Post-Cortex FAQs

Question 1

What core problems does Post-Cortex solve for AI assistants?

Accepted Answer

It solves the fundamental limitation of transient AI conversations by enabling assistants to maintain perfect memory across sessions, understand complex semantic relationships, and retrieve contextually relevant information instantly, overcoming the 'forgetting' problem.

Question 2

How does semantic search work in Post-Cortex?

Accepted Answer

Post-Cortex uses local transformer models (like MiniLM or StaticSimilarityMRL) to convert text into high-dimensional vectors. It then uses these embeddings for context-aware retrieval, allowing you to query information based on meaning rather than exact keywords, all powered by on-device AI.

Question 3

What is Post-Cortex?

Accepted Answer

Post-Cortex is a production-grade, privacy-first intelligent memory system for AI assistants. It transforms ephemeral AI conversations into persistent, searchable knowledge, providing durable memory, automatic knowledge graph construction, and local semantic search.

Question 4

Does Post-Cortex automatically build knowledge graphs?

Accepted Answer

Yes, it automatically constructs intelligent knowledge graphs. Every conversation is analyzed to extract key entities, map relationships, and identify importance without manual intervention, dynamically building an interconnected knowledge base.

Question 5

How does Post-Cortex ensure data privacy?

Accepted Answer

It features a privacy-first architecture where all processing occurs locally. There are zero external API dependencies, and local transformer models are used for semantic search, ensuring your data never leaves your environment.

Post-Cortex

Post-Cortex

Key Features

Use Cases

Key Features

Use Cases