cognitive memory
Memory Layer — Scientifically Validated Persistent AI Memory
| Metric | Value | Significance | |
|---|---|---|---|
| Dual-Judge Validation | Cohen's Kappa >0.70 | Gold standard measuring agreement between AI judges - proves reliability | Dual-Judge Validation Cohen's Kappa >0.70 Gold standard measuring agreement between AI judges - proves reliability |
| Cost Optimization | 90% reduction | €106/month → €5-10/month, same quality at fraction of cost | Cost Optimization 90% reduction €106/month → €5-10/month, same quality at fraction of cost |
| Evaluation Tests | 50+ test cases | Dual-judge framework with automated Cohen's Kappa calculation | Evaluation Tests 50+ test cases Dual-judge framework with automated Cohen's Kappa calculation |
Challenge
How can LLMs maintain context beyond their token window, avoid hallucinations, and have persistent memory without prohibitive costs? Naive RAG implementations are expensive (€106/month) and unreliable.
Solution
Hybrid-RAG architecture combining semantic search with episodic memory, enhanced by Dual-Judge Evaluation (Cohen's Kappa >0.70) for scientific validation of AI outputs and 90% cost reduction through intelligent caching.
Built a Hybrid-RAG architecture combining semantic search with episodic memory. Implemented Dual-Judge Evaluation (Cohen's Kappa >0.70) for scientific validation of AI outputs. Achieved 90% cost reduction through intelligent caching strategies.
Cognitive Memory: Memory Layer — Scientifically Validated AI Memory
How Do You Build AI That Remembers Without Hallucinating—And Doesn't Cost a Fortune?
Cognitive Memory is a production-ready memory layer implementing Hybrid-RAG architecture that combines semantic search with episodic memory. Unlike typical RAG implementations that are expensive (€106/month) and unreliable, Cognitive Memory uses scientific validation—Cohen's Kappa >0.70, the gold standard for measuring agreement between AI judges—to prove it actually works. Achieves 90% cost reduction (€106 → €5-10/month) through intelligent caching while maintaining quality.
The Problem: RAG is Expensive and Unreliable
Naive RAG implementations are prohibitively expensive (€106/month) and lack reliability measures for business-critical applications. Without validation frameworks, AI outputs can hallucinate or retrieve irrelevant context.
The Solution: Hybrid-RAG with Scientific Validation
Cognitive Memory combines semantic search with episodic memory, enhanced by Dual-Judge Evaluation (Cohen's Kappa >0.70) for scientific validation. Achieves 90% cost reduction through intelligent caching strategies while maintaining output quality.
Key Features
- Hybrid-RAG Architecture: Combines semantic search with episodic memory
- Dual-Judge Evaluation: Cohen's Kappa >0.70 (gold standard measuring agreement between AI judges)
- Cost Optimization: €106/month → €5-10/month (90% reduction)
- Validation Framework: 50+ test cases with automated Cohen's Kappa calculation
- MCP Integration: Serves i-o-system and agentic-business projects
Technical Stack
- Python, MCP (Model Context Protocol)
- Qdrant (vector database)
- FastAPI (API layer)
- Pytest, numpy, pandas (validation)
Impact
Production-ready memory layer serving multiple AI projects with validated reliability. Scientific validation through Cohen's Kappa ensures business-critical reliability while reducing costs by 90%.
Technologies & Skills Demonstrated: RAG Architecture, Vector Databases, Scientific Validation, MCP, Python, Cost Optimization, Testing
Timeline: 2025 | Role: Developer
Screenshots
Backend
Tools & Services
Database
AI Stack Connections
Impact
Production-ready memory layer serving both i-o-system and agentic-business. Validated with Cohen's Kappa >0.70 inter-rater reliability. Reduced operational costs from €106/month to €5-10/month.
Key Learnings
- Scientific validation matters: Cohen's Kappa >0.70 provides statistical proof that the system works—most AI systems claim reliability but don't measure it
- Cost optimization through caching: 90% reduction (€106 → €5-10/month) proves production RAG can be economically viable with intelligent architecture
- Hybrid-RAG balance: Semantic search + episodic memory provides optimal retrieval—pure semantic or pure episodic approaches each have limitations