In this comprehensive guide, we compare SearchUnify and Stonly 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 SearchUnify and Stonly, 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 SearchUnify if: you value g2 leader for 21 consecutive quarters (5+ years) in enterprise search - exceptional market validation vs newer rag startups
Choose Stonly if: you value exceptional ease of use - 4.8/5 g2 rating with intuitive visual editor praised in 32 reviews
About SearchUnify
SearchUnify is ai-powered unified enterprise search and knowledge management. Enterprise cognitive search platform with proprietary Federated RAG (FRAG™) architecture, 100+ pre-built connectors, and mature Salesforce integration. G2 Leader for 21 consecutive quarters (5+ years). Parent company Grazitti Interactive (founded 2008) maintains SOC 2 Type 2 + ISO 27001 + HIPAA compliance. BYOLLM flexibility supports OpenAI, Azure, Google Gemini, Hugging Face, custom models. Critical gaps: NO WhatsApp/Telegram messaging, NO public pricing (AWS Marketplace: $0.01-$0.025/request), NO Zapier integration. Enterprise search heritage vs RAG-first positioning. Founded in 2008 (Grazitti), SearchUnify product launched ~2012, headquartered in Panchkula, India / San Jose, CA, USA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
84/100
Starting Price
Custom
About Stonly
Stonly is interactive knowledge base platform with enterprise ai-powered answers. Stonly is a customer support knowledge management platform with embedded AI capabilities focused on interactive step-by-step guides and help desk agent assistance. Its AI Answers feature (Enterprise-only add-on) achieves 71% self-serve success rates, but it's fundamentally a knowledge base platform with AI features—not a pure RAG-as-a-Service solution. Founded in 2017, headquartered in San Francisco, CA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
96/100
Starting Price
$249/mo
Key Differences at a Glance
In terms of user ratings, Stonly in overall satisfaction. From a cost perspective, SearchUnify starts at a lower price point. The platforms also differ in their primary focus: Enterprise Search versus Knowledge Management. 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.
12MB Size Limit: Upper limit per document field - may constrain large PDF processing vs unlimited competitors
Website Crawling: Public and gated sites (excluding CAPTCHA-protected), configurable depth, JavaScript-enabled, sitemap support (.txt/.xml), custom HTML selectors
LMS Systems: Docebo, Absorb LMS, LearnUpon, Saba Cloud for training content
Video Platforms: YouTube, Vimeo, Wistia, Vidyard with transcript extraction
Universal Content API: Custom connector development for unsupported platforms
PDF uploads confirmed
Public website crawling: Pages not requiring authentication
Zendesk help center content indexing
Proprietary interactive guide format as primary content model
Note: No Google Drive, Dropbox, Notion, or SharePoint integrations for data ingestion
Note: No YouTube transcript extraction (videos can be embedded but not processed)
Note: No direct Word document (.docx) or HTML file imports confirmed
Note: No automatic content syncing from external sources - updates are manual through Stonly's visual editor
Content limits by tier: Basic (5 guides, 400 views/mo), Small Business (unlimited guides, 4K views/mo), Enterprise (custom)
Content versioning: Side-by-side comparison and instant restore on Business and Enterprise plans
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
Native Search Clients: Salesforce Service Console/Communities, ServiceNow, Zendesk Support/Help Center, Khoros Aurora/Classic, Slack
SUVA Virtual Assistant: "World's First Federated RAG Chatbot" analyzing 20+ attributes (customer history, similar cases, past resolutions)
Multi-Turn Conversation: Context retention across sessions with conversation memory
Lead Capture: Custom slots and in-chat case creation for lead generation
Human Handoff: Seamless escalation to Salesforce, Zendesk, Khoros with full conversation history transfer
Intent Recognition: Unsupervised ML with NER entity extraction and sentiment analysis
Voice Capabilities: Speech-to-Text and Text-to-Speech integration
35+ Languages: Native handling for Arabic, German, French, Mandarin Chinese with extended support via translation CSV
Up to 5 Virtual Agents: Per instance deployable across internal and customer-facing portals
Temperature Controls: Adjust response creativity by persona, use case, and audience type
SearchUnifyGPT™: LLM answers with inline citations above traditional search results
Conversational AI Bot: Delivers confident answers backed by verified structured knowledge unlike generic LLMs that can hallucinate or invent answers
Knowledge-grounded responses: Provides answers backed by verified structured knowledge from guides you create preventing fabricated information
AI Agent Assist: Automatically summarizes tickets, suggests right path to resolution, and generates responses for support agents
Three core automation functions: Automatically analyzes and summarizes support ticket content, recommends most relevant Stonly guide/knowledge path to resolve issues, drafts complete responses for agents to review/edit/send
Process automation: Define processes to be followed and link them to different back-office tools to resolve customer requests before they reach support
Personalized knowledge: AI-powered solutions and process automation allow creation of guides, walkthroughs, checklists, knowledge bases adapting to each customer's needs
71% self-serve success rate: With AI Answers feature documented in company data
Hallucination reduction: Knowledge-grounding approach vs generic chatbots reduces off-topic responses
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
Customization & Branding
Theme Editor: Visual chat widget customization without code
Color Configuration: Background, text, conversation bubbles, user input areas with full palette control
Typography: Font style selection across all chat elements
White-Labeling: Supported through custom branding elements (explicit 'white-label' documentation not found)
Domain Restrictions: Platform-specific deployment configurations and role-based content permissions
Visual Search Tuning: Boost or downgrade document rankings without code via admin UI
NLP Manager: Synonym, acronym, keyword configuration via visual interface
Temperature Controls: Per-persona, use case, and audience type creativity adjustment for LLM responses
Visual editor: Intuitive no-code interface for creating guides, decision trees, checklists, forms
CSS customization: Available on all paid plans
White-labeling: Enterprise plan only - complete branding removal
Pre-built templates: Common support scenarios covered
Role-based access control: Advanced permissions on Enterprise plan
Learning curve: Described as "small" - users can create guides in under 30 minutes
Note: No formal content approval workflows documented
Note: Cannot edit guides on mobile devices
Note: Angular framework compatibility issues reported - "Stonly onboarding will work randomly" with dynamic code
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
BYOLLM Architecture: Bring Your Own LLM flexibility avoiding vendor lock-in
Partner-Provisioned: Claude via Amazon Bedrock (14-day trial), OpenAI Service
Self-Provisioned OpenAI: GPT models via API key with full configuration control
Azure OpenAI Service: Complete endpoint configuration for enterprise Azure deployments
Google Gemini: Integration for Google's multimodal LLM capabilities
Hugging Face: Open-source model support for custom or community models
In-House Custom Models: Support for proprietary inference models and custom deployments
Multiple LLM Connections: Connect multiple providers simultaneously with activation toggles
Fallback Mechanisms: Automatic failover when primary LLMs become inaccessible
Temperature Controls: Adjust creativity by persona, use case, audience type for each LLM
CRITICAL: NO Automatic Model Routing: No intelligent selection based on query characteristics - manual configuration required vs competitors with query complexity-based routing
Note: Undisclosed proprietary LLM - Stonly does not disclose the specific model powering AI Answers
Note: No model selection - users cannot choose between GPT-3.5, GPT-4, Claude, or other models
Note: No temperature controls, fine-tuning, or model routing
AI Profiles: Up to 20 per team for tone and behavior customization
Custom Instructions: Up to 100 per team defining boundaries and style
Guided AI Answers: Define specific questions that trigger predetermined answers, bypassing AI generation for sensitive scenarios
Automatic fallback: When AI confidence is low, system falls back to ML-powered search rather than forcing an answer
Knowledge-grounded approach: AI responses anchored in Stonly guides, external websites, and selected PDFs to reduce hallucinations
Taps into top models—OpenAI’s GPT-5.1 series, GPT-4 series, and even Anthropic’s Claude for enterprise needs (4.5 opus and sonnet, etc ).
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)
Three Official SDKs: JavaScript/Node.js (su-sdk on NPM), Python (searchunify on PyPI), Java (Maven artifact)
Python SDK: Full API coverage with 22+ analytics methods for data analysis and reporting
Java SDK: Non-blocking I/O, high concurrency, data marshaling for enterprise Java applications
RESTful API v2: Swagger documentation at each instance with v2-prefixed endpoints
API Categories: Search (/v2_search/), Content Source management (/v2_cs/), Analytics (/api/v2/)
OAuth 2.0 Authentication: Password grant and client credentials with 4-hour access tokens, 14-day refresh tokens
MCP (Model Context Protocol) Support: su-mcp library for Claude Desktop and similar LLM tooling integration
Documentation Quality: Solid core API coverage with curl examples and authentication guides
CRITICAL: CRITICAL GAPS - Rate Limits: Specific limits require community documentation access - transparency gap vs competitors with public rate limit tables
CRITICAL: NO API Versioning Policy: No documented deprecation policy - potential breaking change risk
CRITICAL: LIMITED Cookbook Examples: Basic code samples but not comprehensive practical examples vs competitors with extensive cookbook libraries
REST API: Supports user provisioning, content management, widget control
Mobile SDKs (Enterprise only): iOS, Android, React Native, Flutter
Note: No Python SDK or server-side Node.js SDK
Note: No GraphQL API or OpenAPI/Swagger specification
Note: Rate limits not publicly documented
Note: No API Explorer, sandbox environment, or Postman collections
Note: REST API versioning strategy unclear
Widget API: Programmatic control including opening specific content, listening for events, user identification
CSP whitelisting: Instructions documented for Content Security Policy compliance
Widget versioning documented
Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat.
API Documentation
Visual Search Tuning: Boost or downgrade document rankings via admin UI without coding
NLP Manager: Synonym, acronym, keyword configuration per language through visual interface
Temperature Controls: Per-persona, use case, audience type creativity adjustment for LLM responses
Multi-LLM Support: Connect multiple providers simultaneously with activation toggles and failovers
Custom Slots: Lead capture field configuration for SUVA conversations
Custom HTML Selectors: Precise website crawling targeting specific content elements
Configurable Crawl Depth: Control how deeply websites are indexed for knowledge base
Sync Frequency Options: 15-minute intervals to manual on-demand for different update requirements
RBAC Customization: Super Admin, Admin, Moderator tiers with configurable permissions
Custom User Attributes: Organization-specific analytics dimensions for tailored reporting
CSS and HTML customization: Change layout and look of knowledge base with custom code capabilities
Intuitive customization tools: Easy-to-use tools that don't require code for basic customization
Layout customization: Decide how content is structured and presented with flexible options
Design controls: Manage visual components like colors, logo, or cover image for brand alignment
Personalized content: Use customer data to show personalized content from knowledge base for targeted experiences
Data-driven personalization: Customers see what they need right away when first accessing knowledge base
Analytics insights: Guide usage analytics provide insight into customer behavior for continuous improvement
Highly flexible platform: Users appreciate ability to use Stonly for knowledge bases and guided tours with target properties based on specific user needs
Rich media support: Add images, GIFs, videos, and annotations to bring knowledge base content to life
Third-party scripts: Install scripts from other tools like Google Analytics for extended functionality
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
NO Public Pricing: Website requires custom enterprise quotes - transparency gap vs competitors with published tiers
AWS Marketplace Revealed Pricing: Up to 100K searches/month $0.025/request, up to 200K $0.015/request, up to 300K $0.01/request
Unlimited Content Sources: Flat subscription pricing with no per-connector fees
Free Trials: Available without credit card requirement for evaluation
Annual Escalation: User reviews note "guaranteed price increase every year" - budget unpredictability concern
7-14 Day Deployment: Using pre-built connectors for implementation timeframe
Multi-Geographic AWS: Automatic backups across regions for data redundancy
Enterprise Consulting: Assess, Advise, Engage packages for implementation support
Startup to Enterprise: Platform scales from small teams to large organizations
Basic (Free): 5 guides, 400 views/month, 1 seat, single language
Small Business ($249/mo, $199/mo annual): Unlimited guides, 4,000 views/month, 5 seats, 3 knowledge bases, CSS customization, Zapier, NPS surveys
Enterprise (Custom, ~$39K/year avg): Custom views, unlimited seats, AI Answers add-on, Mobile SDKs, SAML SSO, white-label, auto-translation, CSAT/CES surveys
SUVA Agent Builder: Visual configuration for up to 5 virtual agents per instance
Analytics Dashboard: Point-and-click metric exploration with AI-generated Actionable Insights
Guided Workflows: Step-by-step contextual help for common admin tasks
4.8/5 ease of use rating on G2
"Ease of use" mentioned 32 times in G2 reviews
Visual drag-and-drop editor requires no coding
Small learning curve - non-technical teams productive quickly
Guide creation in under 30 minutes reported by users
Pre-built templates for common scenarios
Intuitive interface for support teams
Note: Some navigation confusion reported in admin interface
Note: Cannot edit on mobile devices
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.
Federated R A G ( F R A G™) Architecture ( Core Differentiator)
Proprietary 3-Layer Framework: Specifically designed for hallucination mitigation in enterprise knowledge retrieval
Federation Layer: Constructs 360-degree enterprise context by unifying data across all 100+ connected sources simultaneously
Retrieval Layer: Filters responses using keyword matching, semantic similarity, and vector search for comprehensive result accuracy
Augmented Generation Layer: Produces responses using neural networks with temperature-controlled creativity balancing accuracy and natural language
Vector Search Integration: Semantic embedding-based retrieval combined with traditional keyword matching for best-of-both-worlds accuracy
Prompt Optimization: Local retrieval enhances prompts with relevant context from federated sources before LLM submission
Multi-Repository Context: Documentation, forums, LMS, CRM, support tickets unified for comprehensive answer grounding
User Feedback Loops: Continuous improvement through response validation and audit mechanisms
Fallback Generation: Maintains service during LLM downtime with alternative response mechanisms
SUVA "World's First Federated RAG Chatbot": Analyzes 20+ attributes (customer history, similar cases, past resolutions) across federated enterprise sources
Competitive Advantage: Most RAG platforms focus on single-source or simple multi-source retrieval - FRAG™ explicitly designed for complex enterprise federation
N/A
N/A
100+ Pre- Built Connectors ( Differentiator)
Dramatically Reduced Integration Effort: Out-of-box connectors vs custom development required by many RAG platforms
CRM/Support Systems: Salesforce, ServiceNow, Zendesk, Dynamics 365, Help Scout with bi-directional sync
Collaboration Platforms: Slack, MS Teams, Confluence, Jira for internal knowledge aggregation
Comparison Validity: Architectural comparison to CustomGPT.ai is VALID but highlights different priorities - SearchUnify enterprise search platform with RAG vs likely more developer-first RAG API from CustomGPT
Use Case Fit: Large enterprises with fragmented knowledge across 100+ systems (Salesforce-centric orgs especially), organizations prioritizing enterprise security/compliance, teams needing mature analytics and no-code usability
NOT Ideal For: Developers seeking lightweight API-first RAG, SMBs without enterprise platform ecosystem, consumer-facing chatbot deployments (WhatsApp/Telegram absent)
Note: NOT a RAG-as-a-Service platform - fundamentally a knowledge base tool with embedded AI
Data source flexibility: Limited (PDF, public web, Zendesk only) vs comprehensive RAG platforms
LLM model options: None (undisclosed proprietary model, no user selection)
API-first architecture: Weak (widget-focused, limited SDKs, no server-side SDKs)
Performance benchmarks: Not published
Self-service AI pricing: Not available (Enterprise-gated, ~$39K/year)
Help desk integration depth: Excellent (best-in-class Zendesk, Salesforce, Freshdesk)
Hallucination controls: Strong (grounded in structured content)
Best for: Customer support ticket deflection, not flexible RAG backends
Not ideal for: Developers seeking pure RAG API, multi-tenant SaaS RAG backends, use cases needing model selection/fine-tuning
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
Competitive Positioning
Market Position: Enterprise cognitive search leader with RAG enhancement vs pure-play RAG startups
5+ Years Market Leadership: G2 Leader 21 consecutive quarters in Enterprise Search - exceptional validation vs newer RAG platforms
IDC/Forrester Recognition: IDC MarketScape 2024 Major Player (Knowledge Management), Forrester Wave Q3 2021 Strong Performer (Cognitive Search)
FRAG™ Differentiator: Proprietary 3-layer federated architecture specifically designed for enterprise hallucination mitigation vs generic RAG implementations
100+ Connector Advantage: Dramatically reduced integration effort vs platforms requiring custom connector development for enterprise systems
Salesforce Strength: Summit Partner status with native Service Console/Communities clients, drag-and-drop components, AppExchange - unmatched depth vs API-only Salesforce integrations
YouTube Capability: Transcript-based timestamped search rare among RAG platforms - strong for video training content
BYOLLM Flexibility: Claude, OpenAI, Azure, Google Gemini, Hugging Face, custom models vs vendor lock-in from single-provider platforms
Enterprise Security: SOC 1/2/3 + ISO 27001/27701 + HIPAA + GDPR with single-tenant architecture competitive with Cohere, Progress enterprise offerings
vs. CustomGPT: SearchUnify enterprise search platform + RAG vs likely more developer-first RAG API - different target markets
vs. Cohere: SearchUnify 100+ connectors + no-code usability vs Cohere superior AI models + air-gapped deployment
vs. Progress: SearchUnify FRAG™ + Salesforce depth vs Progress REMi quality monitoring + open-source NucliaDB
vs. Chatling/Jotform: SearchUnify enterprise cognitive search vs SMB no-code chatbot tools - fundamentally different scales
CRITICAL: Pricing Transparency Gap: NO public pricing vs competitors with published tiers - requires sales engagement and annual escalation clauses
CRITICAL: Consumer Messaging Absent: NO WhatsApp, Telegram, Zapier vs omnichannel competitors - enterprise support channels only
CRITICAL: Cloud-Only Limitation: NO on-premise/air-gapped deployment vs Cohere's private deployment options for highly regulated industries
Unique strength: Interactive guide format for structured support content
vs CustomGPT: Not comparable - different product categories (knowledge base vs RAG-as-a-Service)
vs Zendesk: Lighter-weight alternative focused on self-service guides vs full customer service platform
vs traditional chatbots: Interactive guides provide structured paths vs free-form conversation
Target audience: Support teams using Zendesk/Salesforce, not developers building RAG applications
70-76% ticket reduction documented in case studies
71% self-serve success rate with AI Answers
Enterprise compliance suitable for regulated industries
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
Deployment & Infrastructure
Cloud-Only SaaS: Hosted on AWS infrastructure with multi-geographic automatic backups
Single-Tenant Architecture: Customer data isolation preventing cross-tenant information leakage
Multi-Geographic AWS: Redundant backups across regions for data protection and disaster recovery
Native Widget Deployment: Salesforce Service Console/Communities, ServiceNow, Zendesk Support/Help Center, Khoros Aurora/Classic, Slack
JavaScript Widget: Embeddable search and chat widgets for custom web deployments
API-Based Deployment: RESTful endpoints with OAuth 2.0 for custom application integration
Marketplace Availability: Salesforce AppExchange, ServiceNow Store, Microsoft AppSource for streamlined procurement
7-14 Day Deployment: Using pre-built connectors for rapid implementation timeframes
CRITICAL: NO On-Premise Option: Cloud-only deployment may disqualify air-gapped enterprise requirements
CRITICAL: NO Hybrid Deployment: Cannot combine cloud processing with on-premise data storage
N/A
N/A
Customer Base & Case Studies
Accela: 99.7% support cost savings with SUVA chatbot deflecting cases and providing instant answers
Cornerstone OnDemand: 98% self-service resolution rate using SearchUnify federated search across LMS and support content
97-98% G2 Satisfaction: "Ease of Doing Business With" rating from customer reviews indicates strong relationship management
N/A
N/A
A I Models
BYOLLM (Bring Your Own LLM) Architecture: Avoid vendor lock-in with flexible model selection
Partner-Provisioned LLMs: Claude via Amazon Bedrock (14-day trial), OpenAI GPT models with managed service
Self-Provisioned OpenAI: Connect your own OpenAI API key with full configuration control (GPT-4, GPT-3.5-turbo, etc.)
Azure OpenAI Service: Complete endpoint configuration for enterprise Azure deployments with data residency control
Google Gemini: Integration for Google's multimodal LLM capabilities and competitive pricing
Hugging Face Models: Open-source model support for custom or community models (Llama, Falcon, etc.)
Custom In-House Models: Support for proprietary inference models and custom deployments
Multiple LLM Connections: Connect multiple providers simultaneously with activation toggles and automatic failover
Temperature Controls: Adjust creativity by persona, use case, and audience type for each LLM
No Automatic Model Routing: Manual configuration required vs competitors with query complexity-based routing
Undisclosed Proprietary LLM: Stonly does not publicly disclose the specific model powering AI Answers feature
No Model Selection: Users cannot choose between GPT-3.5, GPT-4, Claude, Gemini, or other LLM providers
No Temperature Controls: No user-facing controls for adjusting response creativity, randomness, or formatting
No Fine-Tuning or Model Routing: Cannot customize model behavior beyond predefined AI Profiles and Custom Instructions
AI Profiles (Up to 20): Define tone, boundaries, and behavior for different use cases or audiences
Custom Instructions (Up to 100): Set specific rules and style guidelines for AI response generation
Guided AI Answers: Predefined responses for specific questions bypassing AI generation for sensitive scenarios
Automatic Fallback: Low-confidence scenarios trigger fallback to ML-powered search rather than forcing unreliable AI answer
Knowledge-Grounded Approach: AI responses anchored in Stonly guides, external websites, and PDFs to reduce hallucinations
Primary models: GPT-5.1 and 4 series from OpenAI, and Anthropic's Claude 4.5 (opus and sonnet) 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
FRAG™ (Federated RAG) Architecture: Proprietary 3-layer framework specifically designed for hallucination mitigation in enterprise knowledge retrieval
Federation Layer: Constructs 360-degree enterprise context by unifying data across all 100+ connected sources simultaneously
Retrieval Layer: Filters responses using keyword matching, semantic similarity, and vector search for comprehensive result accuracy
Augmented Generation Layer: Produces responses using neural networks with temperature-controlled creativity balancing accuracy and natural language
Vector Search Integration: Semantic embedding-based retrieval combined with traditional keyword matching
Hybrid Search: Reciprocal rank fusion combines dense and sparse retrieval for best-of-both-worlds accuracy
Multi-Repository Context: Documentation, forums, LMS, CRM, support tickets unified for comprehensive answer grounding
SUVA "World's First Federated RAG Chatbot": Analyzes 20+ attributes (customer history, similar cases, past resolutions) across federated enterprise sources
Hallucination Mitigation: 3-layer FRAG architecture with sensitive data removal before LLM transmission and response analysis preventing leakage
User Feedback Loops: Continuous improvement through response validation and audit mechanisms
Fallback Generation: Maintains service during LLM downtime with alternative response mechanisms
AI Answers (Enterprise Add-On): Generative AI responses grounded in Stonly guides, external websites, and selected PDFs
Knowledge-Grounding: Responses anchored to structured content (interactive guides, decision trees, checklists) reducing hallucinations vs generic chatbots
Confidence-Based Fallback: Automatic switch to ML-powered search when AI confidence is low preventing unreliable answers
Multi-Source Ingestion: PDF uploads, public website crawling, Zendesk help center content indexing
Interactive Guide Format: Proprietary content model combining structured workflows with AI-generated answers
Limited Data Sources: No Google Drive, Dropbox, Notion, SharePoint, or YouTube transcript extraction
Manual Content Updates: Updates through Stonly's visual editor—no automatic syncing from external sources
71% Self-Serve Success Rate: Documented effectiveness of AI Answers in reducing support escalations
Hallucination Controls: Strong grounding in structured content vs open-ended conversational AI
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
Use Cases
Enterprise Customer Support: SUVA virtual assistant deflects support tickets with federated knowledge across all enterprise systems (99.7% cost savings at Accela)
Salesforce Service Cloud Enhancement: Native Service Console and Communities integration for unified knowledge search within Salesforce workflows
Multi-System Knowledge Unification: Consolidate fragmented knowledge across 100+ systems (CRM, LMS, forums, documentation, SharePoint, etc.)
Employee Self-Service: Internal help desks and HR portals with federated search across all internal knowledge sources
Customer Community Portals: Self-service communities with SearchUnifyGPT™ answers and traditional search results side-by-side
Training & LMS Search: Unified search across Docebo, Absorb LMS, YouTube transcripts, and documentation for training content discovery
Contact Center Optimization: Agent Helper provides real-time knowledge suggestions during live support interactions to improve resolution times
Case Deflection: 98% self-service resolution (Cornerstone OnDemand) reducing support ticket volume and operational costs
Customer Support Ticket Deflection: 70-76% ticket reduction through interactive self-service guides and AI Answers
Help Desk Integration: Deep Zendesk, Salesforce Service Cloud, Freshdesk, ServiceNow integration for unified support workflows
Interactive Onboarding: Step-by-step guides, decision trees, and checklists for product onboarding and user education
Knowledge Base Enhancement: Augment traditional help centers with interactive guides and AI-powered search
Agent Assistance: Provide support agents with guided workflows and AI answers during live interactions
Multi-Language Support: Auto-translation on Enterprise plan for global support teams and multilingual customers
Complex Troubleshooting: Decision tree logic guides users through multi-step troubleshooting processes
Compliance & Training: Structured guides ensuring consistent information delivery for regulated industries
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)
Automatic Tier Upgrades: Exceeding limits for 2 consecutive months triggers automatic upgrade and billing adjustment
Enterprise-Gated Features: AI Answers, Mobile SDKs, SAML SSO, white-labeling all require Enterprise plan
Average Enterprise Contract: ~$39,000 annually according to Vendr procurement data
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
SearchUnify Academy: Free self-paced training with certifications covering cognitive search fundamentals, search tuning, content source configuration, platform administration
Swagger Documentation: Per-instance API documentation with curl examples and authentication guides at each deployment
Three Official SDKs: JavaScript/Node.js (su-sdk on NPM), Python (searchunify on PyPI), Java (Maven artifact) with comprehensive method coverage
MCP (Model Context Protocol) Support: su-mcp library for Claude Desktop and similar LLM tooling integration
Community Forum: User forum and knowledge base access for peer support and best practices sharing
Enterprise Support Channels: Phone, email, chat support for enterprise customers with SLA guarantees
Implementation Consulting: Assess, Advise, Engage packages for deployment assistance and optimization
Dedicated Account Management: Enterprise tier with assigned account managers and quarterly business reviews
97-98% G2 Satisfaction: "Ease of Doing Business With" rating from customer reviews indicating strong relationship management
Guided Workflows: Contextual help suggestions for admin onboarding and platform navigation
4.8/5 G2 Rating: 132 reviews with consistently high satisfaction scores
Ease of Use Praised: "Ease of use" mentioned 32 times in G2 reviews indicating intuitive platform
Help Center Documentation: Comprehensive guides and tutorials for platform features
Email and Chat Support: Standard support channels for all paid plans
Dedicated Support (Enterprise): Priority support with dedicated account team and faster response times
Pre-Built Templates: Common support scenario templates accelerating guide creation
Quick Onboarding: Users report creating guides in under 30 minutes with small learning curve
REST API Documentation: API reference for user provisioning, content management, and widget control
Mobile SDKs (Enterprise): iOS, Android, React Native, Flutter for native app integration
Limited Developer Resources: No Python/Node.js SDKs, GraphQL, OpenAPI specs, or API Explorer/sandbox
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
Additional Considerations
Enterprise-First Platform: Designed for large organizations with complex, federated knowledge ecosystems - may be overwhelming for small businesses seeking simple chatbot solutions
Implementation Complexity: While pre-built connectors accelerate deployment (7-14 days), proper configuration of 100+ sources, FRAG™ architecture, and SUVA agents requires thoughtful planning and technical expertise
Learning Curve for Advanced Features: Temperature controls, NLP Manager, visual search tuning, and multi-LLM configuration provide powerful customization but require understanding of AI/RAG concepts for optimal utilization
Cost Structure Opacity: Lack of public pricing transparency creates evaluation friction - potential customers must engage sales for quotes, making competitive comparison difficult without significant time investment
Annual Price Escalation Risk: User reviews consistently mention "guaranteed price increase every year" - organizations should factor long-term budget growth into ROI calculations and contract negotiations
Integration Gaps for Modern Workflows: Missing Zapier (7,000+ app ecosystem), Notion (popular knowledge base), and consumer messaging platforms (WhatsApp, Telegram) limit use cases vs competitors with broader integration catalogs
Limited Customization for External Use: Platform optimized for internal employee support and customer self-service portals - not designed for white-labeled external chatbot products or complex conversational commerce applications
Cloud-Only Deployment Constraint: Organizations requiring air-gapped environments, on-premise data residency, or hybrid cloud architectures cannot use SearchUnify (vs competitors like Cohere offering private deployment options)
Document Size Limitations: 12MB per document field may constrain processing of large technical manuals, legal documents, or comprehensive training materials vs competitors with unlimited document ingestion
Manual LLM Configuration Required: No automatic model routing based on query complexity - IT teams must manually configure which LLM handles which scenarios vs intelligent routing competitors
API Documentation Transparency Gaps: Rate limits require community access, no public API versioning policy, limited cookbook examples compared to developer-first platforms with comprehensive API documentation and sandbox environments
Best For: Large enterprises with Salesforce-centric operations, organizations with 100+ fragmented knowledge sources, regulated industries requiring SOC 2/HIPAA/GDPR compliance, teams prioritizing federated search accuracy over rapid deployment simplicity
NOT Ideal For: Small businesses with limited budgets, startups needing rapid prototyping without sales engagement, organizations requiring consumer messaging platform support, teams seeking white-labeled external chatbot products, companies needing air-gapped/on-premise deployment
Limited UI customization: Limited ability to customize user interface and workflows to match specific brand requirements is primary user concern
Basic collaboration tools: Without real-time editing or advanced team management features can hinder team productivity when multiple people need to work together
No offline access: Guides unavailable without internet connectivity reducing usability in areas with unreliable internet
Performance degradation: Can degrade with very large or complex guides causing slower responsiveness indicating scalability concerns
Restricted language options: Limit efficient creation of multilingual content which may be barrier for global organizations
Mixed media support missing: Users find missing features wishing for mixed media support and enhanced reporting tools
Step ordering difficulties: Users report limitations in feature usability and difficulties with step ordering though support offers helpful workarounds
Requires coding knowledge: Unlike most competitors, doesn't advertise as no-code platform - need coding knowledge to track events, target users, stream data, and style content
Image workflow limitations: Inability to use images in base offering limits utility in some workflows with some advanced features requiring extra costs
View-based pricing: Charges additional fees based on guide views - customers exceeding 4,000 guide views/month pay extra $250-500 monthly depending on volume
Integration reliability: Users find lack of integrations limits ability to fully connect Stonly with other tools - Stonly/Zendesk integration isn't as reliable as desired (stops working every few weeks)
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.
Limitations & Considerations
No Public Pricing Transparency: Requires sales engagement for quotes - budget planning difficulty vs published pricing tiers
No Real-Time Analytics: Flow reports update every 15 minutes—not true real-time monitoring
Limited Developer API: No Python/Node.js SDKs, GraphQL, Swagger specs, or API sandbox for testing
Overage Pricing Escalation: View limits can trigger expensive automatic upgrades after 2 consecutive months
Not Ideal For: Developers seeking pure RAG API, multi-tenant SaaS RAG backends, use cases needing model selection/fine-tuning, or flexible data source integration
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-5.1 and 4 series) and Anthropic (Claude, opus and sonnet 4.5) - 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
Core Knowledge Base Features
N/A
Interactive step-by-step guides with visual flow builder
After analyzing features, pricing, performance, and user feedback, both SearchUnify and Stonly are capable platforms that serve different market segments and use cases effectively.
When to Choose SearchUnify
You value g2 leader for 21 consecutive quarters (5+ years) in enterprise search - exceptional market validation vs newer rag startups
Proprietary FRAG™ architecture specifically designed for hallucination mitigation with 3-layer federation, retrieval, augmented generation
100+ pre-built connectors dramatically reduce integration effort - Google Drive, Salesforce, ServiceNow, Zendesk, Slack, MS Teams, YouTube, Adobe AEM
Best For: G2 Leader for 21 consecutive quarters (5+ years) in Enterprise Search - exceptional market validation vs newer RAG startups
When to Choose Stonly
You value exceptional ease of use - 4.8/5 g2 rating with intuitive visual editor praised in 32 reviews
Deep help desk integrations - bidirectional Zendesk, Salesforce, Freshdesk, ServiceNow connections
Strong compliance - SOC 2 Type 2, GDPR, HIPAA, ISO 27001, PCI, CSA Star Level 1
Best For: Exceptional ease of use - 4.8/5 G2 rating with intuitive visual editor praised in 32 reviews
Migration & Switching Considerations
Switching between SearchUnify and Stonly 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
SearchUnify starts at custom pricing, while Stonly begins at $249/month. 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 SearchUnify and Stonly 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 11, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
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DevRel at CustomGPT.ai. Passionate about AI and its applications. Here to help you navigate the world of AI tools and make informed decisions for your business.
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