Improve output accuracy
To solve your most complex business challenges, you need a RAG pipeline you can rely on to deliver laser-like accuracy.
Reduce preparation time
Don't waste time on manual data and content preparation. Vertesia's agentic RAG pipeline processes long-form content and rich media effortlessly.
Avoid production blocks
Most AI apps don't make it past prototype. Get to production faster with an agentic RAG pipeline that gives you consistent, reliable results.
Agentic processing
Use AI agents to structure content during pre-processing and automate your RAG pipeline.
Patent-pending tech
We have five patent-pending patterns for processing complex content.
Meta content
Produce meta information, like source, date, and relevance, during content pre-processing. This helps AI models to understand and process data.
Hybrid retrieval
Combine multiple search methods to refine retrieval results. Our agents refine semantic search results with structured and graph searches.
Multiple vector indexes
Create full text, property, and visual image indices to enhance semantic search accuracy.
Automated embeddings
Change GenAI models without touching your vector index. Simply switch and go for seamless searching and generation.
Semantic groupings
Use an AI agent to group text based on language understanding and analysis.
Context preservation
Prevent the separation of critical concepts and context across arbitrary, character-based chunks.
Input limits
Automatically addresses model input limits while ensuring quality outputs.
Improve results
Reduce the risk of losing meaning across token windows, leading to more accurate responses and better comprehension.
Reduce hallucinations
Ensure search queries return more relevant and contextually complete results for fewer hallucinations and enhanced information retrieval.
Optimize costs
Minimize redundant or unnecessary text in prompts to optimize token usage. This leads to lower processing costs and improved performance.
ENTERPRISE ARCHITECTURE GUIDE
Effective RAG strategies for LLM applications & AI agents
This paper explores the intricacies of RAG strategies, emphasizing the superiority of semantic RAG for enterprise software architects aiming to build robust LLM-enabled applications and services.
