CHARLIE: An On-Premise Multi-Agent Retrieval-Augmented Generation System for Evidential Reasoning in Forensic Science
arXiv:2607.05428v1 Announce Type: cross Abstract: We present Charlie, an on-premise multi-agent Retrieval-Augmented Generation (RAG) system for structured evidential processing in digital forensic environments. Contemporary forensic workflows must handle large volumes of heterogeneous and unstructured documents under strict requirements of traceability, confidentiality, and legal compliance. Charlie addresses this challenge through a controlled agent architecture that combines local retrieval, task decomposition, structured memory, and verification mechanisms. Unlike cloud-based systems, it op
The increasing volume and complexity of digital data in forensic science, coupled with evolving security and regulatory demands, necessitate advanced, privacy-preserving AI solutions like Charlie.
This development highlights the critical need for secure, traceable, and legally compliant AI in sensitive domains, pushing RAG systems towards robust, on-premise deployments that circumvent cloud-based vulnerabilities.
The deployment of multi-agent RAG systems explicitly designed for on-premise operation indicates a growing bifurcation in AI application—cloud for general use, and secure local solutions for critical, sensitive workflows.
- · Digital forensic firms
- · On-premise AI infrastructure providers
- · Legal and compliance tech sector
- · Governments seeking data sovereignty
- · Cloud-native RAG solution providers in sensitive sectors
- · Less secure, general-purpose AI tools
- · Organizations relying solely on public cloud for sensitive data
Forensic investigations become significantly more efficient and accurate due to automated evidence processing and verification.
Increased demand for on-premise AI hardware and specialized cybersecurity for sensitive multi-agent systems.
Potential for similar on-premise, secure multi-agent AI systems to proliferate across other highly regulated industries like finance and healthcare.
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Read at arXiv cs.AI