SciPhi
Open siteIntroduction
Simplify building and deploying RAG pipelines.
SciPhi Product Information
SciPhi | The most advanced AI retrieval system
SciPhi is an enterprise-grade AI retrieval platform designed to enable reliable, context-aware AI applications through advanced hybrid search, knowledge graphs, and scalable RAG (Retrieval-Augmented Generation) workflows. It emphasizes rapid deployment, strong access controls, and deep document intelligence to power knowledge management and AI-powered insights at scale.
Overview
SciPhi's R2R (Retrieval-to-Response) framework bridges the gap between prototypes and production by delivering infrastructure for ingestion, retrieval, and scalable AI pipelines. It supports multimodal document ingestion, automatic knowledge graph construction, and precise, context-rich responses across large document collections.
How It Works
- Ingest and index documents across 40+ formats (PDFs, spreadsheets, audio, etc.).
- Build context-aware knowledge graphs that map relationships and enrich context across your documents.
- Perform hybrid search using both vector embeddings and traditional retrieval to surface relevant chunks.
- Generate AI-driven responses with integrated retrieval results, enabling accurate and explainable outputs.
- Deploy rapidly with a managed cloud solution and minimal configuration, optimized for developers and enterprises.
Features
- Universal Document Processing: Process millions of documents across 40+ formats with quick onboarding.
- Enterprise Access Control: Granular permissions, document-level access, and teamwork-ready collections.
- Advanced Document Intelligence: Automatically map relationships and enrich context; build comprehensive knowledge graphs.
- Hybrid Search: Combine knowledge graphs and vector retrieval for precise, context-rich results.
- Knowledge Graphs: Extract entities and relationships to create navigable knowledge graphs.
- Seamless Deployment: Instant deployment with managed cloud support and auto-scaling.
- Developer-Friendly API: Clear APIs and SDKs to integrate R2R into existing workflows.
- Configurable Retrieval Pipelines: Flexible settings for search, ranking, and generation.
- Scalable Infrastructure: Designed to handle enterprise workloads with reliability.
Why SciPhi R2R (Retrieval-Augmented Generation)
- Improves accuracy and relevance by leveraging both structured graphs and unstructured text.
- Accelerates time-to-value with rapid ingestion, indexing, and deployment.
- Helps organizations uncover hidden insights through comprehensive knowledge graphs and advanced analytics.
Metrics & Outcomes
- 150% Improved Accuracy with HybridRAG (graph-based + vector retrieval).
- 75 hours Reduction in Setup Time vs traditional RAG frameworks.
- 87% Customer Retention Rate among enterprise clients.
- 2.5x Lower Costs through optimized infrastructure.
Use Cases
- Enterprise knowledge bases and document management
- AI assistants that require up-to-date, source-backed information
- Research and regulatory compliance workflows
- Multimodal information retrieval across documents, audio, and more
How to Get Started
- Connect your document store and begin ingesting files.
- Set up access control and collections for your teams.
- Configure hybrid search and retrieval pipelines to match your use case.
- Integrate R2R into your AI applications and start generating context-rich responses.
Safety and Compliance
- Enterprise-grade access control and auditability.
- Designed for data governance, privacy, and compliance across industries.
Core Features
- Universal Document Processing across 40+ formats
- Enterprise Access Control with granular permissions
- Advanced Document Intelligence and Knowledge Graphs
- Hybrid Search combining vector and traditional retrieval
- Seamless, scalable deployment (managed cloud)
- Developer-friendly APIs and SDKs
- Configurable retrieval and generation pipelines
- Multimodal ingestion and rich context enrichment