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On-Device RAG for Enterprise CRM - Optimizing Privacy, Latency, and Offline Availability
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 67 |
| Year of Publication: 2025 |
| Authors: Vijaya Sai Munduru |
10.5120/ijca2025926119
|
Vijaya Sai Munduru . On-Device RAG for Enterprise CRM - Optimizing Privacy, Latency, and Offline Availability. International Journal of Computer Applications. 187, 67 ( Dec 2025), 8-13. DOI=10.5120/ijca2025926119
Abstract
Traditional CRM knowledge systems remain heavily dependent on cloud processing, where latency, data privacy, and network availability pose significant challenges. The author presents an Edge-First Retrieval-Augmented Generation system for mobile CRM applications. It runs every information-retrieval and text-generation task on the user's mobile device or an edge server nearby, without allowing sensitive customer data to leave the device. To implement the prototype, a lightweight, on-device generative text and semantic search process is used, executed locally. The system has been tested with a custom-built synthetic dataset called 'CRM-410', which includes 410 anonymized customer interaction profiles. This has been performed primarily to measure and compare a quantified edge-first system against a traditional cloud-based baseline across three key axes: query-to-response time (latency), data exfiltration risk or privacy, and functionality during network loss or unavailability. These results demonstrate that edge-first cuts latency to less than 2 seconds, enforces complete data privacy by keeping information local, and provides a strong, viable alternative for responsive, secure mobile CRM professionals.
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