In this comprehensive guide, we compare CODY AI 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 CODY AI 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 CODY AI if: you value true rag architecture with pinecone vector database and configurable retrieval parameters (relevance score, token distribution, focus mode)
Choose SearchUnify if: you value g2 leader for 21 consecutive quarters (5+ years) in enterprise search - exceptional market validation vs newer rag startups
About CODY AI
CODY AI is business-focused no-code rag platform with source attribution. Business-focused RAG-as-a-Service platform enabling no-code AI assistant creation trained on custom knowledge bases. Acquired by Just Build It (May 2024), claims 100,000+ businesses as customers. TRUE RAG platform with Pinecone vector database, multi-LLM support (GPT-4, Claude 3.5, Gemini 1.5, Llama 3.1 on Enterprise), and comprehensive REST API. Differentiators: source attribution with every response, Focus Mode (inject 1,000 docs into context), 15-minute bot deployment. Critical gaps: NO direct SOC 2 certification (infrastructure partners only), NO official SDKs, NO native cloud storage integrations. $0-$249/month credit-based pricing. Founded in 2022, headquartered in United States, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
85/100
Starting Price
$29/mo
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, SearchUnify offers more competitive entry pricing. The platforms also differ in their primary focus: AI Chatbot 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
CODY AI
SearchUnify
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Supported formats: PDF, Word (.doc/.docx), PowerPoint (.ppt/.pptx), plain text files with 100MB maximum file size per document
Built-in text editor: Direct text input for creating knowledge base entries without file uploads
Website crawler (Premium/Advanced): Import up to 25,000 pages on Advanced tier with automatic recurring re-imports for up to 9 websites
Document capacity by tier: Free (100 documents), Basic (1,000), Premium (10,000), Advanced (25,000 documents + 25,000 crawled web pages)
Storage architecture: Amazon S3 with SSE-S3 encryption protocol for documents, Pinecone vector database (SOC 2 Type II certified) for embeddings
Dynamic chunking algorithm: Adjusts chunk size based on token distribution for optimal retrieval performance (specific parameters not publicly documented)
Manual retraining: Always available for immediate knowledge base updates across all plans
Automatic syncing: Limited to website sources only with recurring re-imports (not available for uploaded documents)
CRITICAL LIMITATION: No NO YouTube transcript support - cannot ingest video content from YouTube for training
CRITICAL LIMITATION: No NO native cloud integrations - Google Drive, Dropbox, Notion connections only via Zapier (adds friction vs direct OAuth)
LIMITATION: No NO audio file support (MP3, M4A), No NO video file support (MP4), No NO code file ingestion, No NO Excel/CSV direct import
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.
Integrations & Channels
Native Slack integration: Free for all users with /assign-bot command for channel-specific bot assignment and @mentions for queries
Native Discord integration: Users mention @Cody for queries within Discord servers (free for all users)
Zapier integration: Connects to 5,000+ apps including Telegram, Facebook Messenger, Google Sheets, Google Docs, WhatsApp (via ecosystem)
Website embedding (3 methods): Shareable links (direct URLs without site modification), inline embeds (widgets within page sections), popup embeds (floating chat bubbles)
REST API v1.0: Full API access on all paid plans with documentation at developers.meetcody.ai
CRITICAL GAPS: No NO Microsoft Teams native integration (Zapier workaround required), No NO WhatsApp Business native integration (Zapier only), No NO Google Drive/Dropbox/Notion native connections
LIMITATION: No NO webhook functionality explicitly documented in API - potential constraint for event-driven architectures and real-time notifications
Native Search Clients: Salesforce Service Console/Communities, ServiceNow, Zendesk Support/Help Center, Khoros Aurora/Classic, Slack
Slack /assign-bot command: Assign specific bots to dedicated channels for departmental organization (e.g., IT bot in #it-support, HR bot in #hr-questions)
Free for all users: Native integrations available even on Free plan ($0/month) vs competitors requiring paid tiers or Zapier workarounds
Discord @Cody mentions: Direct mention-based querying within Discord servers for community support or team collaboration
Context preservation: Conversation history maintained within Slack/Discord threads for multi-turn interactions
Competitive advantage: Zero-friction deployment for Slack/Discord workspaces vs API-based integrations requiring developer involvement (7.5/10 rated differentiator)
Use case fit: Internal documentation assistants, IT support bots, HR policy Q&A within existing communication channels
Automatic citation: Every AI response includes links to exact documents used for generation enabling click-through verification
Source verification interface: Centralized conversation logs allow examination of which documents informed each response for audit trails
Trust building: Users can validate AI answers against source material reducing hallucination concerns and increasing adoption confidence
Knowledge gap identification: Responses lacking sufficient sources highlight areas needing additional training data
Compliance advantage: Source traceability supports regulatory requirements for explainable AI in regulated industries (healthcare, finance, legal)
Competitive positioning: Explicit citation vs black-box responses in competitors positions CODY for accuracy-critical use cases (9/10 rated differentiator)
User feedback: Reviews highlight source attribution as primary trust-building feature reducing manual fact-checking burden
N/A
N/A
Focus Mode ( Core Differentiator)
Targeted context injection: Inject up to 1,000 specific documents into single conversation context vs retrieving from full knowledge base
Use cases: Department-specific queries (HR policies for HR team, engineering docs for dev team), project-scoped assistance, client-specific information isolation
Noise reduction: Constrains retrieval to relevant subset preventing irrelevant information from interfering with responses
API support: Focus Mode available via REST API conversations endpoint with document ID array parameter for programmatic control
Performance advantage: Smaller search space improves retrieval speed and relevance vs full-corpus semantic search
Unique capability: Few RAG platforms offer explicit context scoping at this granularity - most retrieve from entire knowledge base (8.5/10 rated differentiator)
N/A
N/A
Core Chatbot Features
Multilingual support: Build and interact with chatbots in any language with no language restrictions or translation layers
Conversation memory: Context retention with configurable token distribution (e.g., 70% context, 10% history, 20% response) for multi-turn interactions
Conversation history logging: Centralized interface with filtering by bot or date range, tiered retention (14 days Basic, 30 days Premium, 90 days Advanced)
Conversational Interface: Securely upload documents (PowerPoints, PDFs) or crawl entire websites to build company-specific knowledge base and quickly retrieve precise information
Traceable Source Attribution: Every answer comes with traceable sources letting users verify accuracy and track where specific information originated
Prompt templates: Shareable custom prompts with variables across team members for consistent bot behavior
Conversation sharing: Share conversations with team via dedicated sharing option for collaboration and quality review
Scratchpad feature: Save, refine, and use derivatives of AI-generated responses to improve specificity over time with micro-management capabilities
Bot Personality Customization: Complete control over bot personality and description to define how bot presents itself and engages with users when creating new bot
LIMITATION: No NO native lead capture - requires custom implementation via API or Zapier workflows (vs built-in form capture in competitors)
LIMITATION: No NO automated human handoff - escalation achieved only through prompt engineering with manual contact info (no automated queue routing or agent assignment)
LIMITATION: Note: Basic analytics only - conversation logs and usage monitoring without advanced dashboards for funnel analysis or trend identification
N/A
Reduces hallucinations by grounding replies in your data and adding source citations for transparency.
Benchmark Details
Handles multi-turn, context-aware chats with persistent history and solid conversation management.
Speaks 90+ languages, making global rollouts straightforward.
Includes extras like lead capture (email collection) and smooth handoff to a human when needed.
Widget Customization & White- Labeling
Header customization: Layout alignment, business logo upload, color schemes, title and subtitle text configuration
Chat interface styling: Message bubble size, background colors, bot and human avatar customization
Composer controls: Placeholder text customization, send button icon selection
Full translation support: Widget UI fully translatable to any language for global deployment consistency
White-labeling (Premium/Advanced): Complete CODY branding removal requires Premium ($99/month) or Advanced ($249/month) - not available on Free/Basic tiers
LIMITATION: No NO domain restriction capabilities documented - cannot limit widget usage to specific domains (security consideration for production deployments)
LIMITATION: Role-based access includes team member limits by tier (3/10/30 members on Basic/Premium/Advanced) with per-chatbot permission enforcement
N/A
N/A
L L M Model Options
Basic plan: GPT-3.5 Turbo only (1 credit per query)
Enterprise plan: Six LLM providers - Llama 3.1, Claude 3.5 Sonnet, GPT-4o, Gemini 1.5, Mixtral-8x7B, GPT-3.5 Turbo
Credit-based consumption: GPT-3.5 Turbo (1 credit), GPT-3.5 16K (5 credits), GPT-4 (10 credits) per query with transparent per-model costs
API model field: REST API returns 'model' field indicating which LLM generated each response for tracking and analysis
Proprietary optimizations: Scratchpad (micro-managing responses), Template Mode (pre-defined prompts), Reverse Vector Search (merging AI and user responses for relevance)
LIMITATION: No NO automatic model routing - users must manually select models, no dynamic routing based on query complexity or cost optimization (vs intelligent routing in competitors)
LIMITATION: Enterprise-only access to advanced models (Claude 3.5, Gemini 1.5, Llama 3.1) locks out SMBs on lower tiers from latest LLM capabilities
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)
REST API v1.0: Comprehensive with Bearer token authentication, last updated May 2024
Bots endpoint: List bots with keyword filtering for discovery and management
Conversations endpoint: Full CRUD operations with Focus Mode parameter (inject specific document IDs into context)
Messages endpoint: Send/receive with optional SSE streaming for real-time responses and progressive answer display
Documents endpoint: Upload files (up to 100MB max), create from text/HTML, import webpages programmatically
Folders endpoint: Organizational structure management for knowledge base hierarchy
Uploads endpoint: AWS S3 signed URLs for direct file uploads bypassing API size limits
Rate limiting: Standard headers (x-ratelimit-limit, x-ratelimit-remaining, x-ratelimit-reset, retry-after) with limits viewable in account settings
API changelog: Tracks breaking changes with explicit "Breaking" labels for version management
CRITICAL LIMITATION: No NO official SDKs for Python, JavaScript, Node.js, or any language - all integrations require direct REST API calls (development friction)
LIMITATION: No NO webhook functionality explicitly documented - limits event-driven architectures and real-time notification patterns
LIMITATION: Documentation quality functional but limited - clear endpoint docs with curl examples and response schemas but lacking tutorials, cookbooks, comprehensive code samples
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
Backs you up with cookbooks, code samples, and step-by-step guides for every skill level.
R A G Implementation & Accuracy
TRUE RAG architecture: Pinecone vector database (SOC 2 Type II certified) with Amazon S3 document storage and SSE-S3 encryption
Dynamic chunking: Algorithm adjusts chunk size based on token distribution for optimal retrieval (specific parameters not publicly documented)
Relevance Score configuration: Adjustable trade-off between accuracy and completeness for retrieval tuning
Token Distribution control: Split configuration between context, history, and response (e.g., 70% context, 10% history, 20% response) for resource allocation
Persist Prompt feature: Continuous re-emphasis of system prompt for instruction compliance and behavior consistency
Reverse Vector Search: Proprietary technique merging AI and user responses for improved relevance matching
Creativity Settings: Options for "creative," "balanced," or "factual" outputs controlling temperature and generation style
Hallucination mitigation: Source citation with every response enables verification, Focus Mode constrains responses to specific documents reducing irrelevant injection
LIMITATION: No NO published benchmark results or quantitative accuracy metrics - no public validation of RAG performance claims vs competitors
LIMITATION: User reviews note "accuracy relies heavily on the quality of uploaded documents" with occasional struggles reported about document facts
N/A
N/A
Performance & Accuracy
Response time: Sub-500ms end-to-end latency target for typical queries on Premium/Advanced plans using GPT-3.5 Turbo (verified from user reports and platform specifications)
Accuracy metrics: No publicly published accuracy benchmarks or F1 scores; user reviews on G2 (4.7/5 stars, 150+ reviews) and Capterra (4.8/5, 50+ reviews) report generally high satisfaction with answer quality when knowledge base is well-curated
Scalability: AWS infrastructure with isolated Kubernetes containers on Enterprise plan supports high-volume deployments; Free plan supports 250 queries/month, scales to "unlimited" on Enterprise with custom infrastructure
Reliability: No public SLA or uptime guarantees on Free/Basic/Premium/Advanced plans; Enterprise plan offers SLA guarantees with dedicated infrastructure (specific uptime % requires sales engagement)
Benchmarks: No published performance benchmarks comparing retrieval speed, accuracy, or latency against competitors (ChatBase, Vectara, CustomGPT); users report "accuracy relies heavily on quality of uploaded documents" with occasional struggles on complex queries
Quality indicators: Source attribution feature enables verification of AI responses; G2 reviews highlight accuracy as strength when knowledge base is comprehensive, with some users noting need for careful prompt engineering
Near Real-Time Analytics: Data refreshes within 120 seconds of capture for dashboard metrics
FRAG™ Hallucination Mitigation: 3-layer architecture (Federation, Retrieval, Augmented Generation) specifically designed to reduce false information
Vector Search Integration: Semantic similarity and keyword matching combined for improved retrieval accuracy
Multi-Repository Context: Documentation, forums, LMS unified for 360-degree enterprise context
User Feedback Loops: Continuous improvement through response validation and audit mechanisms
Fallback Generation: Maintains service during LLM downtime with alternative response mechanisms
YouTube Timestamp Search: Transcript-based retrieval returns exact audio segments for precise video content location
Delivers sub-second replies with an optimized pipeline—efficient vector search, smart chunking, and caching.
Independent tests rate median answer accuracy at 5/5—outpacing many alternatives.
Benchmark Results
Always cites sources so users can verify facts on the spot.
Maintains speed and accuracy even for massive knowledge bases with tens of millions of words.
Customization & Branding
UI customization: Full widget customization including header layout alignment, message bubble size/colors, background colors, bot and human avatars, composer placeholder text, send button icons
Branding control: Business logo upload, color schemes (header, chat interface, launcher button), title and subtitle text configuration, full translation support for widget UI in any language
White-labeling: Complete removal of Cody branding available on Premium ($99/month) and Advanced ($249/month) plans; Free and Basic plans display Cody branding on widgets
Custom domain: Not explicitly documented in public materials; likely requires Enterprise plan with custom deployment infrastructure (specifics require sales engagement)
Design flexibility: Launcher configuration with size adjustment, screen position (left/right/bottom), custom launcher icons; three embedding methods (shareable links, inline embeds, popup embeds) for flexible deployment
Mobile customization: Responsive widget design adapts to mobile devices; mobile-specific branding controls not separately documented (inherits desktop configuration)
LIMITATION: No documented domain restriction capabilities to limit widget usage to specific domains (security consideration for production deployments)
Role-based access: Team member limits by tier (3/10/30 members on Basic/Premium/Advanced) with per-chatbot permission enforcement and real-time updates
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
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.
No- Code Interface & Usability
Visual builder: Three-step bot creation process - (1) add data to knowledge base, (2) define bot purpose/personality, (3) test and share; no drag-and-drop interface, but prompt engineering UI with visual prompt builder including variables and template sharing
Setup complexity: 15-minute bot deployment from account creation to live widget (verified from marketing materials and user reviews); no technical expertise required for basic deployment
Learning curve: User reviews on G2 note "easy to set up" with "intuitive interface," but some users report learning curve for customizing bots to specific business needs despite no-code design; Capterra reviews highlight quick adoption for non-technical teams
Pre-built templates: 11+ templates including Marketing Assistant, HR Chatbot, IT Support, Customer Support, Sales Assistant, Training Bot, Translator AI, Hiring Assistant; each template includes sample prompts, recommended knowledge base content, and example queries
No-code workflows: Model switching (GPT-3.5/GPT-4/Claude/Gemini) without technical reconfiguration; conversation sharing and scratchpad feature for response refinement; testing simulator for pre-launch validation
User experience: G2 rating 4.7/5 (150+ reviews), Capterra 4.8/5 (50+ reviews); users praise ease of deployment and source attribution, note occasional need for prompt engineering expertise to optimize bot behavior
LIMITATION: No drag-and-drop conversation flow builder or visual automation designer like Botpress/Voiceflow; focuses on prompt-based configuration rather than graphical flow design
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.
Security & Privacy
CRITICAL LIMITATION: No CODY itself NOT SOC 2 certified - Help Center explicitly states "As an early stage startup, we are diligently working towards earning SOC 2 compliance"
Infrastructure compliance: Pinecone vector database (SOC 2 Type II certified), AWS S3 (PCI-DSS, HIPAA/HITECH, FedRAMP, FISMA compliant via AWS certification)
GDPR Compliant: Via AWS infrastructure in EU regions for European data residency and privacy requirements
Document storage: Amazon S3 with SSE-S3 encryption protocol for data at rest, TLS for transit
AI training policy: Customer data explicitly NOT used for training - "Your data will not be used to train any existing or new language model"
OpenAI data retention: API policy ensures data retained maximum 30 days for abuse monitoring only (not for model training)
Access controls: Per-chatbot permissions with real-time updates, API key management, role-based team member access
Enterprise security: Isolated Kubernetes containers on AWS with role-based security and custom infrastructure options
Procurement concern: Lack of direct SOC 2 certification may block enterprise adoption in regulated industries requiring vendor compliance attestations
SOC Certifications: SOC 1 Type 2, SOC 2 Type 2, SOC 3 from parent company Grazitti Interactive
ISO 27001:2013: Information Security Management System compliance
ISO 27701:2019: Privacy Information Management System certification
HIPAA Compliant: Healthcare data protection requirements met
GDPR Compliant: Acts as data processor with EU data protection compliance
Single-Tenant Architecture: Customer data isolation preventing cross-tenant information leakage
AES-256 Encryption: Data at rest protection with industry-standard encryption
TLS 1.3 in Transit: Latest transport layer security for data transmission
SSO Integration: SAML 2.0 with Okta, Azure AD, OneLogin, CyberArk, Google Workspace
FRAG Security: Sensitive data removal before third-party LLM transmission, response analysis preventing leakage, zero-retention policies for LLM interactions
Detailed Audit Trails: Prompts and responses logged for compliance with 30-day retention
RBAC: Super Admin, Admin, Moderator roles with configurable permissions and activity tracking
Admin Logs: 30-day retention with CSV export for compliance and security review
Protects data in transit with SSL/TLS and at rest with 256-bit AES encryption.
Holds SOC 2 Type II certification and complies with GDPR, so your data stays isolated and private.
Security Certifications
Offers fine-grained access controls—RBAC, two-factor auth, and SSO integration—so only the right people get in.
Observability & Monitoring
Conversation logs: Centralized view of all interactions across interface, API, and website widgets with searchability
Filtering capabilities: By bot or date range for quick access to specific conversation subsets
Source verification: Click-through to examine exact documents used for each response enabling audit trails
Usage tracking: Real-time credit consumption monitoring in dedicated usage tab for cost management
Tiered log retention: 14 days (Basic), 30 days (Premium), 90 days (Advanced) - historical analysis constrained on lower plans
Third-party mentions: Usage pattern monitoring, performance metrics, common question tracking, knowledge gap identification (features lack detailed public documentation)
LIMITATION: Note: Advanced analytics dashboard features mentioned in sources lack public screenshots or comprehensive documentation (transparency gap)
LIMITATION: No NO real-time alerting for conversation volume spikes, error rates, or performance degradation
LIMITATION: No NO funnel analytics or conversion tracking for lead generation use cases
30+ Pre-Built Metrics: Comprehensive analytics across search performance, conversion tracking, content gap analysis
Search Performance: Query trends, content source indexing status, click position tracking, Salesforce case creation, SearchUnifyGPT feedback
Conversion Tracking: Full user journey sessions, case deflection rates, popular documents, discussions-to-articles identification
Content Gap Analysis: Unsuccessful searches, no-click/no-result sessions, high-conversion results not on page one, content length insights
Near Real-Time Refresh: Data updates within 120 seconds of capture for analytics dashboards
SUVA Metrics: Deflection rate, handover rate, abandonment rate, average response time, CSAT scores, LLM token usage tracking
Actionable Insights: AI-generated plain-English recommendations from analytics data vs dashboards requiring manual interpretation
Custom User Attributes: Organization-specific analytics dimensions for tailored reporting
Admin Activity Logs: User activity tracking, configuration changes, feature usage with 30-day retention and CSV export
Comes with a real-time analytics dashboard tracking query volumes, token usage, and indexing status.
Lets you export logs and metrics via API to plug into third-party monitoring or BI tools.
Analytics API
Provides detailed insights for troubleshooting and ongoing optimization.
15-minute bot deployment: Three-step process - (1) add data to knowledge base, (2) define bot purpose/personality, (3) test and share
11+ pre-built templates: Marketing Assistant, HR Chatbot, IT Support, Customer Support, Sales Assistant, Training Bot, Translator AI, Hiring Assistant
Template components: Sample prompts, recommended knowledge base content, example queries for rapid deployment
Model-agnostic interface: Switch between GPT-3.5, GPT-4, Claude, Gemini without technical reconfiguration
Prompt engineering UI: Visual prompt builder with variables, template sharing across team members, version control
Testing simulator: Test bot responses before publishing with conversation preview and refinement loops
Role-based access: Team member limits (3/10/30 by tier), per-chatbot permission enforcement, real-time permission updates
Target audience advantage: Business teams deploy knowledge assistants without developer resources vs API-centric platforms requiring technical expertise (9/10 rated differentiator for non-technical users)
N/A
N/A
Proprietary R A G Optimizations ( Differentiator)
Scratchpad: Save, refine, and use derivatives of AI-generated responses to improve specificity through micro-management and iterative enhancement
Template Mode: Pre-defined prompts with variables for consistent behavior patterns across conversations and use cases
Reverse Vector Search: Proprietary technique merging AI responses and user inputs for improved relevance matching and context awareness
Dynamic chunking: Algorithm adjusts chunk size based on token distribution rather than fixed-size chunks (adaptive optimization)
Persist Prompt: Continuous re-emphasis of system prompt throughout conversation preventing instruction drift in long conversations
Creativity Settings: Granular control over "creative," "balanced," or "factual" outputs for use-case-specific tone adjustment
Competitive positioning: Proprietary optimizations differentiate from standard RAG implementations but lack published performance benchmarks (7/10 rated differentiator)
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Pricing & Scalability
Free plan: $0/month - 100 credits, 100 documents, 1 team member, 1 widget, NO API access, NO crawler, monthly query limit 250
Basic plan: $29/month - 2,500 credits, 1,000 documents, 3 team members, 14-day conversation logs, API access, GPT-3.5 only
Enterprise considerations: Lack of direct SOC 2 certification (infrastructure-partner-only compliance) may block regulated industry adoption requiring vendor attestations
Developer experience: Comprehensive REST API with SSE streaming but NO official SDKs requiring direct HTTP calls vs SDK-equipped platforms
Competitive positioning: Business-focused RAG platform emphasizing no-code deployment and source transparency vs developer-centric platforms with enterprise compliance (rated 7.5/10 as RAG platform)
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
Competitive Positioning
vs CustomGPT: CODY excels in no-code deployment and source attribution; CustomGPT excels in enterprise compliance (direct SOC 2) and official SDKs
vs Vectara: CODY offers simpler pricing and no-code interface; Vectara provides enterprise-grade accuracy benchmarks and HHEM hallucination detection
vs Pinecone Assistant: Both use Pinecone vector database; CODY differentiates with Focus Mode and business templates; Pinecone Assistant offers deeper infrastructure control
vs ChatBase/SiteGPT: CODY provides TRUE RAG architecture vs simpler chatbot platforms; Focus Mode and multi-LLM support vs single-model implementations
Market niche: Business-focused RAG platform for teams needing no-code deployment with source transparency, NOT developer tool requiring technical implementation
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
Customer Base & Case Studies
Scale claim: 100,000+ businesses served (unverified, company-provided claim)
Acquisition: Acquired by Just Build It in May 2024 demonstrating market validation and growth trajectory
Use case examples: Customer support automation, HR policy Q&A, IT support documentation, sales enablement, internal knowledge management, training assistants
Target market: SMBs and mid-market companies seeking knowledge base automation without dedicated AI/ML engineering resources
User feedback themes: Ease of deployment praised, source attribution valued for trust, accuracy concerns noted for complex document sets
Common use cases: "AI virtual employee" positioning for customer support, HR, IT support, sales assistance, marketing, training, and hiring workflows
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
Credit-Based Consumption: GPT-3.5 Turbo (1 credit), GPT-3.5 16K (5 credits), GPT-4 (10 credits) per query with transparent per-model costs
Model-Agnostic Architecture: Users stay current with latest LLM updates without retraining bots; bring your own API key for supported LLMs (Claude, Mistral, GPT, Gemini)
Claude 3 Default: Defaults to Claude 3 from Anthropic for code generation, autocomplete, and chat features vs competitors relying solely on GPT models
LIMITATION: No automatic model routing based on query complexity or cost optimization - users must manually select 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
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
TRUE RAG Architecture: Pinecone vector database (SOC 2 Type II certified) with Amazon S3 document storage using SSE-S3 encryption protocol
Dynamic Chunking Algorithm: Adjusts chunk size based on token distribution for optimal retrieval performance (specific parameters not publicly documented)
Relevance Score Configuration: Adjustable trade-off between accuracy and completeness for retrieval tuning
Token Distribution Control: Split configuration between context, history, and response (e.g., 70% context, 10% history, 20% response)
Reverse Vector Search: Proprietary technique merging AI and user responses for improved relevance matching
Context Window: Claude 2 integration provides up to 100K context windows for comprehensive codebase analysis
Advanced Chunking: Comprehensive data segmentation including metadata for superior data management across various file formats
LIMITATION: No published benchmark results or quantitative accuracy metrics for RAG performance validation
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
Primary Departments: Marketing teams (creative strategies, campaign insights), HR departments (employee communication, query management), IT support (technical troubleshooting), Sales departments (AI-driven assistance)
Internal Operations: Answering internal or customer FAQs automatically, training new team members with AI support, generating reports/email replies/summaries using company data, searching thousands of documents instantly
Code Assistance: Engineers saving 5-6 hours per week, writing code 2x faster with AI-powered context-aware code generation and autocomplete
Industries: Financial services (trusted by 4/6 top US banks), technology companies (7/10 top public tech companies), healthcare, professional service firms, government agencies (15+ US agencies)
Team Sizes: Startups managing internal documentation to enterprises coordinating teams across regions; 100,000+ businesses served globally
Educational Use Cases: Educational institutions training students in AI applications, legal firms organizing and retrieving case documents
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)
Credit System: GPT-3.5 Turbo (1 credit), GPT-3.5 16K (5 credits), GPT-4 (10 credits) per query - enables budget forecasting (2,500 GPT-3.5 queries or 250 GPT-4 queries on Basic)
14-Day Free Trials: Available for all paid plans to evaluate features before commitment
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
API Documentation: developers.meetcody.ai with endpoint reference, curl examples, response schemas, API changelog with breaking change labels
Help Center: intercom.help/cody/en/ with getting started guides, compliance information, security bulletins
Community Support: Active Discord community for peer support, troubleshooting, and best practices; GitHub discussions for developer engagement
Email Support: support@meetcody.ai available for all users across all plans
Response Times: Generally praised for responsiveness; Advanced plan includes dedicated account manager for onboarding and optimization guidance
Learning Resources: Blog with tutorials and guides for use case implementation and platform features
Enterprise SLA: Guaranteed response times and uptime commitments (specifics require sales engagement, not publicly documented)
LIMITATION: NO phone support or live chat on any tier (email and community only)
Documentation Quality: Functional but limited - clear endpoint docs with response schemas but lacking tutorials, cookbooks, comprehensive code samples for advanced implementations
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
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
Real-Time Knowledge Updates: Always available manual retraining for immediate knowledge base updates across all plans
Automatic Syncing: Limited to website sources only with recurring re-imports - not available for uploaded documents
Bot Personality Customization: Create custom conversation starters tailored to specific tasks, adjust behavior, tone, and focus to suit each use case
Focus Mode: Generate highly specialized responses based on selected documents for targeted tasks with up to 1,000 specific documents injected into conversation context
Scratchpad for Fine-Tuning: Fine-tune bot responses and knowledge base interactions improving accuracy and relevance of future responses
Custom Prompts: Define bot purpose and personality during creation with shareable prompt templates across team members
Configurable Token Distribution: Adjust split between context, history, and response (e.g., 70% context, 10% history, 20% response)
LIMITATION: No NO programmatic personality management - tone/behavior settings dashboard-only, cannot modify per-user or via API (global configuration only)
LIMITATION: Knowledge base updates require manual intervention - no real-time sync from cloud sources (Google Drive, Dropbox, Notion) except website crawling
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
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.
Additional Considerations
Accuracy Heavily Data-Dependent: Response quality relies on quality and comprehensiveness of uploaded knowledge base - "accuracy relies heavily on quality of uploaded documents"
Learning Curve Exists: Initial setup and customization can be complex for new users despite "easy to set up" reputation - learning curve for customizing bots to specific business needs
Limited Complex Coding: Performs well for simple tasks but struggles with deeper logic, scalability issues, or nuanced multi-step coding challenges
Data Quality Critical: Occasional struggles with document facts - difficulty counting references, performing word counts, handling complex document sets
Cost for Small Businesses: Advanced features and Enterprise-only access (Claude 3.5, Gemini 1.5, Llama 3.1) expensive for smaller businesses
White-Label Minimum: Complete Cody branding removal requires Premium ($99/month) or Advanced ($249/month) - not available on Free/Basic tiers
Performance with Large Data: Speed may slow with large datasets or complex codebases on less powerful systems; requires stable internet (cloud-based)
Compliance Gap: Cody itself NOT SOC 2 certified as early-stage startup "diligently working towards earning SOC 2 compliance" - may block enterprise procurement
Infrastructure Compliance Only: Pinecone (SOC 2 Type II), AWS S3 (PCI-DSS, HIPAA/HITECH, FedRAMP) certified but Cody platform not directly certified
Best For: Business teams needing no-code deployment with 15-minute bot creation and source transparency for internal knowledge management
NOT Ideal For: Enterprises requiring direct SOC 2 vendor certification, native cloud storage sync, YouTube content ingestion, or deep technical problem-solving
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.
Limitations & Considerations
Learning Curve: Initial setup and customization complex for new users; G2 users note "easy to set up" but learning curve exists for customizing bots to specific business needs despite no-code design
Accuracy Dependencies: Response quality heavily relies on quality and comprehensiveness of uploaded knowledge base; user reviews note "accuracy relies heavily on quality of uploaded documents" with occasional struggles on complex queries
Complex Coding Challenges: Limited ability to handle complex, multi-step coding challenges; performs well for simple tasks but struggles with deeper logic, scalability issues, or nuanced coding questions
Data Quality Critical: Occasional struggles with facts about documents - difficulty counting references, performing word counts, handling complex document sets
NO YouTube Transcripts: Cannot ingest video content from YouTube for training
NO Native Cloud Integrations: Google Drive, Dropbox, Notion connections only via Zapier (adds friction vs direct OAuth)
Performance Issues: Performance speed may slow with large datasets or complex codebases on less powerful systems; requires stable internet connection (cloud-based)
Cost Considerations: Advanced features and Enterprise-only access (Claude 3.5, Gemini 1.5, Llama 3.1) can be expensive for smaller businesses; white-labeling requires Premium ($99/month) minimum
NOT Ideal For: Enterprises requiring direct SOC 2 certification (infrastructure-only compliance may block procurement), teams needing deep technical problem-solving for critical systems without traditional development practices, organizations needing native cloud storage sync or YouTube content ingestion
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
N/A
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
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 CODY AI and SearchUnify are capable platforms that serve different market segments and use cases effectively.
When to Choose CODY AI
You value true rag architecture with pinecone vector database and configurable retrieval parameters (relevance score, token distribution, focus mode)
Source attribution with every response - click-through to exact documents used for generation (transparency and trust differentiator)
Focus Mode unique capability: inject up to 1,000 specific documents into conversation context for targeted responses vs full knowledge base
Best For: TRUE RAG architecture with Pinecone vector database and configurable retrieval parameters (relevance score, token distribution, Focus Mode)
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 CODY AI 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
CODY AI starts at $29/month, 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 CODY AI 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
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Last updated: December 11, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
The most accurate RAG-as-a-Service API. Deliver production-ready reliable RAG applications faster. Benchmarked #1 in accuracy and hallucinations for fully managed RAG-as-a-Service API.
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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