PlatformPilot
PlatformPilot is a company brain for AI-first teams. It captures how your company actually works, your decisions, playbooks, and tribal knowledge, and turns it into a living memory your team and your AI agents can use to answer questions and take action across all your tools.
Unlike search tools that only retrieve, PlatformPilot reasons across your systems, shows the why behind every answer, and acts on your own playbooks, in your own cloud, getting sharper every time it is used.
It connects to your stack through the Model Context Protocol (MCP), so it works as a shared memory layer inside the tools your team already uses, including Claude Code, Claude Desktop, and OpenAI-based agents. Memory evolves as you work.
- Living memory that learns from outcomes, not just stores notes
- Reasoning across all your tools. We support +200 tools.
- Plain-language search over your team's decisions, playbooks, and history
- Self-organizing knowledge
Learn more
Qdrant
Qdrant is a high-performance, composable vector search engine built in Rust for production-grade semantic, hybrid, and agentic workloads.
Combine dense vectors, sparse vectors, metadata filters, multi-vector representations, and custom scoring as primitives at query time. Written in Rust for memory efficiency, SIMD optimization, and predictable performance without garbage collection pauses. No wrappers, no bolt-ons, no legacy compromises — just a custom HNSW implementation and storage engine built specifically for vector workloads.
Learn more
Papr
Papr is an AI-native memory and context intelligence platform that provides a predictive memory layer combining vector embeddings with a knowledge graph through a single API, enabling AI systems to store, connect, and retrieve context across conversations, documents, and structured data with high precision. It lets developers add production-ready memory to AI agents and apps with minimal code, maintaining context across interactions and powering assistants that remember user history and preferences. Papr supports ingestion of diverse data including chat, documents, PDFs, and tool data, automatically extracting entities and relationships to build a dynamic memory graph that improves retrieval accuracy and anticipates needs via predictive caching, delivering low latency and state-of-the-art retrieval performance. Papr’s hybrid architecture supports natural language search and GraphQL queries, secure multi-tenant access controls, and dual memory types for user personalization.
Learn more
Membase
Membase is a unified AI memory layer platform designed to help AI agents and tools share and persist context so they “understand you” across sessions without forced repetition or isolated memory silos, enabling consistent conversational experiences and shared knowledge across AI assistants. It provides a secure, centralized memory layer that captures, stores, and syncs context, conversation history, and relevant knowledge across multiple AI agents and integrations with tools such as ChatGPT, Claude, Cursor, and others, so all connected agents can access a common context and avoid repeating user intents. Designed as a foundational memory service, it aims to maintain consistent context across your AI ecosystem, reducing friction and improving continuity in multi-tool workflows by keeping long-term context available and shared rather than locked within individual models or sessions, and letting users focus on outcomes instead of re-entering context for each agent request.
Learn more
