AI/ML CASE STUDY

Enterprise RAG System: Beyond Keyword Search to Semantic Retrieval

Customer support relied on keyword search with low recall and no context awareness, causing 45% efficiency loss.

Built production RAG pipeline with multiple vector stores, hierarchical chunking strategies, and cross-encoder reranking for 99.9% retrieval accuracy.

Retrieval Accuracy

99.9%

P95 Latency

200ms

Support Efficiency

45%

Context

DigitalAlign, a US-based enterprise, needed to replace keyword-based search with semantic retrieval across their internal knowledge base to empower customer support agents.

Problem

Keyword search returned irrelevant results 40% of the time. Agents spent minutes manually searching documentation instead of helping customers. The knowledge base contained 50,000+ documents across multiple formats—HTML, PDF, Markdown, database records. Keyword matching couldn't handle synonyms, technical jargon, or context-dependent queries. Every failed search meant longer resolution times and inconsistent customer experiences.

Constraints

Integration with existing CRM and support workflows without disrupting agent workflows. Enterprise-grade data privacy with role-based access control. 99%+ retrieval accuracy requirement—the system had to be reliable enough for production use. P95 latency under 500ms to maintain agent productivity.

Approach

RAG isn't just embedding + retrieval. We designed a multi-stage retrieval pipeline: (1) initial BM25 recall for recall-bound queries, (2) semantic embedding search across multiple vector stores (different embedding models for different content types), (3) cross-encoder reranking to optimize relevance before presenting results. The key was treating document structure intelligently—chunking strategies varied by content type, not one-size-fits-all.

Implementation

The pipeline used LlamaIndex as the orchestration layer with custom node parsers for each document type. Technical docs used recursive character splitting with parent-child relationships to preserve context. FAQs used sentence-window retrieval to capture complete answers. Three vector stores—Pinecone for semantic search, Weaviate for hybrid search, and a custom in-memory store for hot data—were queried in parallel. A cross-encoder (BGE-reranker) re-scored the top 20 candidates before returning results. The system maintained a rolling cache of recent queries to avoid redundant embedding calls. Integration was via a sidebar widget in the support portal, providing context-aware answers without agents leaving their workflow.

Results

Retrieval accuracy reached 99.9% on the evaluation benchmark—measured as whether the top result answered the agent's query. P95 latency stayed under 200ms through caching and query optimization. Agent handle time dropped 45%, freeing capacity for 30% more support volume without adding staff. First-contact resolution improved 28%.

Key Insight

The biggest RAG gains come from retrieval architecture, not model selection. We spent 80% of our effort on chunking strategies, multi-store fusion, and reranking. The embedding model was the easy part. Most production RAG systems fail because they treat embedding as the entire solution rather than one stage in a multi-stage pipeline.

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