eRAG - 2026 Pricing, Features, Reviews & Alternatives

eRAG is a cloud-based Retrieval-Augmented Generation (RAG) platform built for enterprise structured data. It connects large language models (LLMs), including ChatGPT and compatible alternatives, directly to live operational databases such as ERP, CRM, and supply chain systems. Business users across functions, including executives, operations teams, finance teams, and line managers, can query real-time data using natural language without SQL expertise, pre-built dashboards, or data preparation. A semantic reasoning engine translates raw data structures (tables, columns, joins) into business language, while an internal knowledge graph stores automatically extracted and enriched metadata for fast query resolution. A data virtualization engine harmonizes SQL dialects across multiple databases, and no ETL, data movement, or new infrastructure is required. SaaS deployment with fine-tuning is typically completed in 3 to 4 weeks.

eRAG supports conversational, ad hoc data exploration with follow-up questions, what-if scenario generation, and next-step suggestions grounded in live operational data. An AI agent handles query processing, identifies context, and triggers workflows based on situational data analysis. Ambiguity detection prompts clarifying questions to reduce the risk of inaccurate responses, and human-in-the-loop calibration supports ongoing accuracy verification. Full explainability is provided: sources used, logic applied, and reasoning are surfaced to users alongside every response. Role-based access control, real-time AI risk scoring, and regulatory violation alerts support compliance and data governance, with all queries traceable via auditable SQL.

Target industries include logistics and supply chain, pharma and healthcare, retail and eCommerce, financial services, insurance, energy, hospitality and restaurant chains, education, and procurement and operations. The platform scales to handle large query volumes across teams of all sizes and supports integration with Microsoft 365 Copilot. A customizable knowledge base aligns the system to organization-specific terminology and workflows, reducing time-to-insight from hours or days to seconds while lowering LLM consumption costs.