UBIK glossary

Your guide to the core concepts of our constantly evolving ecosystem.

Clarity is key

The UBIK glossary provides precise definitions for the fundamental concepts and unique technologies that power the UBIK platform.

Explore our vision

Explore the ideas that make our product unique and stay informed on the latest advancements that are shaping the future of our ecosystem.

Published articles

Multi-signal search: unlocking precision and relevance with UBIK

Discover how UBIK's multi-vector search technology transforms data retrieval with precision and flexibility. Learn how multiple search signals deliver superior results, and see how our platform makes this advanced technology accessible to everyone—no technical expertise required.

Tool use and LLM

This article explores how tool integration transforms Large Language Models (LLMs) from simple text generators into reliable AI systems. It examines how tools address key LLM limitations like hallucinations and reasoning deficiencies, explains different implementation protocols including JSON Schema and Anthropic's MCP, and highlights how UBIK's platform makes these advanced capabilities accessible to organizations of all technical levels. The piece offers insights into both current applications and future prospects of tool-augmented AI systems.

What are UBIK agents ?

This article presents UBIK's vision of AI agents: modular, contextual, and customizable systems. Learn how "thought loops" enable agents to maintain context between interactions and execute complex action sequences. A practical perspective on creating agents adapted to specific business needs.

What are the components of the AI tech stack?

Explore the essential components of the modern AI tech stack: from vector databases and document handling to GPU infrastructure, interfaces, and orchestration layers. Learn how UBIK's complete solution addresses fragmentation challenges with a unified platform that delivers the full AI value chain—from data ingestion to user experience.

RAG Bottleneck 1 : Parsing

This article explores why document parsing is the first true bottleneck in most RAG systems. It breaks down how poor parsing corrupts the entire retrieval pipeline, from chunking to embedding to generation, and compares three main parsing approaches: text-based, layout-aware, and Vision Language Models.