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AI can't connect the dots because each source is a silo.\n\n**Papr transforms scattered data into connected intelligence** through seven products \nthat work standalone or together:\n\n- 📊 **Vector Memory** - Semantic search over any content\n- 💬 **Chat Memory** - Conversation storage and compression\n- 📄 **Document Intelligence** - Extract structure from PDFs and docs\n- 🔗 **Knowledge Graphs** - Map relationships, not just similarity\n- 🎯 **Graph-Aware Search** - Domain-tuned retrieval (code, science, etc.)\n- 🤖 **AI Model Proxy** - Unified multi-model API\n- 🔄 **Sync & Portability** - Local/cloud memory sync\n\n[Explore products →](./products.md) | [Quick start →](../quickstart/index.md)\n","lang":"markdown"},"children":[]},{"$$mdtype":"Tag","name":"hr","attributes":{},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"products-section-new-after-opening"},"children":["Products Section (New, After Opening)"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"tsx","header":{"controls":{"copy":{}}},"source":"<Section id=\"products\">\n  <SectionTitle>Seven Products, One Platform</SectionTitle>\n  <SectionSubtitle>\n    Use standalone or combine them. 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Direct memories (explicit data)\n\n**Intelligence Layer:**\n- Vector embeddings (semantic understanding)\n- Knowledge graphs (relationship mapping)\n- Predictive caching (anticipate needs)\n- Domain tuning (specialized search)\n\n**Output:**\n1. Natural language search (ask questions, get insights)\n2. GraphQL analytics (structured queries, aggregations)\n\n[See architecture →](../concepts/architecture.md)\n","lang":"markdown"},"children":[]},{"$$mdtype":"Tag","name":"hr","attributes":{},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"social-proof-section-new-after-products"},"children":["Social Proof Section (New, After Products)"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"tsx","header":{"controls":{"copy":{}}},"source":"<Section id=\"proof\">\n  <SectionTitle>Trusted by AI Teams</SectionTitle>\n  \n  <Stats>\n    <Stat\n      number=\"#1\"\n      label=\"Stanford STaRK Benchmark\"\n      description=\"91%+ retrieval accuracy\"\n    />\n    <Stat\n      number=\"<150ms\"\n      label=\"Response time\"\n      description=\"With predictive caching\"\n    />\n    <Stat\n      number=\"7\"\n      label=\"Products\"\n      description=\"Standalone or combined\"\n    />\n  </Stats>\n  \n  <Testimonial\n    quote=\"Papr turned our scattered documentation into an intelligent knowledge base. \n           Our support team now surfaces answers 3x faster.\"\n    author=\"Engineering Lead\"\n    company=\"Enterprise SaaS\"\n  />\n</Section>\n","lang":"tsx"},"children":[]},{"$$mdtype":"Tag","name":"hr","attributes":{},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"updated-start-here-section"},"children":["Updated \"Start Here\" Section"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Replace \"Evaluate Fit / Start Building\" with:"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"markdown","header":{"controls":{"copy":{}}},"source":"## Start Here\n\n### 1. Choose Your Path\n\n**Building something specific?**\n- [Document Q&A](../tutorials/document-qa.md) - Extract + search PDFs\n- [Conversational AI](../tutorials/chat-history.md) - Chat with memory\n- [Code Search](../guides/graph-aware-embeddings.md) - Find code by intent\n- [Knowledge Management](../tutorials/enterprise-saas.md) - Multi-tenant intelligence\n\n**Just exploring?**\n- [Products Overview](./products.md) - See all seven products\n- [Quick Start (5 min)](../quickstart/index.md) - Ship a prototype\n- [Decision Tree](./decision-tree.md) - Which products do you need?\n\n### 2. Integrate\n\n- [TypeScript SDK](../sdks/typescript.md)\n- [Python SDK](../sdks/python.md)\n- [REST API Reference](../apis/index.yaml)\n- [LangChain / LlamaIndex](../examples/index.md)\n","lang":"markdown"},"children":[]},{"$$mdtype":"Tag","name":"hr","attributes":{},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"complete-updated-overview/index.md"},"children":["Complete Updated Overview/Index.md"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"markdown","header":{"controls":{"copy":{}}},"source":"# Turn Data Into Intelligence\n\n**Seven products that connect your conversations, documents, and knowledge—so AI delivers insights, not just answers.**\n\n> **TL;DR:** Store with `POST /v1/memory`. Search with `POST /v1/memory/search`.  \n> Ranked **[#1 on Stanford's STaRK benchmark](https://huggingface.co/spaces/snap-stanford/stark-leaderboard)** \n> with **91%+ accuracy** and **<150ms retrieval** (when cached).\n\n---\n\n## From Data to Intelligence\n\nYour data is everywhere—conversations in Slack, documents in Drive, code in GitHub, tickets in Linear. AI can't connect the dots because each source is a silo.\n\n**Papr transforms scattered data into connected intelligence** through seven products that work standalone or together:\n\n- 📊 **Vector Memory** - Semantic search over any content\n- 💬 **Chat Memory** - Conversation storage and compression\n- 📄 **Document Intelligence** - Extract structure from PDFs and docs\n- 🔗 **Knowledge Graphs** - Map relationships, not just similarity\n- 🎯 **Graph-Aware Search** - Domain-tuned retrieval (code, science, etc.)\n- 🤖 **AI Model Proxy** - Unified multi-model API\n- 🔄 **Sync & Portability** - Local/cloud memory sync\n\n[Explore all products →](./products.md) | [Quick start (5 min) →](../quickstart/index.md)\n\n---\n\n## Intelligence Outcomes\n\n### Search that understands\nNot just keywords—semantic search across conversations, docs, and code. **91%+ accuracy** on Stanford's STaRK benchmark.\n\n### Connected context\nCode → ticket → conversation → decision. Your knowledge becomes **one connected story**, not isolated pieces.\n\n### Predictive intelligence\nAnticipates what users need before they ask. Pre-caches context for **<150ms retrieval** (when cached).\n\n### Domain-aware precision\nFilter by programming language, scientific field, or custom dimensions. Search returns **what you actually need**.\n\n### Analytics and insights\nQuery relationships with GraphQL. Find patterns across your data. Answer **\"why\"** not just **\"what\"**.\n\n### Enterprise-ready security\nBuilt-in ACLs, namespace isolation, compliance controls. **Data never leaks** across users.\n\n---\n\n## Seven Products, One Platform\n\nUse standalone or combine them. Start simple, add intelligence as you need it.\n\n### Core Products\n\n**[Vector Memory](./products.md#vector-memory)** - Semantic search and RAG foundation  \n**[Chat Memory](./products.md#chat-memory)** - Conversation storage with compression  \n**[Document Intelligence](./products.md#document-intelligence)** - Extract from PDFs and docs  \n**[AI Model Proxy](./products.md#ai-model-proxy)** - Unified multi-model API\n\n### Add Intelligence\n\n**[Knowledge Graphs](./products.md#knowledge-graphs)** - Map entity relationships (addon)  \n**[Graph-Aware Search](./products.md#graph-aware-search)** - Domain-tuned retrieval (addon)  \n**[Sync & Portability](./products.md#sync--portability)** - Local/cloud sync (feature)\n\n[Compare all products →](./products.md)\n\n---\n\n## Data In, Intelligence Out\n\n**Three ways to send data:**\n1. Documents (PDFs, Word docs) - `POST /v1/document`\n2. Conversations (chat messages) - `POST /v1/messages`\n3. Direct memories (explicit data) - `POST /v1/memory`\n\n**Intelligence layer transforms it:**\n- Vector embeddings (semantic understanding)\n- Knowledge graphs (relationship mapping)\n- Predictive caching (anticipate needs)\n- Domain tuning (specialized search)\n\n**Two ways to query:**\n1. Natural language search - `POST /v1/memory/search`\n2. GraphQL analytics - `POST /v1/graphql`\n\n[See architecture →](../concepts/architecture.md) | [API reference →](../apis/index.yaml)\n\n---\n\n## What You Can Build\n\n[Personal AI Assistant](../tutorials/chat-history.md) - Store/retrieve conversations  \n[Document Q&A](../tutorials/document-qa.md) - Intelligent document chat  \n[Customer Support](../tutorials/customer-experience.md) - Answer FAQs, resolve tickets  \n[Enterprise Knowledge](../tutorials/enterprise-saas.md) - Multi-tenant intelligence  \n[Code Search](../guides/graph-aware-embeddings.md) - Find code by natural language  \n[Domain Ontologies](../guides/custom-schemas.md) - Custom knowledge graphs  \n[Graph Analytics](../guides/graphql-analysis.md) - Query insights with GraphQL\n\n---\n\n## Start Here\n\n### 1. Choose Your Path\n\n**Building something specific?**\n- [Document Q&A](../tutorials/document-qa.md) - Extract + search PDFs\n- [Conversational AI](../tutorials/chat-history.md) - Chat with memory\n- [Code Search](../guides/graph-aware-embeddings.md) - Find code by intent\n- [Knowledge Management](../tutorials/enterprise-saas.md) - Multi-tenant intelligence\n\n**Just exploring?**\n- [Products Overview](./products.md) - See all seven products\n- [Quick Start (5 min)](../quickstart/index.md) - Ship a prototype\n- [Decision Tree](./decision-tree.md) - Which products do you need?\n\n### 2. Integrate\n\n- [TypeScript SDK](../sdks/typescript.md) | [Python SDK](../sdks/python.md)\n- [REST API Reference](../apis/index.yaml)\n- [LangChain / LlamaIndex Examples](../examples/index.md)\n\n---\n\n## Deployment Options\n\n**Papr Cloud** - Managed service, 5-minute setup  \n[Get started →](../quickstart/index.md) | [Learn more →](../deployment/cloud.md)\n\n**Hybrid Cloud** - Your infrastructure, we manage it  \n[Enterprise →](../deployment/hybrid.md) | [Talk to sales →](https://calendly.com/amirkabbara/30min)\n\n**Self-Hosted** - Open source, full control  \n[Setup →](../deployment/self-hosted.md) | [GitHub →](https://github.com/Papr-ai/memory-opensource)\n\nAll deployment options use **identical APIs**. Code written for one works with all three.\n\n[Compare deployments →](../deployment/index.md)\n\n---\n\n## Why Teams Choose Papr\n\n**Instead of building:**\n- Basic RAG (70-80% accuracy)\n- Manual fusion of keyword + vector\n- Custom knowledge graphs\n- Fragmented data sources\n\n**You get:**\n- 91%+ retrieval accuracy (#1 benchmark)\n- Hybrid search built-in\n- Predictive intelligence layer\n- Connected context across sources\n- Seven products, one API\n- Open source + enterprise\n\n[See detailed comparison →](./why-papr.md)\n","lang":"markdown"},"children":[]},{"$$mdtype":"Tag","name":"hr","attributes":{},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"implementation-priority"},"children":["Implementation Priority"]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"phase-1-messaging-only-15-min"},"children":["Phase 1: Messaging Only (15 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