Ground generative AI in your organization's verified ground truth.
Hybrid semantic search, vector databases (Qdrant/Milvus), graph RAG, and reranking pipelines.
Why Legacy Alternatives Fall Short
Legacy Stack Pitfall
Basic vector-search RAG returns irrelevant chunks, misses tabular information, and hallucinates false corporate policy.
The WEBTRIP Standard
We build advanced 4-stage RAG: semantic chunking, hybrid dense+sparse BM25 retrieval, cross-encoder reranking, and graph knowledge verification.
Runtime Topology: Retrieval-Augmented Generation (RAG)
Document Ingestion
Semantic Chunking & Parsing
Hybrid Search Engine
Dense Vector + BM25 Sparse
Cross-Encoder Rerank
Top-5 Precision Passages
Production Implementations & Deliverables
Hybrid Vector & Keyword Indexing
Combining dense vector embeddings with sparse BM25 keyword matching for exact code/SKU matching.
Cross-Encoder Neural Reranking
Cohere and BGE rerankers scoring candidate passages for true semantic relevance.
Knowledge Graph Integration (GraphRAG)
Extracting entity relationships into Neo4j to resolve multi-hop organizational questions.
Ready to Build or Modernize Your Software Infrastructure?
Schedule a 30-minute technical feasibility call with our senior solutions architects to explore custom Retrieval-Augmented Generation (RAG) Engineering systems.