Sentinel AI
The Cognitive Engine for Your Code
Sentinel AI transforms your codebase and documentation from static artifacts into a dynamic, reasoning partner. It's time to stop searching and start understanding.
The Architecture of Understanding
Sentinel AI operates on a sophisticated multi-layered memory system, ensuring every query is answered with optimal speed and context. This is the foundation of our Cached Augmented Generation (CAG) model.
Your Natural Language Query
"Show me all error handling in payment flows since Q1."
L0
Neural Cache
Instant, sub-millisecond responses from an in-memory cache. The system's muscle memory.
L1
Distributed Memory
A Redis-backed cache creates a shared brain for the entire team, compounding knowledge.
L2
Semantic Memory
Vector databases map complex relationships for deep, context-aware understanding.
L3
Source of Truth
Git-backed store provides perfect historical context for time-aware reasoning.
The CAG Economic Advantage
Cached Augmented Generation isn't just about speed; it's about efficiency. By intelligently caching responses, Sentinel AI dramatically reduces costly LLM calls and computational overhead. This chart shows a typical query distribution, where the vast majority of requests are served instantly from low-cost caches.
- 75% of queries resolved by L0/L1 Cache
- 20% answered by L2 Semantic Search
- 5% require full L3 Git analysis
Core Capabilities
💬 Conversational Search
Stop guessing keywords. Ask complex questions about your code and documentation in plain English and get precise, relevant answers.
⏳ Time-Aware Intelligence
Understand the "why" behind code changes. Sentinel AI analyzes your Git history to surface the rationale behind architectural decisions and track technical debt.
🤖 Multi-Agent Reasoning
Specialized AI agents work together to analyze code topology, evolution, and context, providing insights that a single model would miss.
🔬 Advanced AST Analysis
The high-speed Rust parser builds Abstract Syntax Trees to map hidden dependencies and complex patterns across multiple languages.
Under the Hood: Parsing Performance
The performance of the Rust-based AST parser is critical for rapid analysis. It efficiently processes various languages, enabling deep code intelligence at scale.
The Future Roadmap
Q4 2024
Public Beta Launch
Initial release with support for Python, JS/TS, and Go. Focus on core conversational search and L0/L1 caching.
Q1 2025
Enterprise Tooling
Deeper IDE integration (VS Code, JetBrains), advanced AST analysis, and initial multi-agent reasoning capabilities.
Q2 2025
Self-Hosting & Advanced Security
Offer self-hosting options for enterprises with strict data privacy requirements. Introduce advanced security and code vulnerability scanning agents.
Cache Challenge
Test your memory. Watch the sequence of cache layers light up, then repeat the pattern by clicking them in the correct order.
Level
0
Status
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