Aphygo
Aphygo provides a persistent memory layer that runs on top of existing large language models, enabling AI systems to remember facts, context, concepts, and the reasoning behind past decisions. The system uses a three-part architecture—short-term, cognitive working, and long-term memory—to transform AI from a stateless response engine into a context-aware partner. This technology is designed for AI-powered SaaS companies and professionals such as doctors, lawyers, and engineers who need explainable, multi-step decision support.
- Artificial Intelligence
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Standard large language models lack persistent memory, responding to each query in isolation and forgetting prior interactions, context, and the reasoning behind past decisions. This limits their usefulness for complex, multi-step tasks where continuity and explainability are essential.
Solution
Aphygo provides a rich memory system that runs on top of an LLM, giving AI systems the ability to remember not only facts but also context and concepts in ways standard models cannot. The system captures the "why" behind actions, allowing AI to justify its responses and build a dynamic history of interactions. Aphygo's architecture comprises three parts—short-term memory, cognitive working memory, and long-term memory—so the AI evolves from a simple response engine into an empowered partner. This persistent memory layer enriches everything the AI does, enabling more sophisticated and context-aware assistance across various professional applications.
Target Audience
Primary customers are AI-powered SaaS companies seeking to enhance their products, as well as businesses and organizations requiring advanced decision support for professionals such as doctors, lawyers, and engineers.
Features
- Three-part memory architecture: short-term, cognitive working, and long-term memory for comprehensive context retention
- Captures reasoning and decision rationale, enabling AI to justify its answers and actions
- Runs on top of existing LLMs, enhancing their capabilities without requiring model replacement
- Supports rich interaction histories that accumulate over time for continuous learning and adaptation
- Designed for complex problem-solving scenarios including decision support and iterative design workflows
- Employs Matryoshka Representation Learning (MRL) to optimize vector embedding dimensions, balancing retrieval speed and semantic precision for scalable memory systems