AI Development

KeenDreams.

AI-powered development memory system. Semantic search across codebase history, documentation, and team communications.

Vector DB OpenAI Embeddings LangChain Next.js

Knowledge Rot

As engineering teams grow, institutional knowledge gets fragmented across Slack, Jira, GitHub, and Google Docs. New hires spend weeks just figuring out "how things work" or why certain decisions were made.


KeenDreams was built to solve this by creating a unified semantic index of all technical knowledge.

  • Search Natural language queries, not just keywords.
  • Privacy Self-hosted LLM options for enterprise data.
  • Integrations Connectors for GitHub, Slack, and Linear.
  • Ranking Context-aware result scoring.
The Solution

Vector Search

Ingesting thousands of documents, chunking them, and storing vector embeddings for semantic similarity search.

RAG Pipeline

Retrieval Augmented Generation to feed relevant context into an LLM, allowing it to answer questions accurately.

Auto-Tagging

Using weak supervision to automatically classify and tag content based on its semantic meaning.

Results
50%
Faster Onboarding
1M+
Vectors Indexed
90%
Query Accuracy