Multimodal RAG & Agentic Workflows via Enterprise h2oGPTe | Part 15

2026-05-04 · Source: H2O.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

A platform designed to ground Large Language Models (LLMs) in enterprise-specific knowledge utilizes Retrieval Augmented Generation (RAG). This system ingests over 50 data formats, including PDFs, PowerPoints, audio, and video, employing multi-engine OCR for text extraction from images and PDFs, P speaker identification for audio, and keyframe extraction for video. It features native connectors for enterprise data sources like SharePoint, Confluence, S3, and Azure storage. Data is indexed and embedded for retrieval, with a built-in vector database and support for external integrations. Retrieval combines semantic similarity and BM25 keyword matching, fused with reciprocal rank fusion and reranked by cross encoders. The platform also supports metadata and access control at the collection level, ensuring users only access authorized documents, and extends RAG into agent workflows for iterative retrieval, reasoning, and tool execution.

Key takeaway

For CTOs and VPs of Engineering evaluating LLM integration, this RAG-based approach offers a robust method to infuse proprietary enterprise data into LLMs, ensuring accuracy and relevance. You should prioritize platforms that offer comprehensive data ingestion, advanced hybrid retrieval, and granular access control to maintain data security and operational efficiency. Consider how such a system can extend into agent workflows to automate complex tasks.

Key insights

RAG effectively grounds LLMs in enterprise knowledge by integrating diverse data sources and advanced retrieval mechanisms.

Principles

• Hybrid search improves retrieval accuracy.
• Access control is critical for enterprise data.
• Automated data processing enhances usability.

Method

The platform ingests diverse enterprise data, indexes and embeds it, then uses a hybrid search (semantic + BM25) with reranking for retrieval, ensuring access control and supporting agent workflows.

In practice

• Integrate RAG for custom LLM applications.
• Utilize native connectors for data sources.
• Implement metadata for access control.

Topics

• Retrieval-Augmented Generation
• Enterprise Knowledge Grounding
• Multimodal Data Processing
• Hybrid Search
• Agentic Workflows

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Engineer, Machine Learning Engineer, AI Architect

Related on AIssential

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• Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG — Corpus2Skill distills document corpora into navigable hierarchical skills, enabling LLM agents to actively explore and combine evidence.
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Editorial summary, takeaway, and curation by AIssential. Original article published by H2O.ai.