With the increasing adoption of Large Language Models (LLMs) in industry, ensuring trust, transparency and data sovereignty has become critical. Within the TrustInLLM project, Area Analytics introduces AERIALL, a robust RAG-based framework designed for trustworthy AI applications.
AERIALL: Bringing Trust to Industrial LLM Applications
AERIALL is a Retrieval-Augmented Generation (RAG) framework powered by a locally hosted LLM. This architecture enables organizations to maintain full control over sensitive data while leveraging advanced AI capabilities.
Users can upload domain-specific documents to extend the model’s knowledge base, allowing professionals to query highly specialized content and receive context-aware, precise answers.
Transparency and Explainability by Design
A key feature of AERIALL is its strong focus on explainability and traceability:
- AI-generated answers include direct citations of document chunks
- A built-in source viewer visualizes retrieved information within original documents
- Confidence scores provide insights into the reliability of outputs
This ensures that users can understand, verify, and trust the system’s responses.
Multimodal and Flexible Knowledge Integration
Unlike conventional RAG systems, AERIALL supports a wide range of data sources:
- Text documents (e.g., PDFs)
- Structured data (e.g., Excel files)
- Code files and technical documentation
- Embedded multimodal elements such as images, tables, and diagrams
These elements are directly integrated into the generated answers, enabling richer and more context-aware AI interactions.
Security and Data Sovereignty
AERIALL places strong emphasis on security and privacy:
- Locally hosted models ensure data sovereignty
- Sensitive and confidential information remains within organizational boundaries
- Suitable for industrial and enterprise environments with strict compliance requirements
Contact
Interested in integrating trustworthy AI into your processes?
Contact Belgin Mutlu to explore collaboration opportunities.