NeuroRAG

NeuroRAG is the backbone of Neural AI’s conversational AI products. It combines large language models with intelligent retrieval systems that search your proprietary data — documents, databases, websites, and knowledge bases — to generate responses that are accurate, cited, and grounded in fact rather than hallucination.

How It Works

When a user asks a question, NeuroRAG first searches your indexed knowledge base using semantic similarity, keyword matching, and contextual re-ranking. The most relevant passages are then provided as context to the language model, which generates a natural-language answer with references to the source material. This approach ensures responses reflect your actual data rather than the model’s general training.

Enterprise Knowledge Bases

NeuroRAG ingests documents in any format — PDFs, Word files, web pages, spreadsheets, database records — and builds a searchable vector index. Incremental updates keep the index current as your knowledge base evolves, with no downtime or full re-indexing required.

Accuracy and Trust

Every NeuroRAG response includes source citations, confidence scores, and the ability to trace answers back to specific documents. For regulated industries such as financial services and government, this audit trail is essential for compliance and accountability.

Proven at Scale

NeuroRAG powers chatbots for Ligi.ai’s legal research platform, the mySocialSecurity government portal, Browns supermarket customer service, the Arts Council Malta information system, and Climate Action’s environmental advisory tool. Each deployment handles thousands of queries daily with consistently high accuracy.