RAG & Vector Databases

RAG and Vector Databases

This showcase demonstrates how RAG and Vector Databases transform static document collections into fast, actionable knowledge. We move beyond simple keyword matching by using high-dimensional vector search to understand the meaning behind every query.

Orchestrated via n8n and powered by Qdrant (a high-performance Vector Engine), these workflows enable AI agents to retrieve precise information from corporate contracts, technical manuals, or product catalogs instantly. From text-based retrieval to advanced visual vectorization, this is the backbone of modern AI automation.

System Capabilities

Semantic Search: Finds answers by meaning and context, not just exact keywords.
Internal Knowledge Agent: Instantly answers employee questions across all internal documents.
Live RAG Connection: Connects your company’s real-time database directly to the AI model.
Autonomous SalesBot: Features product search, stock checks, recommendations, and automated ordering.
Analytics & Self-Improvement: Conversation logging with sentiment analysis, allowing the AI to refine its tone and logic based on real interactions.
Visual Vectorization: Facial recognition and image-based product search capabilities.

Video

Build Your Intelligent Ecosystem

This showcase illustrates the core technology behind our enterprise solutions. Depending on your business needs, we apply these RAG and Vector Database principles in four specific ways:

1. For Internal Efficiency
Deploy a private Enterprise AI Knowledge Assistant to let your team query technical docs and contracts securely.

2. For Customer Revenue
Launch an AI Sales Bot and Automation to handle inquiries, recommend products, and close deals 24/7.

3. For Visual Intelligence
Use AI Visual Search and Recognition to implement “Search by Image” or facial recognition systems.

4. For Data Ingestion
Need to digitize paper archives first? Use our OCR and Document Processing (або лінк на сторінку OCR) to convert raw files into vector-ready data.