In this comprehensive guide, we compare Dataworkz and SearchUnify 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 Dataworkz and SearchUnify, 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 Dataworkz if: you value free tier available for testing
Choose SearchUnify if: you value g2 leader for 21 consecutive quarters (5+ years) in enterprise search - exceptional market validation vs newer rag startups
About Dataworkz
Dataworkz is rag-as-a-service platform for rapid genai development. Dataworkz is a managed RAG platform that enables businesses to build, deploy, and scale GenAI applications using proprietary data with pre-built tools for data discovery, transformation, and monitoring. Founded in 2020, headquartered in Milpitas, CA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
79/100
Starting Price
Custom
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
Key Differences at a Glance
In terms of user ratings, both platforms score similarly in overall satisfaction. From a cost perspective, pricing is comparable. The platforms also differ in their primary focus: RAG Platform versus Enterprise Search. 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
Dataworkz
SearchUnify
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Brings in a mix of knowledge sources through a point-and-click RAG pipeline builder
[MongoDB Reference].
Lets you wire up SharePoint, Confluence, databases, or document repositories with just a few settings.
Gives fine-grained control over chunk sizes and embedding strategies.
Happy to blend multiple sources—pull docs and hit a live database in the same pipeline.
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
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.
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
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
Model-agnostic: plug in GPT-4, Claude, open-source models—whatever fits.
You also pick the embedding model, vector DB, and orchestration logic.
More power, a bit more setup—full control over the pipeline.
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
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)
No-code builder lets you design pipelines; once ready, hit a single API endpoint to deploy.
No official SDK, but REST/GraphQL integration is straightforward.
Sandbox mode encourages rapid testing and tweaking before production.
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
Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat.
API Documentation
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.
Additional Considerations
Supports graph-optimized retrieval for interlinked docs
[MongoDB Reference].
Can act as a central AI orchestration layer—call APIs or trigger actions as part of an answer.
Best for teams with LLMOps expertise who want deep customization, not a prefab chatbot.
Aims for tailor-made AI agents rather than an out-of-box chat tool.
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
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
No-code / low-code builder helps set up pipelines, chunking, and data sources.
Exposes technical concepts—knowing embeddings and prompts helps.
No end-user UI included; you build the front-end while Dataworkz handles the back-end logic.
97-98% G2 Usability Satisfaction: Consistently high ratings for "Ease of Doing Business With"
Visual Content Source Configuration: OAuth flows handled through admin UI without manual setup
Pre-Built Templates: Knowbler for KCS-aligned knowledge articles with structured creation workflows
Drag-and-Drop Components: Salesforce Console search client components for visual customization
NLP Manager: Synonym, acronym, keyword configuration without coding requirements
Visual Search Tuning: Boost or downgrade document rankings via UI sliders and controls
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
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: Enterprise agentic RAG platform with point-and-click pipeline builder for organizations needing custom AI orchestration without heavy coding
Target customers: Large enterprises with LLMOps expertise, data engineering teams building complex AI agents, and organizations requiring agentic architecture with multi-step reasoning and tool use capabilities
Key competitors: Deepset Cloud, LangChain/LangSmith, Haystack, Vectara.ai, and custom-built RAG solutions using MongoDB Atlas Vector Search
Competitive advantages: Model-agnostic with full control over LLM/embedding choices, agentic architecture for multi-step reasoning and dynamic tool selection, graph-optimized retrieval for interlinked documents, no-code pipeline builder with sandbox testing, MongoDB partnership for enterprise integrations, and bring-your-own-infrastructure flexibility (DB, embeddings, VPC)
Pricing advantage: Custom enterprise contracts with usage-based pricing; no public tiers but typically competitive for organizations with existing infrastructure that want orchestration layer without SaaS lock-in; best value for high-volume, complex use cases
Use case fit: Best for enterprises building sophisticated AI agents requiring multi-step reasoning, organizations needing to blend structured APIs/databases with unstructured documents seamlessly, and teams with ML expertise wanting deep customization of chunking, retrieval algorithms, and orchestration logic without building from scratch
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
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
Model-agnostic architecture: Supports GPT-4, Claude, Llama, and other open-source models - full flexibility in LLM selection
Public LLM APIs: Integration with AWS Bedrock and OpenAI APIs for managed model access
Private hosting: Option to host open-source foundation models in your own VPC for data sovereignty and cost control
Composable AI stack: Choose your own embedding model, vector database, chunking strategy, and LLM independently
No vendor lock-in: Flexibility to switch models based on performance, cost, or compliance requirements without platform migration
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
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
Advanced RAG pipeline: Point-and-click builder for configuring and optimizing each aspect of RAG with fine-grained control
RAG-as-a-Service
Agentic architecture: LLM-powered agents that reason through multi-step tasks, call external tools/APIs, and adapt based on context
Agentic RAG
Hybrid retrieval: Mix semantic and lexical retrieval, or use graph search for sharper context and improved accuracy
Hallucination mitigation: RAG references source data to reduce hallucinations and improve factual accuracy
Graph-optimized retrieval: Specialized for interlinked documents with relationship-aware context
Graph Capabilities
Threshold tuning: Balance precision vs. recall for domain-specific requirements
Dynamic tool selection: Agents decide when to query knowledge bases vs. live databases vs. external APIs based on question context
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
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
Retail and e-commerce: Product recommendations, inventory queries, customer service with agentic RAG blending structured data (inventory) and unstructured content (product guides)
Retail Case Study
Banking and financial services: Regulatory compliance queries, customer onboarding, risk assessment with enterprise-grade security and auditability
Healthcare: Clinical decision support, patient information systems, medical knowledge bases with HIPAA-compliant deployment options
Enterprise knowledge management: Internal documentation, policy queries, onboarding assistance with multi-source data integration (SharePoint, Confluence, databases)
Customer support: Multi-step troubleshooting, ticket routing, automated responses with tool calling and API integration
Research and analytics: Document analysis, research assistance, data exploration with graph-optimized retrieval for interlinked content
Manufacturing: Equipment manuals, maintenance procedures, supply chain queries with structured and unstructured data blending
Legal and compliance: Contract analysis, regulatory research, compliance checking with audit trails and traceability
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 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)
Scalability: Pricing scales with usage - cost-effective for high-volume, complex use cases where control matters
No Public Pricing: Website requires custom enterprise quotes - transparency gap vs competitors with published tiers
AWS Marketplace Pricing (Revealed): Up to 100K searches/month at $0.025/request, up to 200K at $0.015/request, up to 300K at $0.01/request
Unlimited Content Sources: Flat subscription pricing with no per-connector fees for 100+ pre-built integrations
Free Trials: Available without credit card requirement for evaluation and proof-of-concept
Annual Price Escalation: User reviews note "guaranteed price increase every year" - budget unpredictability concern
7-14 Day Deployment: Using pre-built connectors for rapid implementation timeframe
Multi-Geographic AWS: Automatic backups across regions for data redundancy and disaster recovery
Enterprise Consulting: Assess, Advise, Engage packages for implementation support and best practices guidance
Scalability: Platform scales from small teams to large organizations without architectural changes
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
Enterprise onboarding: Tailored onboarding and solution engineering for large organizations with complex requirements
Direct engineering support: Engineer-to-engineer support focused on technical implementation and optimization
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
No built-in UI: Platform is API-first with no prefab chat widget - you must build or bring your own front-end interface
Technical expertise required: Best for teams with LLMOps expertise who understand embeddings, prompts, and RAG architecture - not ideal for non-technical users
Custom pricing only: No transparent public pricing tiers - requires sales engagement for pricing quotes and contracts
Enterprise focus: Designed for large organizations - may be overkill for small teams or simple chatbot use cases
Setup complexity: Point-and-click builder simplifies pipeline creation but still requires understanding of RAG concepts and architecture
Limited pre-built templates: Platform provides flexibility but fewer out-of-box solutions compared to turnkey chatbot platforms
No official SDK: REST/GraphQL integration is straightforward but lacks dedicated client libraries for popular languages
Infrastructure requirements: Bring-your-own-infrastructure model requires existing cloud infrastructure and data engineering capabilities
No Public Pricing Transparency: Requires sales engagement for quotes - budget planning difficulty vs published pricing tiers
No Consumer Messaging Platforms: Missing WhatsApp, Telegram, Facebook Messenger native integrations - enterprise support channels only
No Zapier Integration: Significant gap for no-code workflow automation - competitors offer 7,000-8,000+ app connections
Cloud-Only Deployment: No on-premise or air-gapped deployment options - may disqualify certain regulated industries
No Automatic Model Routing: Manual LLM configuration required vs intelligent query-based routing in competitors
12MB Document Size Limit: Upper limit per document field may constrain large PDF processing vs unlimited competitors
No Notion Integration: Notable absence from cloud storage connectors vs competitors supporting Notion knowledge bases
Rate Limits Not Public: Specific API rate limits require community documentation access - transparency gap
No API Versioning Policy: Undocumented deprecation policy - potential breaking change risk for integrations
Limited API Cookbook Examples: Basic code samples but not comprehensive practical examples vs competitors with extensive libraries
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 Agent Features
Agentic RAG Architecture: LLM-powered agents that reason through multi-step tasks, call external tools/APIs, and adapt based on context - built for autonomous operation
Agentic Capabilities
Agent Memory System: Derived from three key artifacts - conversational history, user preferences, and business context from external sources via RAG pipelines and enterprise knowledge graphs
Complex Task Execution: Reasoning capabilities decompose complex tasks into multiple interdependent sub-tasks represented as directed acyclic graphs (DAGs) for parallel execution where possible
Multi-Step Reasoning
LLM Compiler Integration: Identifies optimal sequence for executing sub-tasks with parallel execution when dependencies allow - implements advanced task orchestration patterns
Dynamic Tool Selection: Agents decide when to query knowledge bases versus live databases versus external APIs based on question context and system state
External API Integration: Invoke external APIs to create CRM leads, create support tickets, lookup order details, or trigger actions as part of generating answers
Agent Builder
Continuous Learning & Adaptation: Agent frameworks support continuous learning and context switching across workflows - agents not only retrieve and generate but also plan multi-step tasks and adapt over time
Agent Builder Interface: Easy-to-use interface to assemble Agentic RAG Applications with minimal technical knowledge - takes business requirements and generates agent definitions
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
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 Type: TRUE RAG-AS-A-SERVICE PLATFORM - enterprise agentic RAG orchestration layer designed for custom AI agent development with point-and-click pipeline builder
Core Architecture: Model-agnostic RAG infrastructure with full control over LLM selection, embedding models, vector databases, and chunking strategies - composable AI stack approach
Agentic Focus: Built around LLM-powered autonomous agents that reason through multi-step tasks, call external tools/APIs, and adapt based on user interactions - not simple Q&A chatbots
Agentic RAG
Developer Experience: Point-and-click pipeline builder with sandbox testing, REST/GraphQL API integration, and agent builder for minimal-code assembly - targets LLMOps-savvy teams
No-Code Capabilities: Agent Builder interface and pipeline configuration UI reduce coding requirements, but platform still assumes technical knowledge of RAG concepts and architectures
Target Market: Large enterprises with data engineering teams building sophisticated AI agents, organizations requiring agentic architecture with multi-step reasoning, and teams wanting deep customization without building RAG from scratch
RAG Technology Differentiation: Graph-optimized retrieval for interlinked documents, hybrid retrieval (semantic + lexical), threshold tuning for precision/recall balance, and agentic task decomposition via DAG execution
Graph Capabilities
Deployment Flexibility: Bring-your-own-infrastructure model with MongoDB partnership - deploy on your cloud/VPC with full data sovereignty and infrastructure control
Enterprise Readiness: Enterprise-grade security and scalability, audit trails for every interaction, data sovereignty options, and custom enterprise contracts with usage-based pricing
Enterprise Security
Use Case Fit: Best for enterprises building sophisticated AI agents requiring multi-step reasoning, organizations needing to blend structured APIs/databases with unstructured documents seamlessly, and teams with ML expertise wanting deep RAG customization
NOT Suitable For: Non-technical teams seeking turnkey chatbots, organizations without existing infrastructure, small businesses needing simple Q&A bots, or teams wanting pre-built UI widgets
Competitive Positioning: Competes with Deepset Cloud, LangChain/LangSmith, and custom RAG builds - differentiates through agentic architecture, no-code pipeline builder, and MongoDB partnership for enterprise scalability
Platform Type: ENTERPRISE COGNITIVE SEARCH PLATFORM with RAG capabilities - NOT RAG-first product positioning
Market Heritage: 5+ years enterprise search leadership (G2 Leader 21 consecutive quarters) with RAG added as enhancement vs built RAG-first
FRAG™ Architecture: Proprietary Federated RAG specifically designed for enterprise knowledge unification and hallucination mitigation
Developer Access: Three official SDKs (JavaScript, Python, Java) + RESTful API + MCP support provide programmatic control
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)
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
Federated R A G ( F R A G™) Architecture ( Core Differentiator)
N/A
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
100+ Pre- Built Connectors ( Differentiator)
N/A
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
After analyzing features, pricing, performance, and user feedback, both Dataworkz and SearchUnify are capable platforms that serve different market segments and use cases effectively.
When to Choose Dataworkz
You value free tier available for testing
No-code approach simplifies development
Flexible LLM and vector database choices
Best For: Free tier available for testing
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
Migration & Switching Considerations
Switching between Dataworkz and SearchUnify 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
Dataworkz starts at custom pricing, while SearchUnify 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 Dataworkz and SearchUnify 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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