Platform
2026Beta
Synapse Copilot
AI diagnostics engine for your ops knowledge base
An intelligent ops diagnostics assistant built on the Gemini 2.5 model with enterprise knowledge-base Retrieval-Augmented Generation (RAG), automatically analyzing log anomalies and outputting precise fix suggestions.
Mean Time To Recovery
- 62%
Doubled troubleshooting efficiency
Time to first token
380ms
Real-time streamed token push
Retrieval accuracy
94.8%
Hybrid dense-vector semantic matching
Illustrative · not real assets
Tech Stack
Next.jsGoogle GenAI SDKTypeScriptVector DBTailwind CSS
Background & Evolution
Combining the strengths of LLMs in code and log context understanding, we built in-house vector retrieval and dynamic prompt engineering to significantly reduce Mean Time To Recovery (MTTR).
Key Highlights
1
Server-side seamless streaming typewriter rendering with Markdown and architecture-code highlighting2
Automatic desensitization of sensitive log data: strips IPs, secrets, and PII before submission to the model3
Multi-turn context self-correction mechanism with historical diagnosis comparison archivingArchitecture & Topology
Next.js 15 App Router server-side API routes wrap the Google GenAI SDK, combined with a pgvector semantic embedding store for hybrid recall.
About This Project
An intelligent ops diagnostics assistant built on the Gemini 2.5 model with enterprise knowledge-base Retrieval-Augmented Generation (RAG), automatically analyzing log anomalies and outputting precise fix suggestions.