By the Numbers
Retrieval Accuracy (Top-5)
Embedding Dimensions
Avg. Query Latency
Documents Indexed
How It Works
We catalog your document corpus, identifying formats, languages, and update frequencies. A chunking and embedding strategy is designed to maximize retrieval accuracy for your use case.
Documents are processed through the ingestion pipeline, generating embeddings and sparse vectors. Milvus indexes are created with optimized parameters for your data volume and query patterns.
We benchmark retrieval quality with your real queries, tuning RRF weights, similarity thresholds, and chunk sizes. Automated evaluation scripts measure recall and precision against gold-standard answers.
The RAG system goes live behind a secure API with caching, rate limiting, and monitoring. Incremental ingestion pipelines keep the index current as new documents are added.
BAAI/bge-multilingual-gemma2 model generates 3584-dimensional embeddings that capture deep semantic meaning. Multilingual by design, ensuring consistent quality across Spanish, English, and more.
Combines traditional keyword matching with semantic vector similarity using Reciprocal Rank Fusion. This dual approach ensures both exact term matches and conceptual relevance are captured.
Enterprise-grade vector database infrastructure optimized for billion-scale similarity search. Partitioned indexes and filtered queries deliver sub-100ms retrieval at production loads.
Automated processing of DOCX, PDF, and structured data into chunked, embedded representations. Intelligent splitting preserves document structure, headings, and cross-references.
Retrieved chunks are ranked, deduplicated, and assembled to maximize relevance within the LLM context window. Prompt engineering ensures the model uses retrieved context faithfully.
Every AI response includes references to the source documents used. Users can verify answers against original materials, building trust and reducing hallucination risk.
Use Cases
A company with thousands of internal documents deploys RAG so employees can ask questions in natural language. The system retrieves the most relevant policy sections and generates precise answers with citations.
A law firm indexes contracts, case files, and regulations into a vector database. Attorneys use semantic search to find relevant precedents and clauses in seconds instead of hours.
A distributor with thousands of SKUs enables natural language product search. Sales reps describe what a customer needs in plain language and the system returns the best matching products with specifications.
A company with thousands of internal documents deploys RAG so employees can ask questions in natural language. The system retrieves the most relevant policy sections and generates precise answers with citations.
A law firm indexes contracts, case files, and regulations into a vector database. Attorneys use semantic search to find relevant precedents and clauses in seconds instead of hours.
A distributor with thousands of SKUs enables natural language product search. Sales reps describe what a customer needs in plain language and the system returns the best matching products with specifications.
Technology Stack
Let's discuss how this solution fits your business.