RAG Systems & Vector Search

Retrieval-Augmented Generation (RAG) at Scale

Transform your documents, databases, and knowledge bases into intelligent AI-powered systems. Our RAG solutions ensure your AI stays accurate, grounded, and up-to-date.

Vector Search Infrastructure

Deploy Qdrant or pgvector-based systems to index and search embeddings at scale. We handle clustering, replication, and optimization for production workloads.

• Qdrant: Distributed vector DB with advanced filtering, multi-vector search, and SIMD-optimized operations
• pgvector: PostgreSQL extension for tight integration with relational data, HNSW indices, and cost efficiency
• Hybrid search: Combine keyword matching with semantic similarity for best-of-both-worlds retrieval

Semantic Chunking & Embeddings

Proper document preparation is crucial. We implement intelligent chunking strategies that preserve semantic boundaries, not just split by token count.

• Recursive splitting with overlap for context preservation
• Semantic clustering to group related information
• Cohere multilingual embeddings for English, Ukrainian, and Polish text
• Custom embedding fine-tuning for domain-specific vocabularies

Reranking & Relevance Optimization

Retrieve more candidates, then intelligently rank them. Cohere reranking ensures top results are truly relevant to the query.

• Multi-stage retrieval: dense → rerank → select
• Context-aware ranking considering question relevance and answer completeness
• Cost optimization through efficient candidate selection

Metadata & Filtering

Go beyond similarity scores. Filter by document type, date, author, category, or any business attribute. Ensure retrieved documents match your constraints.

Knowledge Graph Integration

For complex domains (legal, medical, technical), add knowledge graphs to your RAG. Entity extraction, relationship mapping, and graph traversal for structured reasoning.

Real-Time Index Updates

New documents? Updated information? Our systems sync with your data sources automatically. Incremental indexing keeps vector databases fresh without rebuilds.

Tech Stack

• Vector DBs: Qdrant, pgvector, Milvus
• Embeddings: Cohere embed-multilingual-v3.0, OpenAI text-embedding-3-large
• Reranking: Cohere Rerank, LLM-based relevance scoring
• Search frameworks: LangChain, LlamaIndex, custom Python
• Data pipelines: Apache Airflow, Luigi, custom ETL
• Backend: FastAPI, Django, PostgreSQL

Case Examples

• Legal tech: Contract analysis with 100K+ documents, instant retrieval of relevant clauses
• Support automation: FAQ system with semantic search, answers sourced from your knowledge base
• Enterprise search: Cross-document Q&A for compliance, internal procedures, technical specs
• Research assistant: Paper indexing and citation retrieval for academic workflows

Cost: $5K-12K for setup and optimization. Ongoing maintenance $500-2K/month depending on data volume.