Data Ingestion & Knowledge Sources
✅ Auto-Indexing – Points at files, indexes unstructured data automatically without manual setup✅ Auto-Sync – Connected repositories sync automatically, document changes reflected almost instantlyFile Formats – Supports PDF, DOCX, PPT, TXT and common enterprise formats⚠️ Limited Scope – No website crawling or YouTube ingestion, narrower than CustomGPTEnterprise Scale – Handles large corporate data sets, exact limits not published
Handles 40 + formats—from PDFs and spreadsheets to audio—at massive scale
Reference .
Async ingest auto-scales, crunching millions of tokens per second—perfect for giant corpora
Benchmark details .
Ingest via code or API, so you can tap proprietary databases or custom pipelines with ease.
1,400+ file formats – PDF, DOCX, Excel, PowerPoint, Markdown, HTML + auto-extraction from ZIP/RAR/7Z archivesWebsite crawling – Sitemap indexing with configurable depth for help docs, FAQs, and public contentMultimedia transcription – AI Vision, OCR, YouTube/Vimeo/podcast speech-to-text built-inCloud integrations – Google Drive, SharePoint, OneDrive, Dropbox, Notion with auto-syncKnowledge platforms – Zendesk, Freshdesk, HubSpot, Confluence, Shopify connectorsMassive scale – 60M words (Standard) / 300M words (Premium) per bot with no performance degradation
⚠️ Standalone Only – Own chat/search interface, not a "deploy everywhere" platform⚠️ No External Channels – No Slack bot, Zapier connector, or public APIWeb/Desktop UI – Users interact through Pyx's interface, minimal third-party chat synergyCustom Integration – Deeper integrations require custom dev work or future updates
Ships a REST RAG API—plug it into websites, mobile apps, internal tools, or even legacy systems.
No off-the-shelf chat widget; you wire up your own front end
API snippet .
Website embedding – Lightweight JS widget or iframe with customizable positioningCMS plugins – WordPress, WIX, Webflow, Framer, SquareSpace native support5,000+ app ecosystem – Zapier connects CRMs, marketing, e-commerce toolsMCP Server – Integrate with Claude Desktop, Cursor, ChatGPT, WindsurfOpenAI SDK compatible – Drop-in replacement for OpenAI API endpointsLiveChat + Slack – Native chat widgets with human handoff capabilities
Conversational Search – Context-aware Q&A over enterprise documents with follow-up questions⚠️ Internal Focus – Designed for knowledge management, no lead capture or human handoffMulti-Language – Likely supports multiple languages, though not a headline feature⚠️ Basic Analytics – Stores chat history, fewer business insights than customer-facing tools
Core RAG engine serves retrieval-grounded answers; hook it to your UI for multi-turn chat.
Multi-lingual if the LLM you pick supports it.
Lead-capture or human handoff flows are yours to build through the API.
✅ #1 accuracy – Median 5/5 in independent benchmarks, 10% lower hallucination than OpenAI✅ Source citations – Every response includes clickable links to original documents✅ 93% resolution rate – Handles queries autonomously, reducing human workload✅ 92 languages – Native multilingual support without per-language config✅ Lead capture – Built-in email collection, custom forms, real-time notifications✅ Human handoff – Escalation with full conversation context preserved
⚠️ Minimal Branding – Logo/color tweaks only, designed as internal tool not white-label⚠️ No Embedding – Standalone interface, no domain-embed or widget options availablePyx UI Only – Look stays "Pyx AI" by design, public branding not supportedSecurity Focus – Emphasis on user management and access controls over theming
Fully bespoke—design any UI you want and skin it to match your brand.
SciPhi focuses on the back end, so front-end look-and-feel is entirely up to you.
Full white-labeling included – Colors, logos, CSS, custom domains at no extra cost2-minute setup – No-code wizard with drag-and-drop interfacePersona customization – Control AI personality, tone, response style via pre-promptsVisual theme editor – Real-time preview of branding changesDomain allowlisting – Restrict embedding to approved sites only
⚠️ Undisclosed Model – Likely GPT-3.5/GPT-4 but exact model not publicly documented⚠️ No Model Selection – Cannot switch LLMs or configure speed vs accuracy tradeoffs⚠️ Single Configuration – Every query uses same model, no toggles or fine-tuningClosed Architecture – Model details, context window, capabilities hidden from users intentionally
LLM-agnostic—GPT-4, Claude, Llama 2, you choose.
Pick, fine-tune, or swap models anytime to balance cost and performance
Model options .
GPT-5.1 models – Latest thinking models (Optimal & Smart variants)GPT-4 series – GPT-4, GPT-4 Turbo, GPT-4o availableClaude 4.5 – Anthropic's Opus available for EnterpriseAuto model routing – Balances cost/performance automaticallyZero API key management – All models managed behind the scenes
Developer Experience ( A P I & S D Ks)
⚠️ No API – No open API or SDKs, everything through Pyx interface⚠️ No Embedding – Cannot integrate into other apps or call programmaticallyClosed Ecosystem – No GitHub examples, community plug-ins, or extensibility optionsTurnkey Only – Great for ready-made tool, limits deep customization or extensions
REST API plus a Python client (R2RClient) handle ingest and query tasks
Docs and GitHub repos offer deep dives and open-source starter code
SciPhi GitHub .
REST API – Full-featured for agents, projects, data ingestion, chat queriesPython SDK – Open-source customgpt-client with full API coveragePostman collections – Pre-built requests for rapid prototypingWebhooks – Real-time event notifications for conversations and leadsOpenAI compatible – Use existing OpenAI SDK code with minimal changes
Real-Time Answers – Serves accurate responses from internal documents, sparse public benchmarksAuto-Sync Freshness – Connected repositories keep retrieval context always current automatically⚠️ Limited Transparency – No anti-hallucination metrics or advanced re-ranking details publishedCompetitive RAG – Likely comparable to standard GPT-based systems on relevance control
Hybrid search (dense + keyword) keeps retrieval fast and sharp.
Knowledge-graph boosts (HybridRAG) drive up to 150 % better accuracy
Sub-second latency—even at enterprise scale.
Sub-second responses – Optimized RAG with vector search and multi-layer cachingBenchmark-proven – 13% higher accuracy, 34% faster than OpenAI Assistants APIAnti-hallucination tech – Responses grounded only in your provided contentOpenGraph citations – Rich visual cards with titles, descriptions, images99.9% uptime – Auto-scaling infrastructure handles traffic spikes
Customization & Flexibility ( Behavior & Knowledge)
✅ Auto-Sync Updates – Knowledge base updated without manual uploads or scheduling⚠️ No Persona Controls – AI voice stays neutral, no tone or behavior customization✅ Access Controls – Strong role-based permissions, admins set document visibility per userClosed Environment – Great for content updates, limited for AI behavior or deployment
Add new sources, tweak retrieval, mix collections—everything’s programmable.
Chain API calls, re-rank docs, or build full agentic flows
Live content updates – Add/remove content with automatic re-indexingSystem prompts – Shape agent behavior and voice through instructionsMulti-agent support – Different bots for different teamsSmart defaults – No ML expertise required for custom behavior
Seat-Based Pricing – ~$30 per user per month, predictable monthly costs✅ Cost-Effective Small Teams – Affordable for teams under 50 users⚠️ Large Team Costs – 100 users = $3,000/month, can scale expensivelyUnlimited Content – Document/token limits not published, gated only by user seatsFree Trial + Enterprise – Hands-on trial available, custom pricing for large deployments
Free tier plus a $25/mo Dev tier for experiments.
Enterprise plans with custom pricing and self-hosting for heavy traffic
Pricing .
Standard: $99/mo – 60M words, 10 botsPremium: $449/mo – 300M words, 100 botsAuto-scaling – Managed cloud scales with demandFlat rates – No per-query charges
✅ GDPR Compliance – Germany-based, implicit EU data protection and regional sovereignty✅ Enterprise Privacy – Data isolated per customer, encrypted in transit and rest✅ No Model Training – Customer data not used for external LLM training✅ Role-Based Access – Built-in controls, admins set document visibility per role⚠️ Limited Certifications – On-prem or SOC 2/ISO 27001/HIPAA not publicly documented
Customer data stays isolated in SciPhi Cloud; self-host for full control.
Standard encryption in transit and at rest; tune self-hosted setups to meet any regulation.
SOC 2 Type II + GDPR – Third-party audited complianceEncryption – 256-bit AES at rest, SSL/TLS in transitAccess controls – RBAC, 2FA, SSO, domain allowlistingData isolation – Never trains on your data
Observability & Monitoring
Basic Stats – User activity, query counts, top-referenced documents for admins⚠️ No Deep Analytics – No conversation analytics dashboards or real-time loggingAdoption Tracking – Useful for usage monitoring, lighter insights than full suitesSet-and-Forget – Minimal monitoring overhead, contact support for issues
Dev dashboard shows real-time logs, latency, and retrieval quality
Dashboard .
Hook into Prometheus, Grafana, or other tools for deep monitoring.
Real-time dashboard – Query volumes, token usage, response timesCustomer Intelligence – User behavior patterns, popular queries, knowledge gapsConversation analytics – Full transcripts, resolution rates, common questionsExport capabilities – API export to BI tools and data warehouses
✅ Direct Support – Email, phone, chat with hands-on onboarding approach⚠️ No Open Community – Closed solution, no plug-ins or user-built extensionsInternal Roadmap – Product updates from Pyx only, no community marketplaceQuick Setup Focus – Emphasizes minimal admin overhead for internal knowledge search
Community help via Discord and GitHub; Enterprise customers get dedicated support
Open-source core encourages community contributions and integrations.
Comprehensive docs – Tutorials, cookbooks, API referencesEmail + in-app support – Under 24hr response timePremium support – Dedicated account managers for Premium/EnterpriseOpen-source SDK – Python SDK, Postman, GitHub examples5,000+ Zapier apps – CRMs, e-commerce, marketing integrations
Additional Considerations
✅ No-Fuss Internal Search – Employees use without coding, simple deployment for teams⚠️ Not Public-Facing – Not ideal for customer chatbots or developer-heavy customizationSiloed Environment – Single AI search environment, not broad extensible platformSimpler Scope – Less flexible than CustomGPT, but faster setup for internal use
Advanced extras like GraphRAG and agentic flows push beyond basic Q&A
Great fit for enterprises needing deeply customized, fully integrated AI solutions.
Time-to-value – 2-minute deployment vs weeks with DIYAlways current – Auto-updates to latest GPT modelsProven scale – 6,000+ organizations, millions of queriesMulti-LLM – OpenAI + Claude reduces vendor lock-in
No- Code Interface & Usability
✅ Straightforward UI – Users log in, ask questions, get answers without coding✅ No-Code Admin – Admins connect data sources, Pyx indexes automaticallyMinimal Customization – UI stays consistent and uncluttered by designInternal Q&A Hub – Perfect for employee use, not external embedding or branding
No no-code UI—built for devs to wire into their own front ends.
Dashboard is utilitarian: good for testing and monitoring, not for everyday business users.
2-minute deployment – Fastest time-to-value in the industryWizard interface – Step-by-step with visual previewsDrag-and-drop – Upload files, paste URLs, connect cloud storageIn-browser testing – Test before deploying to productionZero learning curve – Productive on day one
Market Position – Turnkey internal knowledge search (Germany), not embeddable chatbot platformTarget Customers – Small-mid European teams needing GDPR compliance and simple deploymentKey Competitors – Glean, Guru, Notion AI; not customer-facing chatbots like CustomGPT✅ Advantages – Simple scope, auto-sync, GDPR compliance, ~$30/user/month predictable pricing⚠️ Use Case Fit – Perfect for <50 user teams, not API integrations or public chatbots
Market position – Developer-first RAG infrastructure combining open-source flexibility with managed cloud serviceTarget customers – Dev teams needing high-performance RAG, enterprises requiring millions tokens/second ingestionKey competitors – LangChain/LangSmith, Deepset/Haystack, Pinecone Assistant, custom RAG implementationsCompetitive advantages – HybridRAG (150% accuracy boost), async auto-scaling, 40+ formats, sub-second latencyPricing advantage – Free tier + $25/mo Dev plan; open-source foundation enables cost optimizationUse case fit – Massive document volumes, advanced RAG needs, self-hosting control requirements
Market position – Leading RAG platform balancing enterprise accuracy with no-code usability. Trusted by 6,000+ orgs including Adobe, MIT, Dropbox.Key differentiators – #1 benchmarked accuracy • 1,400+ formats • Full white-labeling included • Flat-rate pricingvs OpenAI – 10% lower hallucination, 13% higher accuracy, 34% fastervs Botsonic/Chatbase – More file formats, source citations, no hidden costsvs LangChain – Production-ready in 2 min vs weeks of development
⚠️ Undisclosed LLM – Likely GPT-3.5/GPT-4 but model details not publicly documented⚠️ No Model Selection – Cannot switch LLMs or choose speed vs accuracy configurations⚠️ Opaque Architecture – Context window size and capabilities not exposed to usersSimplicity Focus – Hides technical complexity, users ask questions and get answers⚠️ No Fine-Tuning – Cannot customize model on domain data for specialized responses
LLM-Agnostic Architecture – GPT-4, GPT-3.5, Claude, Llama 2, and other open-source modelsModel Flexibility – Easy swapping to balance cost/performance without vendor lock-inCustom Support – Configure any LLM via API including fine-tuned or proprietary modelsEmbedding Providers – Multiple embedding options for semantic search and vector generation✅ Full control over temperature, max tokens, and generation parameters
OpenAI – GPT-5.1 (Optimal/Smart), GPT-4 seriesAnthropic – Claude 4.5 Opus/Sonnet (Enterprise)Auto-routing – Intelligent model selection for cost/performanceManaged – No API keys or fine-tuning required
Conversational RAG – Context-aware search over enterprise documents with follow-up support✅ Auto-Sync – Repositories sync automatically, changes reflected almost instantlyDocument Formats – PDF, DOCX, PPT, TXT and common enterprise formats supported⚠️ No Advanced Controls – Chunking, embedding models, similarity thresholds not exposed⚠️ Limited Transparency – No citation metrics or anti-hallucination details publishedClosed System – Optimized for internal Q&A, limited visibility into retrieval architecture
HybridRAG Technology – Vector search + knowledge graphs for 150% accuracy improvementHybrid Search – Dense vector + keyword with reciprocal rank fusionAgentic RAG – Reasoning agent for autonomous research across documents and webMultimodal Ingestion – 40+ formats (PDFs, spreadsheets, audio) at massive scale✅ Millions of tokens/second async auto-scaling ingestion throughput
✅ Sub-second latency even at enterprise scale with optimized operations
GPT-4 + RAG – Outperforms OpenAI in independent benchmarksAnti-hallucination – Responses grounded in your content onlyAutomatic citations – Clickable source links in every responseSub-second latency – Optimized vector search and cachingScale to 300M words – No performance degradation at scale
✅ Internal Knowledge Search – Employees asking questions about company documents and policies✅ Team Onboarding – New hires finding information without bothering colleagues✅ Policy Lookup – HR, compliance, operational procedure retrieval for staff✅ Small European Teams – GDPR-compliant internal search with EU data residency⚠️ NOT SUITABLE FOR – Public chatbots, customer support, API integrations, multi-channel deployment
Enterprise Knowledge – Process millions of documents with knowledge graph relationshipsSupport Automation – RAG-powered support bots with accurate, grounded responsesResearch & Analysis – Agentic RAG for autonomous research across collections and webCompliance & Legal – Large document repositories with precise citation trackingInternal Docs – Developer-focused RAG for code, API references, technical knowledgeCustom AI Apps – API-first architecture integrates into any application or workflow
Customer support – 24/7 AI handling common queries with citationsInternal knowledge – HR policies, onboarding, technical docsSales enablement – Product info, lead qualification, educationDocumentation – Help centers, FAQs with auto-crawlingE-commerce – Product recommendations, order assistance
✅ GDPR Compliance – Germany-based with implicit EU data protection compliance✅ German Data Residency – EU storage location for regional data sovereignty requirements✅ Enterprise Privacy – Customer data isolated, encrypted in transit and at rest✅ Role-Based Access – Built-in controls, admins set document visibility per user⚠️ Limited Certifications – SOC 2, ISO 27001, HIPAA not publicly documented
Data Isolation – Single-tenant architecture with isolated customer data in SciPhi CloudSelf-Hosting Option – On-premise deployment for complete data control in regulated industriesEncryption Standards – TLS in transit, AES-256 at rest encryptionAccess Controls – Document-level granular permissions with role-based access control (RBAC)✅ Open-source R2R core enables security audits and compliance validation
✅ Self-hosted deployments tunable for HIPAA, SOC 2, and other regulations
SOC 2 Type II + GDPR – Regular third-party audits, full EU compliance256-bit AES encryption – Data at rest; SSL/TLS in transitSSO + 2FA + RBAC – Enterprise access controls with role-based permissionsData isolation – Never trains on customer dataDomain allowlisting – Restrict chatbot to approved domains
Seat-Based Pricing – ~$30 per user per month✅ Small Team Value – Affordable for teams under 50 users, predictable costs⚠️ Scalability Cost – 100 users = $3,000/month, expensive for large organizationsUnlimited Content – No published document limits, gated only by user seatsFree Trial + Enterprise – Evaluation available, custom pricing for volume discounts
Free Tier – Generous no-credit-card tier for experimentation and developmentDeveloper Plan – $25/month for individual developers and small projectsEnterprise Plans – Custom pricing based on scale, features, and supportSelf-Hosting – Open-source R2R available free (infrastructure costs only)✅ Flat subscription pricing without per-query or per-document charges
✅ Managed cloud handles infrastructure, deployment, scaling, updates, maintenance
Standard: $99/mo – 10 chatbots, 60M words, 5K items/botPremium: $449/mo – 100 chatbots, 300M words, 20K items/botEnterprise: Custom – SSO, dedicated support, custom SLAs7-day free trial – Full Standard access, no chargesFlat-rate pricing – No per-query charges, no hidden costs
✅ Direct Support – Email, phone, chat with hands-on onboarding approach✅ Quick Deployment – Minimal admin overhead, connect sources and start asking questions⚠️ No Open Community – Closed solution, no plug-ins or user extensions⚠️ No Developer Docs – No API documentation or programmatic access guidesInternal Roadmap – Updates from Pyx only, no user-contributed features
Comprehensive Docs – Detailed docs at r2r-docs.sciphi.ai covering all features and endpointsGitHub Repository – Active open-source development at github.com/SciPhi-AI/R2R with code examplesCommunity Support – Discord community and GitHub issues for peer supportEnterprise Support – Dedicated channels for enterprise customers with SLAs✅ Python client (R2RClient) with extensive examples and starter code
✅ Developer dashboard with real-time logs, latency, and retrieval quality metrics
Documentation hub – Docs, tutorials, API referencesSupport channels – Email, in-app chat, dedicated managers (Premium+)Open-source – Python SDK, Postman, GitHub examplesCommunity – User community + 5,000 Zapier integrations
Limitations & Considerations
⚠️ No Public API – Cannot embed or call programmatically, standalone UI only⚠️ No Messaging Integrations – No Slack, Teams, WhatsApp or chat platform connectors⚠️ Limited Branding – Minimal customization, not white-label solution for public deployment⚠️ No Advanced Controls – Cannot configure RAG parameters, model selection, retrieval strategies⚠️ Seat-Based Scaling – Expensive for large orgs vs usage-based pricing models✅ Best For – Small European teams (<50 users) prioritizing simplicity and GDPR over flexibility
⚠️ Developer-Focused – Requires technical expertise to build and wire custom front ends
⚠️ Infrastructure Requirements – Self-hosting needs GPU infrastructure and DevOps expertise
⚠️ Integration Effort – API-first design means building your own chat UI
⚠️ Learning Curve – Advanced features like knowledge graphs require RAG concept understanding
⚠️ Community Support Limits – Open-source support relies on community unless enterprise plan
Managed service – Less control over RAG pipeline vs build-your-ownModel selection – OpenAI + Anthropic only; no Cohere, AI21, open-sourceReal-time data – Requires re-indexing; not ideal for live inventory/pricesEnterprise features – Custom SSO only on Enterprise plan
⚠️ NO Agent Capabilities – No autonomous agents, tool calling, or multi-agent orchestrationConversational Search Only – Context-aware dialogue for Q&A, not agentic or autonomous behaviorBasic RAG Architecture – Standard retrieval without function calling, tool use, or workflows⚠️ No External Actions – Cannot invoke APIs, execute code, query databases, or interact externallyInternal Knowledge Focus – Employee Q&A about documents, not task automation or workflows
Agentic RAG – Reasoning agent for autonomous research across documents/web with multi-step problem solvingAdvanced Toolset – Semantic search, metadata search, document retrieval, web search, web scraping capabilitiesMulti-Turn Context – Stateful dialogues maintaining conversation history via conversation_id for follow-upsCitation Transparency – Detailed responses with source citations for fact-checking and verification⚠️ No Pre-Built UI – API-first platform requires custom front-end development
⚠️ No Lead Analytics – Lead capture and dashboards must be implemented at application layer
Custom AI Agents – Autonomous GPT-4/Claude agents for business tasksMulti-Agent Systems – Specialized agents for support, sales, knowledgeMemory & Context – Persistent conversation history across sessionsTool Integration – Webhooks + 5,000 Zapier apps for automationContinuous Learning – Auto re-indexing without manual retraining
R A G-as-a- Service Assessment
⚠️ NOT TRUE RAG-AS-A-SERVICE – Standalone internal app, not API-accessible RAG platformTurnkey Application – Self-contained Q&A tool vs developer-accessible RAG infrastructure⚠️ No API Access – No REST API, SDKs, programmatic access unlike CustomGPT/VectaraClosed Application – Web/desktop interface only, cannot build custom applications on topSaaS vs RaaS – Software-as-a-Service (standalone app) NOT Retrieval-as-a-Service (API infrastructure)Best Comparison Category – Internal search tools (Glean, Guru), not developer RAG platforms
Platform Type – HYBRID RAG-AS-A-SERVICE combining open-source R2R with managed SciPhi CloudCore Mission – Bridge experimental RAG models to production-ready systems with deployment flexibilityDeveloper Target – Built for OSS community, startups, enterprises emphasizing developer flexibility and controlRAG Leadership – HybridRAG (150% accuracy), millions tokens/second, 40+ formats, sub-second latency✅ Open-source R2R core on GitHub enables customization, portability, avoids vendor lock-in
⚠️ NO no-code features – No chat widgets, visual builders, pre-built integrations or dashboards
Platform type – TRUE RAG-AS-A-SERVICE with managed infrastructureAPI-first – REST API, Python SDK, OpenAI compatibility, MCP ServerNo-code option – 2-minute wizard deployment for non-developersHybrid positioning – Serves both dev teams (APIs) and business users (no-code)Enterprise ready – SOC 2 Type II, GDPR, WCAG 2.0, flat-rate pricing
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