An objective technical overview explaining the design choices, infrastructure differences, and privacy frameworks of Snèh AI and ChatGPT.
| Architecture Dimension | Snèh AI | ChatGPT (Free Consumer Tier) |
|---|---|---|
| Inference Acceleration | Sneh-V3 ® LPU™ Hardware Routing | Standard GPU Clusters |
| User Data Training Policy | Zero User Data Training | Prompts Used for Training (Unless Opted Out) |
| Memory Processing | Volatile Server RAM Session Flushing | Persistent Account Message Logs |
| Live Web Grounding | Integrated Toggleable Web Search | Tier Dependent / Account Gated |
| Account Requirements | Instant Browser / Guest Access | Mandatory User Account Registration |
| PWA Standalone App | Installable Cross-Platform PWA | Web Browser / Native App Store Downloads |
Snèh AI is designed with an explicit zero-retention data policy. User conversations are processed in volatile RAM buffers during active response streaming and flushed immediately after completion. Prompts submitted to Snèh AI are never retained to pre-train or fine-tune public base models. In contrast, standard consumer web interfaces typically retain chat logs for model improvement unless users explicitly adjust privacy settings.
Snèh AI routes model requests through Sneh-V3 ® Language Processing Units (LPUs), an architecture engineered specifically for low-latency sequential token streaming. This setup delivers ultra-fast response generation, making it suited for real-time coding assistance and rapid research queries.
While OpenAI operates a centralized platform requiring account authentication, Snèh AI provides immediate web access alongside an installable Progressive Web Application (PWA). This allows users to run Snèh AI on mobile and desktop platforms as a lightweight, independent workspace application.