Stash
Stash provides a continual learning platform that captures AI agent traces—complete records of actions, decisions, and outcomes—to build persistent organizational memory. The platform syncs with enterprise tools like Slack and GitHub, applies sleep-time compute to precompute anticipated answers, and uses multi-strategy retrieval including semantic search, filesystem grep, and LLM-curated wikis. This enables knowledge workers and AI-transformation leaders to continuously improve agent performance across teams.
- Artificial Intelligence
- AI Agents
- Enterprise Software
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
AI agents increasingly perform general knowledge work—creating spreadsheets, writing reports, and preparing audits—but they lack persistent context about organizational history, past decisions, and prior problem-solving approaches. Without access to this accumulated knowledge, agents repeat mistakes, struggle with complex tasks, and require laborious, handcrafted evaluation setups or reinforcement learning environments to improve, limiting their practical usefulness in real business settings.
Solution
Stash provides a continual learning platform that captures, stores, and learns from agent traces—the complete record of an AI agent's actions, decisions, and outcomes. The platform synchronizes with existing enterprise tools, builds searchable indexes on top of the data, and applies machine learning to extract patterns and improvements automatically. Instead of relying on static evaluations, Stash enables agents to recall relevant past experiences through semantic retrieval, filesystem grep, and curated LLM-based document summaries or wikis. This approach lets teams improve agent performance continuously, sharing insights across the organization and creating a compounding knowledge base that makes every subsequent agent interaction smarter.
Target Audience
Primary customers are knowledge workers and AI-transformation leaders at companies—including product managers, consultants, and executives—who use AI agents for general knowledge work and need persistent organizational memory to make those agents effective.
Features
- Automated trace capture with hourly syncing from integrations like Slack and GitHub, with real-time updates via webhooks where available
- Sleep-time compute architecture that precomputes anticipated question-answer pairs for faster and more relevant AI retrieval
- Multi-strategy memory retrieval combining semantic vector search, filesystem grep, and curated LLM wiki documents
- Central integration control plane with authentication abstraction supporting OAuth, API tokens, and Dynamic Client Registration for scalable multi-tool connectivity
- Agentic mapreduce summarization for handling large-scale "summary" queries over historical data, such as analyzing customer churn patterns