matters.
- →Results are always paired with transparent source linking.
How It Works
1
StrategyMap data siloes and define knowledge scope.
2
BuildCode cleaning scripts, compute dense/sparse vectors.
3
DeployProvision vector databases (pgvector) and search indices.
4
OptimizeTune re-ranker top-K relevance over real queries.
Use Cases
Enterprise SearchConnect scattered technical docs seamlessly.
Support AutomationInstant, verified answers drafted for support reps.
Metrics
0.89 Precision@50 Hallucinations
Tech Stack & Tools
pgvectorLlamaIndexCohere Re-rankBM25