In this comprehensive guide, we compare Azumo and SciPhi across various parameters including features, pricing, performance, and customer support to help you make the best decision for your business needs.
Overview
When choosing between Azumo and SciPhi, understanding their unique strengths and architectural differences is crucial for making an informed decision. Both platforms serve the RAG (Retrieval-Augmented Generation) space but cater to different use cases and organizational needs.
Quick Decision Guide
Choose Azumo if: you value highly skilled nearshore developers in same timezone
Choose SciPhi if: you value state-of-the-art retrieval accuracy
About Azumo
Azumo is top-rated nearshore ai development services for custom solutions. Azumo is a leading nearshore software development company specializing in custom AI and machine learning solutions, offering dedicated teams and enterprise-grade development services for businesses looking to build intelligent applications. Founded in 2016, headquartered in San Francisco, CA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
92/100
Starting Price
$100000/mo
About SciPhi
SciPhi is the most advanced ai retrieval system. R2R is a production-ready AI retrieval system supporting Retrieval-Augmented Generation with advanced features including multimodal ingestion, hybrid search, knowledge graphs, and a Deep Research API for multi-step reasoning across documents and the web. Founded in 2023, headquartered in San Francisco, CA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
89/100
Starting Price
Custom
Key Differences at a Glance
In terms of user ratings, both platforms score similarly in overall satisfaction. From a cost perspective, SciPhi offers more competitive entry pricing. The platforms also differ in their primary focus: AI Development versus RAG Platform. These differences make each platform better suited for specific use cases and organizational requirements.
⚠️ What This Comparison Covers
We'll analyze features, pricing, performance benchmarks, security compliance, integration capabilities, and real-world use cases to help you determine which platform best fits your organization's needs. All data is independently verified from official documentation and third-party review platforms.
Detailed Feature Comparison
Azumo
SciPhi
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Builds custom ETL pipelines that pull data from your proprietary systems, internal wikis, SharePoint, and cloud storage—so everything ends up in one place.
Works with both unstructured sources—PDFs, HTML, even multimedia—and structured data like databases or spreadsheets, bringing it all together into a single knowledge index.
Learn more
Stores and indexes your content in vector databases such as Pinecone or Weaviate, giving you the flexibility to handle domain-specific data.
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.
Lets you ingest more than 1,400 file formats—PDF, DOCX, TXT, Markdown, HTML, and many more—via simple drag-and-drop or API.
Crawls entire sites through sitemaps and URLs, automatically indexing public help-desk articles, FAQs, and docs.
Turns multimedia into text on the fly: YouTube videos, podcasts, and other media are auto-transcribed with built-in OCR and speech-to-text.
View Transcription Guide
Connects to Google Drive, SharePoint, Notion, Confluence, HubSpot, and more through API connectors or Zapier.
See Zapier Connectors
Supports both manual uploads and auto-sync retraining, so your knowledge base always stays up to date.
Integrations & Channels
Specializes in bespoke integrations: Azumo can craft custom connectors for your enterprise tools—CRM, ERP, or even internal intranets.
Puts AI agents wherever your users are—web, mobile, Slack, Microsoft Teams—through custom interfaces and API wrappers.
Integration services
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.
Embeds easily—a lightweight script or iframe drops the chat widget into any website or mobile app.
Offers ready-made hooks for Slack, Zendesk, Confluence, YouTube, Sharepoint, 100+ more.
Explore API Integrations
Connects with 5,000+ apps via Zapier and webhooks to automate your workflows.
Supports secure deployments with domain allowlisting and a ChatGPT Plugin for private use cases.
Hosted CustomGPT.ai offers hosted MCP Server with support for Claude Web, Claude Desktop, Cursor, ChatGPT, Windsurf, Trae, etc.
Read more here.
Builds RAG agents that focus on context-rich, accurate answers by pairing advanced relevancy search with thoughtful prompt engineering.
Supports multi-turn conversations with context retention and clear source attribution to bolster trust.
See their approach
Handles complex multi-agent systems and multi-step reasoning whenever the business case calls for it.
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.
Reduces hallucinations by grounding replies in your data and adding source citations for transparency.
Benchmark Details
Handles multi-turn, context-aware chats with persistent history and solid conversation management.
Speaks 90+ languages, making global rollouts straightforward.
Includes extras like lead capture (email collection) and smooth handoff to a human when needed.
Customization & Branding
Gives you unlimited room to customize—from the agent’s persona and tone to a fully branded UI—through bespoke development.
Works side-by-side with your team to match brand voice, greetings, fonts, colors, and layouts.
Learn about branding
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.
Fully white-labels the widget—colors, logos, icons, CSS, everything can match your brand.
White-label Options
Provides a no-code dashboard to set welcome messages, bot names, and visual themes.
Lets you shape the AI’s persona and tone using pre-prompts and system instructions.
Uses domain allowlisting to ensure the chatbot appears only on approved sites.
L L M Model Options
Takes a model-agnostic stance, integrating whichever model best fits your project—OpenAI's GPT, Anthropic's Claude, Meta's LLaMA, Cohere, or open-source alternatives.
Pick, fine-tune, or swap models anytime to balance cost and performance
Model options.
Taps into top models—OpenAI’s GPT-4, GPT-3.5 Turbo, and even Anthropic’s Claude for enterprise needs.
Automatically balances cost and performance by picking the right model for each request.
Model Selection Details
Uses proprietary prompt engineering and retrieval tweaks to return high-quality, citation-backed answers.
Handles all model management behind the scenes—no extra API keys or fine-tuning steps for you.
Developer Experience ( A P I & S D Ks)
Delivers a tailor-made API or microservice that meets your integration needs—no off-the-shelf SDKs, just code built for you.
Collaborates closely on endpoint design, using frameworks like LangChain or Haystack internally, and hands over clear docs and code reviews on delivery.
See development process
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.
Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat.
API Documentation
Lets you build multiple datastores, set role-based access, and tweak system prompts so the agent behaves exactly as you want.
Makes continuous refinement easy—add new training data, tune prompts, or plug in custom logic for tricky queries.
Customization approach
Add new sources, tweak retrieval, mix collections—everything’s programmable.
Chain API calls, re-rank docs, or build full agentic flows
Lets you add, remove, or tweak content on the fly—automatic re-indexing keeps everything current.
Shapes agent behavior through system prompts and sample Q&A, ensuring a consistent voice and focus.
Learn How to Update Sources
Supports multiple agents per account, so different teams can have their own bots.
Balances hands-on control with smart defaults—no deep ML expertise required to get tailored behavior.
Pricing & Scalability
Uses a bespoke, project-based pricing model—costs scale with scope, complexity, and timeline, so expect a higher upfront investment than a typical SaaS subscription.
Pricing overview
Architected for enterprise scale: as query volume and data grow, the infrastructure scales right along with you.
Free tier plus a $25/mo Dev tier for experiments.
Enterprise plans with custom pricing and self-hosting for heavy traffic
Pricing.
Runs on straightforward subscriptions: Standard (~$99/mo), Premium (~$449/mo), and customizable Enterprise plans.
Gives generous limits—Standard covers up to 60 million words per bot, Premium up to 300 million—all at flat monthly rates.
View Pricing
Handles scaling for you: the managed cloud infra auto-scales with demand, keeping things fast and available.
Security & Privacy
Offers the choice of on-prem or VPC deployments for full data sovereignty.
Implements enterprise-grade encryption, granular access controls, and compliance measures (HIPAA, FINRA, and more) tailored to your industry.
Learn about security
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.
Protects data in transit with SSL/TLS and at rest with 256-bit AES encryption.
Holds SOC 2 Type II certification and complies with GDPR, so your data stays isolated and private.
Security Certifications
Offers fine-grained access controls—RBAC, two-factor auth, and SSO integration—so only the right people get in.
Observability & Monitoring
Bakes in comprehensive logging and monitoring—tracking query performance, retrieval success, and response times out of the box.
Can tie into your monitoring stack (Splunk, CloudWatch, etc.) for real-time alerts and KPI-driven analytics.
Monitoring capabilities
Dev dashboard shows real-time logs, latency, and retrieval quality
Dashboard.
Hook into Prometheus, Grafana, or other tools for deep monitoring.
Comes with a real-time analytics dashboard tracking query volumes, token usage, and indexing status.
Lets you export logs and metrics via API to plug into third-party monitoring or BI tools.
Analytics API
Provides detailed insights for troubleshooting and ongoing optimization.
Support & Ecosystem
Provides white-glove support with a dedicated account manager and direct access to the dev team during and after deployment.
Support details
Leverages a broad technology network—including partnerships like Snowflake—and deep expertise across multiple AI platforms.
Community help via Discord and GitHub; Enterprise customers get dedicated support
Open-source core encourages community contributions and integrations.
Supplies rich docs, tutorials, cookbooks, and FAQs to get you started fast.
Developer Docs
Offers quick email and in-app chat support—Premium and Enterprise plans add dedicated managers and faster SLAs.
Enterprise Solutions
Benefits from an active user community plus integrations through Zapier and GitHub resources.
Core Agent Features
Custom RAG Agents: Builds context-rich, accurate answers by pairing advanced relevancy search with thoughtful prompt engineering tailored to specific business needs
Multi-Turn Conversations: Supports conversation context retention and clear source attribution to bolster trust across multi-step interactions
Conversation approach
Multi-Agent Systems: Handles complex multi-agent orchestration and multi-step reasoning when business case demands coordination across specialized agents
Voice & Text Capabilities: Can implement voice agents, text chatbots, or hybrid solutions depending on channel requirements and use case specifications
Custom Analytics: Performance monitoring, query tracking, response time metrics integrated with client monitoring stacks (Splunk, CloudWatch) for KPI-driven insights
Lead Capture & CRM: Custom integration with enterprise CRM systems (Salesforce, HubSpot, Microsoft Dynamics) for lead qualification and contact management
Human Handoff: Configurable escalation logic with full conversation context transfer to human agents when AI confidence drops below thresholds or complex queries detected
Workflow Automation: Connects with enterprise tools (ERP, CRM, internal intranets) for complex multi-step workflows beyond simple Q&A retrieval
Proprietary System Integration: Builds custom connectors for legacy systems, internal databases, and proprietary data sources without published APIs
Bespoke Development: All features custom-built to specifications - no off-the-shelf limitations on functionality or integration capabilities
Agentic RAG: Reasoning agent integrated with retrieval for autonomous research across documents and web with multi-step problem solving
Conversational Interface: Complex information retrieval maintaining context across multiple interactions via conversation_id for stateful dialogues
Multi-Turn Context Management: Agent remembers previous interactions and builds upon conversation history for follow-up questions
Deep Research API: Multi-step reasoning system fetching data from knowledgebase and/or internet for rich, context-aware answers to complex queries
Tool Orchestration: Dynamic tool invocation with intelligent routing based on query characteristics and context requirements
Citation Transparency: Detailed responses with citations to source material for fact-checking and verification
LIMITATION - No Pre-Built Chat UI: API-first platform requiring developers to build custom conversational interfaces - not a turnkey chatbot solution
LIMITATION - No Lead Capture/Analytics: Focuses on knowledge retrieval infrastructure - lead generation, dashboards, and human handoff must be implemented at application layer
Custom AI Agents: Build autonomous agents powered by GPT-4 and Claude that can perform tasks independently and make real-time decisions based on business knowledge
Decision-Support Capabilities: AI agents analyze proprietary data to provide insights, recommendations, and actionable responses specific to your business domain
Multi-Agent Systems: Deploy multiple specialized AI agents that can collaborate and optimize workflows in areas like customer support, sales, and internal knowledge management
Memory & Context Management: Agents maintain conversation history and persistent context for coherent multi-turn interactions
View Agent Documentation
Tool Integration: Agents can trigger actions, integrate with external APIs via webhooks, and connect to 5,000+ apps through Zapier for automated workflows
Hyper-Accurate Responses: Leverages advanced RAG technology and retrieval mechanisms to deliver context-aware, citation-backed responses grounded in your knowledge base
Continuous Learning: Agents improve over time through automatic re-indexing of knowledge sources and integration of new data without manual retraining
R A G-as-a- Service Assessment
Platform Classification: CUSTOM AI DEVELOPMENT AGENCY, NOT a self-service RAG platform - delivers bespoke RAG solutions vs providing standardized API service
Architecture Philosophy: Full custom implementation from scratch vs plug-and-play API consumption - requires development partnership not subscription
Target Audience: Enterprises with complex, mission-critical requirements and dedicated budgets ($10K+ minimum) vs developers seeking instant API access
Agentic RAG Capabilities: Implements cutting-edge agentic RAG with multi-agent reasoning, self-validation, real-time orchestration between retrievers/planners/verifiers
Agentic RAG approach
Code Ownership: Clients own delivered code and infrastructure enabling complete control, modification rights, and independent maintenance post-delivery
Deployment Flexibility: On-premise, VPC, cloud-agnostic options for complete data sovereignty vs SaaS vendor lock-in
Developer Experience: Tailor-made APIs and microservices designed for specific integration needs - no generic SDKs but custom endpoints with comprehensive documentation
Implementation Timeline: Weeks to months for delivery vs instant API access - requires discovery, design, development, testing, deployment phases
Ongoing Support: Professional services model with dedicated account manager and direct development team access vs community forums or ticketing systems
Cost Structure: Project-based pricing ($10K-$70K+ range) vs monthly subscription - higher upfront but includes customization, deployment, training
Use Case Fit: Ideal for enterprises needing custom RAG for legacy systems, specialized workflows, compliance requirements; poor fit for rapid prototyping or simple chatbot deployments
Platform Type: HYBRID RAG-AS-A-SERVICE - combines open-source R2R framework with SciPhi Cloud managed service for enterprise deployments
Core Mission: Bridge gap between experimental RAG models and production-ready systems with straightforward path to deploy, adapt, and maintain RAG pipelines
Developer Target Market: Built by and for OSS community to help startups and enterprises quickly build with RAG - emphasizes developer flexibility and control
Deployment Flexibility: Free tier + $25/month Dev tier, Enterprise plans with custom pricing and self-hosting options - unique among RAG platforms for offering both managed and on-premise
RAG Technology Leadership: HybridRAG (knowledge graph boosting for 150% accuracy improvement), async auto-scaling to millions of tokens/second, 40+ format support including audio at massive scale, sub-second latency
Open-Source Advantage: Complete transparency with R2R core on GitHub, enables customization and portability, avoids vendor lock-in while offering managed cloud option
Enterprise Features: Multimodal ingestion, agentic RAG with reasoning agents, document-level security, comprehensive observability, customer-managed encryption for self-hosted deployments
API-First Architecture: REST API + Python client (R2RClient) with extensive documentation, sample code, GitHub repos for deep integration control
LIMITATION vs No-Code Platforms: NO native chat widgets, Slack/WhatsApp integrations, visual agent builders, or pre-built analytics dashboards - developer-first approach requires technical resources
Comparison Validity: Architectural comparison to CustomGPT.ai is VALID but highlights different priorities - SciPhi developer infrastructure with self-hosting vs CustomGPT likely more accessible no-code deployment
Use Case Fit: Enterprises processing massive document volumes requiring async auto-scaling, development teams needing advanced RAG (HybridRAG, knowledge graphs) for accuracy improvements, organizations wanting open-source foundation with self-hosting for complete control
NOT Ideal For: Non-technical teams requiring no-code chatbot builders, businesses needing immediate deployment without developer involvement, organizations seeking turnkey UI widgets and integrations
Core Architecture: Serverless RAG infrastructure with automatic embedding generation, vector search optimization, and LLM orchestration fully managed behind API endpoints
API-First Design: Comprehensive REST API with well-documented endpoints for creating agents, managing projects, ingesting data (1,400+ formats), and querying chat
API Documentation
Developer Experience: Open-source Python SDK (customgpt-client), Postman collections, OpenAI API endpoint compatibility, and extensive cookbooks for rapid integration
No-Code Alternative: Wizard-style web dashboard enables non-developers to upload content, brand widgets, and deploy chatbots without touching code
Hybrid Target Market: Serves both developer teams wanting robust APIs AND business users seeking no-code RAG deployment - unique positioning vs pure API platforms (Cohere) or pure no-code tools (Jotform)
RAG Technology Leadership: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, proprietary anti-hallucination mechanisms, and citation-backed responses
Benchmark Details
Deployment Flexibility: Cloud-hosted SaaS with auto-scaling, API integrations, embedded chat widgets, ChatGPT Plugin support, and hosted MCP Server for Claude/Cursor/ChatGPT
Enterprise Readiness: SOC 2 Type II + GDPR compliance, full white-labeling, domain allowlisting, RBAC with 2FA/SSO, and flat-rate pricing without per-query charges
Use Case Fit: Ideal for organizations needing both rapid no-code deployment AND robust API capabilities, teams handling diverse content types (1,400+ formats, multimedia transcription), and businesses requiring production-ready RAG without building ML infrastructure from scratch
Competitive Positioning: Bridges the gap between developer-first platforms (Cohere, Deepset) requiring heavy coding and no-code chatbot builders (Jotform, Kommunicate) lacking API depth - offers best of both worlds
Additional Considerations
Perfect for organizations that need a custom, mission-critical AI solution that integrates with legacy systems or runs complex multi-step workflows.
You own the delivered code and system, giving you ultimate flexibility to maintain or extend it later.
Custom development approach
Expect a higher initial investment and a longer rollout compared with off-the-shelf SaaS tools.
Advanced extras like GraphRAG and agentic flows push beyond basic Q&A
Great fit for enterprises needing deeply customized, fully integrated AI solutions.
Slashes engineering overhead with an all-in-one RAG platform—no in-house ML team required.
Gets you to value quickly: launch a functional AI assistant in minutes.
Stays current with ongoing GPT and retrieval improvements, so you’re always on the latest tech.
Balances top-tier accuracy with ease of use, perfect for customer-facing or internal knowledge projects.
No- Code Interface & Usability
Doesn't come with a ready-made no-code interface—any admin or user UI is built as part of the custom solution.
While the final UI can be polished and user-friendly, non-developers will generally need developer help for changes.
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.
Offers a wizard-style web dashboard so non-devs can upload content, brand the widget, and monitor performance.
Supports drag-and-drop uploads, visual theme editing, and in-browser chatbot testing.
User Experience Review
Uses role-based access so business users and devs can collaborate smoothly.
Competitive Positioning
Market position: Premium custom AI development agency specializing in bespoke RAG and AI agent solutions for enterprises with complex, mission-critical requirements
Target customers: Large enterprises and regulated industries (HIPAA, FINRA) needing fully customized AI solutions that integrate with legacy systems and proprietary infrastructure
Key competitors: Deviniti, Contextual.ai (enterprise RAG), Azure AI, OpenAI (enterprise offerings), and internal AI development teams
Competitive advantages: Model-agnostic flexibility, white-glove support with dedicated dev teams, full code ownership, on-prem/VPC deployment options for data sovereignty, and deep expertise across multiple AI platforms including Snowflake partnerships
Pricing advantage: Higher upfront investment than SaaS solutions but provides long-term ownership without recurring subscription costs; best value for organizations with unique, complex requirements that can't be met by off-the-shelf tools
Use case fit: Ideal when you need custom integrations with legacy systems, specialized multi-step workflows, domain-specific fine-tuning, or compliance requirements that demand on-premises deployment and full data control
Market position: Developer-first RAG infrastructure (R2R framework) combining open-source flexibility with managed cloud service, specializing in enterprise-scale performance and advanced RAG techniques
Target customers: Development teams building high-performance RAG applications, enterprises requiring massive-scale ingestion (millions of tokens/second), and organizations wanting HybridRAG with knowledge graph capabilities for 150% accuracy improvements
Key competitors: LangChain/LangSmith, Deepset/Haystack, Pinecone Assistant, and custom RAG implementations
Competitive advantages: Async ingest auto-scaling to millions of tokens/second, 40+ format support including audio at massive scale, HybridRAG with knowledge-graph boosting (up to 150% better accuracy), sub-second latency even at enterprise scale, LLM-agnostic with easy model swapping (GPT-4, Claude, Llama 2), open-source R2R core for transparency and portability, and self-hosting options for complete control
Pricing advantage: Free tier plus $25/month Dev tier for experiments; enterprise plans with custom pricing and self-hosting; open-source foundation enables cost savings for teams with infrastructure expertise; best value for high-volume applications requiring enterprise-scale performance
Use case fit: Perfect for enterprises processing massive document volumes requiring async auto-scaling ingestion, development teams needing advanced RAG techniques (HybridRAG, knowledge graphs) for accuracy improvements, and organizations wanting open-source foundation with option to self-host for complete control and cost optimization
Market position: Leading all-in-one RAG platform balancing enterprise-grade accuracy with developer-friendly APIs and no-code usability for rapid deployment
Target customers: Mid-market to enterprise organizations needing production-ready AI assistants, development teams wanting robust APIs without building RAG infrastructure, and businesses requiring 1,400+ file format support with auto-transcription (YouTube, podcasts)
Key competitors: OpenAI Assistants API, Botsonic, Chatbase.co, Azure AI, and custom RAG implementations using LangChain
Competitive advantages: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, SOC 2 Type II + GDPR compliance, full white-labeling included, OpenAI API endpoint compatibility, hosted MCP Server support (Claude, Cursor, ChatGPT), generous data limits (60M words Standard, 300M Premium), and flat monthly pricing without per-query charges
Pricing advantage: Transparent flat-rate pricing at $99/month (Standard) and $449/month (Premium) with generous included limits; no hidden costs for API access, branding removal, or basic features; best value for teams needing both no-code dashboard and developer APIs in one platform
Use case fit: Ideal for businesses needing both rapid no-code deployment and robust API capabilities, organizations handling diverse content types (1,400+ formats, multimedia transcription), teams requiring white-label chatbots with source citations for customer-facing or internal knowledge projects, and companies wanting all-in-one RAG without managing ML infrastructure
A I Models
Primary models: Model-agnostic approach supporting GPT-4, GPT-3.5, Claude 3.5, Gemini, Meta LLaMA 3.3, Qwen 2.5, Cohere, and open-source alternatives
Model selection: Custom selection determined during discovery phase with Azumo development team based on project requirements and use case
Fine-tuning capabilities: Domain-specific model fine-tuning using efficient, scalable techniques on curated and annotated datasets reflecting real business environments
Model switching: Not self-service - model configuration determined by professional services team during implementation
Provider relationships: Works with top LLM providers including OpenAI, Anthropic, Google DeepMind, Meta, DeepSeek, xAI, and Mistral
LLM-Agnostic Architecture: Supports GPT-4, GPT-3.5-turbo, Claude (Anthropic), Llama 2, and other open-source models
Model Flexibility: Easy model swapping to balance cost and performance without vendor lock-in
Custom Model Support: Configure any LLM via API, including fine-tuned or proprietary models
Embedding Models: Supports multiple embedding providers for semantic search and vector generation
Model Configuration: Full control over temperature, max tokens, and other generation parameters
Primary models: GPT-4, GPT-3.5 Turbo from OpenAI, and Anthropic's Claude for enterprise needs
Automatic model selection: Balances cost and performance by automatically selecting the appropriate model for each request
Model Selection Details
Proprietary optimizations: Custom prompt engineering and retrieval enhancements for high-quality, citation-backed answers
Managed infrastructure: All model management handled behind the scenes - no API keys or fine-tuning required from users
Anti-hallucination technology: Advanced mechanisms ensure chatbot only answers based on provided content, improving trust and factual accuracy
R A G Capabilities
Vector databases: Integration with Pinecone, Weaviate, Qdrant, and other leading vector database solutions for domain-specific data handling
Chunking strategy: Semantic chunking breaks documents into meaningful sections by topic/intent rather than fixed-size pieces; chunk size depends on content type (paragraph-sized for FAQs, larger with overlap for narratives)
Retrieval methods: Advanced relevancy search with reranking to keep only most relevant context; optimization of retrieval components for high accuracy
Context window: Leverages 128k token context windows for large document processing and complex queries
Pipeline optimization: Complete RAG pipeline including chunking, embedding, vector search, reranking, and answer generation with citations
HybridRAG Technology: Combines vector search with knowledge graphs for up to 150% accuracy improvement over traditional RAG
Hybrid Search: Dense vector retrieval + keyword search with reciprocal rank fusion for optimal precision
Knowledge Graph Extraction: Automatic entity and relationship mapping enriches context across documents
Agentic RAG: Reasoning agent integrated with retrieval for autonomous research across documents and web
Multimodal Ingestion: Process 40+ formats including PDFs, spreadsheets, audio files at massive scale
Async Auto-Scaling: Millions of tokens per second ingestion throughput for enterprise document volumes
Sub-Second Latency: Fast retrieval even at enterprise scale with optimized vector operations
Core architecture: GPT-4 combined with Retrieval-Augmented Generation (RAG) technology, outperforming OpenAI in RAG benchmarks
RAG Performance
Anti-hallucination technology: Advanced mechanisms reduce hallucinations and ensure responses are grounded in provided content
Benchmark Details
Automatic citations: Each response includes clickable citations pointing to original source documents for transparency and verification
Optimized pipeline: Efficient vector search, smart chunking, and caching for sub-second reply times
Scalability: Maintains speed and accuracy for massive knowledge bases with tens of millions of words
Context-aware conversations: Multi-turn conversations with persistent history and comprehensive conversation management
Source verification: Always cites sources so users can verify facts on the spot
Enterprise applications: Custom ETL pipelines for proprietary systems, internal wiki integration, SharePoint connectors, multi-step reasoning agents, complex multi-agent systems
Ideal team sizes: Large enterprises with dedicated development teams; projects typically involve teams of 1-15 Azumo members working alongside client teams
Common implementations: Legacy system modernization, SQL Server to Azure migrations, health screening platforms, real-time AI agent assistance with CRM system integration and automated reporting
Deployment timeline: 12-18 month pilot phases common before company-wide rollout; implementations take longer than SaaS solutions but deliver mission-critical custom capabilities
Enterprise Knowledge Management: Process and search across millions of documents with knowledge graph relationships
Customer Support Automation: Build RAG-powered support bots with accurate, grounded responses
Research & Analysis: Agentic RAG capabilities for autonomous research across document collections and web
Compliance & Legal: Search and analyze large document repositories with precise citation tracking
Internal Documentation: Developer-focused RAG for code documentation, API references, and technical knowledge bases
Custom AI Applications: API-first architecture enables integration into any custom application or workflow
Customer support automation: AI assistants handling common queries, reducing support ticket volume, providing 24/7 instant responses with source citations
Internal knowledge management: Employee self-service for HR policies, technical documentation, onboarding materials, company procedures across 1,400+ file formats
Sales enablement: Product information chatbots, lead qualification, customer education with white-labeled widgets on websites and apps
Documentation assistance: Technical docs, help centers, FAQs with automatic website crawling and sitemap indexing
Educational platforms: Course materials, research assistance, student support with multimedia content (YouTube transcriptions, podcasts)
Healthcare information: Patient education, medical knowledge bases (SOC 2 Type II compliant for sensitive data)
E-commerce: Product recommendations, order assistance, customer inquiries with API integration to 5,000+ apps via Zapier
SaaS onboarding: User guides, feature explanations, troubleshooting with multi-agent support for different teams
Security & Compliance
Certifications: HIPAA with Business Associate Agreement (BAA) capability, FINRA compliance for financial services, GDPR compliance for EU data protection
Deployment options: On-premise or VPC deployments for full data sovereignty and control; cloud-agnostic architecture
Encryption: Enterprise-grade encryption at rest and in transit; granular access controls and role-based permissions
Data retention: Custom data retention policies tailored to industry requirements and compliance mandates
Monitoring: Comprehensive logging and monitoring tied to client monitoring stacks (Splunk, CloudWatch, etc.) for real-time alerts and KPI-driven analytics
Vulnerability management: Continuous security scanning and threat detection for production systems
Data Isolation: Customer data stays isolated in SciPhi Cloud with single-tenant architecture
Self-Hosting Option: Complete data control with on-premise deployment for regulated industries
Encryption Standards: Data encrypted in transit (TLS) and at rest (AES-256)
Access Controls: Granular permissions down to document level with role-based access control
Audit Logging: Comprehensive logs for compliance tracking and security monitoring
Open-Source Transparency: R2R core is open-source enabling security audits and compliance validation
Custom Compliance: Self-hosted deployments can be tuned to meet specific regulatory requirements (HIPAA, SOC 2, etc.)
Encryption: SSL/TLS for data in transit, 256-bit AES encryption for data at rest
SOC 2 Type II certification: Industry-leading security standards with regular third-party audits
Security Certifications
GDPR compliance: Full compliance with European data protection regulations, ensuring data privacy and user rights
Access controls: Role-based access control (RBAC), two-factor authentication (2FA), SSO integration for enterprise security
Data isolation: Customer data stays isolated and private - platform never trains on user data
Domain allowlisting: Ensures chatbot appears only on approved sites for security and brand protection
Secure deployments: ChatGPT Plugin support for private use cases with controlled access
Pricing & Plans
Pricing model: Bespoke project-based pricing with costs scaling by scope, complexity, and timeline; higher upfront investment than SaaS subscriptions
Minimum project size: $10,000+ minimum engagement; average hourly rate $25-49/hour
Project cost range: $4,200 to over $70,000 depending on complexity and requirements
Billing structure: Week-by-week exploratory pricing available for flexibility; custom enterprise agreements for long-term partnerships (average 3.2+ years)
Team composition: Clients work with teams of 1-15 members ensuring quality service and timely delivery
Value proposition: Full code ownership without recurring subscription costs; long-term investment for organizations with unique, complex requirements
Free Tier: Generous free tier requiring no credit card for experimentation and development
Developer Plan: $25/month for individual developers and small projects
Enterprise Plans: Custom pricing based on scale, features, and support requirements
Self-Hosting: Open-source R2R available for free self-hosting (infrastructure costs only)
Managed Cloud: SciPhi handles infrastructure, deployment, scaling, updates, and maintenance
No Per-Request Fees: Flat subscription pricing without per-query or per-document charges
Cost Optimization: Self-hosting option enables cost savings for teams with infrastructure expertise
Standard Plan: $99/month or $89/month annual - 10 custom chatbots, 5,000 items per chatbot, 60 million words per bot, basic helpdesk support, standard security
View Pricing
Premium Plan: $499/month or $449/month annual - 100 custom chatbots, 20,000 items per chatbot, 300 million words per bot, advanced support, enhanced security, additional customization
Enterprise Plan: Custom pricing - Comprehensive AI solutions, highest security and compliance, dedicated account managers, custom SSO, token authentication, priority support with faster SLAs
Enterprise Solutions
7-Day Free Trial: Full access to Standard features without charges - available to all users
Annual billing discount: Save 10% by paying upfront annually ($89/mo Standard, $449/mo Premium)
Flat monthly rates: No per-query charges, no hidden costs for API access or white-labeling (included in all plans)
Managed infrastructure: Auto-scaling cloud infrastructure included - no additional hosting or scaling fees
Support & Documentation
Support model: White-glove support with dedicated account manager and direct access to development team during and after deployment
Project management: Weekly meetings, backlog system, continuous engagement throughout project lifecycle and post-delivery assistance beyond original scope
Documentation: Custom documentation delivered with code including endpoint design, architecture diagrams, and implementation guides
Training: In-person training and knowledge transfer sessions with client teams; hands-over clear docs and code reviews on delivery
Response times: Direct communication with dedicated team; no formal SLAs but clients report high responsiveness and transparency
Community: No public community forum; support delivered through professional services engagement model
Comprehensive Documentation: Detailed docs at r2r-docs.sciphi.ai covering all features and API endpoints
GitHub Repository: Active open-source development at github.com/SciPhi-AI/R2R with code examples
Community Support: Discord community and GitHub issues for peer support and troubleshooting
Enterprise Support: Dedicated support channels for enterprise customers with SLAs
Code Examples: Python client (R2RClient) with extensive examples and starter code
API Reference: Complete REST API documentation with curl examples and authentication guides
Developer Dashboard: Real-time logs, latency monitoring, and retrieval quality metrics
Documentation hub: Rich docs, tutorials, cookbooks, FAQs, API references for rapid onboarding
Developer Docs
Email and in-app support: Quick support via email and in-app chat for all users
Premium support: Premium and Enterprise plans include dedicated account managers and faster SLAs
Code samples: Cookbooks, step-by-step guides, and examples for every skill level
API Documentation
Active community: User community plus 5,000+ app integrations through Zapier ecosystem
Regular updates: Platform stays current with ongoing GPT and retrieval improvements automatically
Limitations & Considerations
Higher initial investment: Project-based pricing ($10,000+ minimum) significantly higher than SaaS alternatives; not suitable for small businesses or startups with limited budgets
Longer implementation timeline: Expect 12-18 month pilot phases before enterprise-wide rollout; implementations take weeks to months vs. hours for self-service platforms
Requires technical resources: Organizations need internal development teams to maintain and extend custom solutions post-delivery; not a turnkey solution
Services-driven approach: Model selection, configuration, and customization determined by Azumo team vs. self-service dashboard controls
Learning curve: Custom systems require significant onboarding and training for client teams to operate and maintain effectively
Not ideal for: Simple use cases that can be solved with off-the-shelf tools, organizations seeking rapid deployment without development resources, budget-constrained small businesses
Developer-Focused: No no-code UI—requires technical expertise to build and wire custom front ends
Infrastructure Requirements: Self-hosting requires GPU infrastructure and DevOps expertise
Integration Effort: API-first design means building your own chat UI and user experience
Learning Curve: Advanced features like knowledge graphs and agentic RAG require understanding of RAG concepts
No Pre-Built Widgets: Unlike plug-and-play chatbot platforms, requires custom implementation
Community Support Limits: Open-source support relies on community unless on enterprise plan
Managed vs Self-Hosted Trade-offs: Cloud convenience vs self-hosting control requires careful evaluation
Managed service approach: Less control over underlying RAG pipeline configuration compared to build-your-own solutions like LangChain
Vendor lock-in: Proprietary platform - migration to alternative RAG solutions requires rebuilding knowledge bases
Model selection: Limited to OpenAI (GPT-4, GPT-3.5) and Anthropic (Claude) - no support for other LLM providers (Cohere, AI21, open-source models)
Pricing at scale: Flat-rate pricing may become expensive for very high-volume use cases (millions of queries/month) compared to pay-per-use models
Customization limits: While highly configurable, some advanced RAG techniques (custom reranking, hybrid search strategies) may not be exposed
Language support: Supports 90+ languages but performance may vary for less common languages or specialized domains
Real-time data: Knowledge bases require re-indexing for updates - not ideal for real-time data requirements (stock prices, live inventory)
Enterprise features: Some advanced features (custom SSO, token authentication) only available on Enterprise plan with custom pricing
After analyzing features, pricing, performance, and user feedback, both Azumo and SciPhi are capable platforms that serve different market segments and use cases effectively.
When to Choose Azumo
You value highly skilled nearshore developers in same timezone
Extensive AI/ML expertise since 2016
Flexible engagement models (staff aug or project-based)
Best For: Highly skilled nearshore developers in same timezone
When to Choose SciPhi
You value state-of-the-art retrieval accuracy
Open-source with strong community
Production-ready with proven scalability
Best For: State-of-the-art retrieval accuracy
Migration & Switching Considerations
Switching between Azumo and SciPhi requires careful planning. Consider data export capabilities, API compatibility, and integration complexity. Both platforms offer migration support, but expect 2-4 weeks for complete transition including testing and team training.
Pricing Comparison Summary
Azumo starts at $100000/month, while SciPhi begins at custom pricing. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.
Our Recommendation Process
Start with a free trial - Both platforms offer trial periods to test with your actual data
Define success metrics - Response accuracy, latency, user satisfaction, cost per query
Test with real use cases - Don't rely on generic demos; use your production data
Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
Check vendor stability - Review roadmap transparency, update frequency, and support quality
For most organizations, the decision between Azumo and SciPhi comes down to specific requirements rather than overall superiority. Evaluate both platforms with your actual data during trial periods, focusing on accuracy, latency, ease of integration, and total cost of ownership.
📚 Next Steps
Ready to make your decision? We recommend starting with a hands-on evaluation of both platforms using your specific use case and data.
• Review: Check the detailed feature comparison table above
• Test: Sign up for free trials and test with real queries
• Calculate: Estimate your monthly costs based on expected usage
• Decide: Choose the platform that best aligns with your requirements
Last updated: December 4, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
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