Enterprise RAG Evaluation & Benchmarking
Eliminate LLM hallucinations and inaccurate retrieval in your production RAG pipelines. We benchmark retrieval accuracy, answer faithfulness, and context recall using Ragas, TruLens, and DeepEval, tuning chunking strategies and hybrid vector search for 99%+ accuracy.
Context Precision: 0.962 (PASS)
Answer Faithfulness: 0.988 (PASS)
Context Recall: 0.945 (PASS)
Hallucination Rate: < 0.4%
✓ Production Grade Enterprise Verification
Quantifiable Accuracy for Regulated Industries
We tune every stage of the knowledge ingestion and inference loop to ensure audit-ready compliance.
Semantic Chunking & Parsing
Replace naive token-splitters with structure-aware markdown, table, and document hierarchy semantic chunking.
Cross-Encoder Re-Ranking
Implement Cohere Rerank and BGE re-ranking models to filter top-k vector chunks before LLM prompt injection.
Automated CI/CD RAG Testing
Run continuous regression tests against golden enterprise Q&A datasets to prevent model accuracy degradation over time.
Benchmark Your Enterprise RAG System
Partner with Webnext to audit, evaluate, and optimize your proprietary RAG vector pipeline.
