Cortex by SKYNETLAB

Product Introduction

1. Definition: Cortex by SKYNETLAB is a hosted, patent-pending semantic memory infrastructure for AI agents. It functions as an external, model-agnostic knowledge base that operates over the Model Context Protocol (MCP), acting as a shared cognitive layer separate from any single AI model's context window.
2. Core Value Proposition: Cortex exists to solve the fundamental problem of AI agent memory loss and knowledge degradation. It provides a persistent, auditable, and self-refining semantic memory that allows multiple AI agents and models to share, build upon, and cite a unified body of knowledge across conversations and sessions, preventing data duplication and tracking belief evolution.

Main Features

1. Quality Gate & Semantic Deduplication: Every piece of information written to Cortex passes through a classification and filtering layer. The system semantically analyzes incoming data against existing memories, rejecting redundant or duplicate entries. In production, this gate rejects approximately 80% of write attempts. This ensures the knowledge base refines rather than bloats, maintaining high signal-to-noise ratio.
2. Cognitive Shift & Contradiction Tracking: Instead of silently overwriting data, Cortex treats contradictions as first-class events. When new information conflicts with established, authoritative claims, the system tags this as a "belief change." It logs the prior state, the new data, and the context of the update, creating an auditable trail of how knowledge evolves. This makes the memory narratable and trustworthy.
3. Epistemic Self-Assessment & Brain Audit: Cortex implements metacognitive metrics to evaluate its own knowledge state. It assesses claims for verifiability, causal grounding, and robustness. The "Brain Audit" capability provides system-level metrics (e.g., knowledge coherence, contradiction rate, prediction accuracy) allowing users to monitor the health and improvement of the semantic memory without manual inspection.

Problems Solved

1. Pain Point: Traditional AI agents suffer from "context-window amnesia," where all reasoning, decisions, and learned facts vanish at the end of a session. Existing external memory solutions often become bloated repositories of unstructured, duplicate, and contradictory data, losing authority and becoming unreliable over time.
2. Target Audience: AI/ML Engineers and Developers building multi-agent systems or persistent AI assistants; Product Teams implementing enterprise AI copilots that require consistent, citable knowledge; Researchers and solo developers needing a robust, affordable semantic memory for complex, long-horizon AI projects.
3. Use Cases: Providing a consistent company knowledge base for a customer support AI copilot that cites sources; enabling a long-running research assistant AI to form and track evolving hypotheses over months; allowing different specialized agents (e.g., coding, writing, analysis) within a team to share and refine a common set of project facts and decisions.

Unique Advantages

1. Differentiation: Unlike simple vector databases or note-taking plugins for AI, Cortex is built as a filtering memory system, not just a storage one. It prioritizes knowledge quality and auditability over raw accumulation. Unlike vendor-locked AI memory features, it is fully model-agnostic, allowing the same "brain" to be used with Claude, GPT, or open-source models via MCP.
2. Key Innovation: The patent-pending engine core lies in its tri-pillar architecture: the pre-write Quality Gate, the contradiction-aware Cognitive Shift, and the self-evaluating Brain Audit. This transforms memory from a passive log into an active, self-improving, and narratable cognitive process. Its integration via the standardized MCP protocol ensures broad compatibility with a rapidly growing AI agent ecosystem.

Frequently Asked Questions (FAQ)

1. How does Cortex by SKYNETLAB connect to my AI? Cortex connects via the Model Context Protocol (MCP), an open standard. Most AI agent frameworks and clients (like Claude Desktop) support MCP servers. You connect your Cortex instance as an MCP server, typically in under 2 minutes, making its memory immediately available to your AI.
2. What is the difference between Cortex and a vector database? A vector database is a generic storage tool for embeddings. Cortex is a full-stack semantic memory system. It includes the vector storage layer but adds critical application logic: the Quality Gate for deduplication, the Cognitive Shift for contradiction management, and epistemic metrics. It manages the entire lifecycle of knowledge, not just retrieval.
3. Is my data locked into Cortex? No. Cortex is designed as infrastructure you connect to. Your semantic memory, built as typed claims and relationships, is your asset. The system's architecture and use of open standards like MCP aim to prevent vendor lock-in, allowing your accumulated knowledge to remain portable and usable.
4. How does the "Quality Gate" achieve an 80% rejection rate? The gate performs real-time semantic similarity checks and classification against the existing knowledge graph. It rejects information that is functionally identical or trivially rephrased from existing high-authority claims. This high rate indicates it is effectively preventing redundant data bloat, a common failure mode in naive memory implementations.
5. Who is behind SKYNETLAB and Cortex? Cortex is built and operated by SKYNETLAB, a solo developer based in Bergamo, Italy. The product is built on EU infrastructure, emphasizing data sovereignty, and is offered with transparent pricing starting from €0.99/month after a 30-day free trial that requires no credit card.