Cogniz AI - AI Context Everywhere

Introduction to Cogniz AI - AI Context Everywhere

Cogniz AI provides an enterprise-grade memory and context layer for AI systems. It enables long-term context preservation across sessions and applications, reducing token usage while maintaining semantic fidelity. The platform is built on AISL (a symbolic compression protocol) and DKCI (a cognitive memory substrate) to make interactions consistent and cost-efficient across models and tools.

The product targets teams and organizations that rely on LLMs in browsers, IDEs, and custom applications. Typical users include developers building AI features, product teams deploying context-aware assistants, and enterprises that need governance, auditability, and data controls alongside persistent AI memory.

Key Takeaways

• Unified memory accessible across ChatGPT, Claude, Gemini, and other AI platforms via a browser extension
• AISL symbolic compression designed to reduce token usage by approximately 30–90% while preserving meaning
• DKCI persistent memory to retain context, policies, and decisions across interactions
• Integrations via MCP, APIs, and SDKs (including a Python SDK) for custom apps and workflows
• VS Code extension and Claude Code hooks for auto-saving coding context to the Cogniz platform
• Role-based access controls, audit trails, and enterprise governance features
• End-to-end encryption and private memory handling
• Use cases spanning customer support (WordPress chatbot), recruitment automation, mobile apps, and business data management

How Cogniz AI - AI Context Everywhere Works

Cogniz AI captures relevant context from user interactions across tools and services. Using AISL, the system compresses intent and meaning into structured packets to reduce token counts while aiming to retain semantic fidelity. This compressed context can be attached to prompts and workflows, with a single API key connecting multiple AI services in the browser and other environments.

DKCI provides a durable memory substrate that persists beyond session limits. It stores decisions, commitments, preferences, and domain knowledge with role-based access controls and auditability. Multiple LLMs or agents can collaborate against the same memory base, with synchronization and governance controls to minimize conflicts. Developers can integrate via MCP, REST APIs, or the Python SDK, while IDE and browser extensions streamline capture and retrieval of relevant context.

Core Benefits and Applications

Cogniz AI helps reduce operational costs by compressing context while maintaining quality, and it improves response relevance by preserving long-term memory across models and tools. Organizations gain centralized governance over AI usage, including permissions, audit trails, and compliance-aligned controls.

Common applications include:

• Coding assistance: Automatically store significant code changes, architecture decisions, and context in VS Code; reuse them in future sessions
• Cross-platform chat: Share a single memory base across ChatGPT, Claude, and other AI chat interfaces for consistent responses
• Customer support: A WordPress chatbot that remembers customer history, products, and policies, integrating with WooCommerce and CRM data
• Recruitment: Automated CV analysis, candidate scoring, and matching against role requirements
• Mobile apps: Build context-aware mobile experiences with persistent user state, personalization, and analytics
• Business governance: Centralize AI data access, track usage, and enforce policies across teams

Plans and Capabilities (Overview)

Note: Specific pricing details are available on the Cogniz AI website and may change over time.