Contextual AI vs LiveChat

Make an informed decision with our comprehensive comparison. Discover which RAG solution perfectly fits your needs.

More about LiveChatMore about Contextual AIGet Started Today
Priyansh Khodiyar's avatar
Priyansh KhodiyarDevRel at CustomGPT.ai

Fact checked and reviewed by Bill Cava

Published: 01.04.2025Updated: 25.04.2025

In this comprehensive guide, we compare Contextual AI and LiveChat 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 Contextual AI and LiveChat, 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 Contextual AI if: you value invented by the original creator of rag technology
  • Choose LiveChat if: you value mature 20+ year platform with proven enterprise reliability (adobe, paypal, ikea, samsung, best buy)

About Contextual AI

Contextual AI Landing Page Screenshot

Contextual AI is rag 2.0 platform for enterprise-grade specialized ai agents. Contextual AI is an enterprise platform that pioneered RAG 2.0 technology, enabling organizations to build specialized RAG agents with exceptional accuracy for complex, knowledge-intensive workloads through end-to-end optimized systems. Founded in 2023, headquartered in Mountain View, CA, the platform has established itself as a reliable solution in the RAG space.

Overall Rating
91/100
Starting Price
Custom

About LiveChat

LiveChat Landing Page Screenshot

LiveChat is enterprise live chat platform with ai-augmented customer support workflows. Mature 20+ year live chat platform owned by publicly-traded Text S.A. (WSE: TXT, $88.9M annual revenue) serving 37,000+ businesses including Adobe, PayPal, IKEA. NOT a RAG-as-a-Service platform—operates as human-agent live chat with AI augmentation features. Proprietary AI engine (not LLM model selection), limited knowledge sources (PDFs/websites only), no vector database controls, no anti-hallucination mechanisms. Strong for traditional customer support workflows. $20-$59/agent/month + $52/month ChatBot addon. Founded in 2002, headquartered in Wroclaw, Poland, the platform has established itself as a reliable solution in the RAG space.

Overall Rating
86/100
Starting Price
$20/mo

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 Customer Support. 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

logo of contextualai
Contextual AI
logo of livechat
LiveChat
logo of customGPT logo
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
  • Easily brings in both unstructured files (PDFs, HTML, images, charts) and structured data (databases, spreadsheets) through ready-made connectors.
  • Does multimodal retrieval—turns images and charts into embeddings so everything is searchable together. Source
  • Hooks into popular SaaS tools like Slack, GitHub, and Google Drive for integrated data flow.
  • Supported formats – PDFs and website crawling only (max 2,000 pages)
  • Plan limits – Team: 10 files/3 sites, Business: 30 files/10 sites
  • ⚠️ NO DOCX, TXT, CSV, Excel – No audio, video, code files (vs 1,400+ formats)
  • ⚠️ NO cloud storage sync – No Google Drive, Dropbox, OneDrive, Notion, Confluence
  • 1,400+ file formats – PDF, DOCX, Excel, PowerPoint, Markdown, HTML + auto-extraction from ZIP/RAR/7Z archives
  • Website crawling – Sitemap indexing with configurable depth for help docs, FAQs, and public content
  • Multimedia transcription – AI Vision, OCR, YouTube/Vimeo/podcast speech-to-text built-in
  • Cloud integrations – Google Drive, SharePoint, OneDrive, Dropbox, Notion with auto-sync
  • Knowledge platforms – Zendesk, Freshdesk, HubSpot, Confluence, Shopify connectors
  • Massive scale – 60M words (Standard) / 300M words (Premium) per bot with no performance degradation
Integrations & Channels
  • Built for API integration first—no plug-and-play web widget included.
  • Enterprise-grade endpoints and a Snowflake Native App option make tight data integration straightforward. Source
  • Messaging platforms – Website, Facebook, WhatsApp, Apple Messages, Telegram, SMS, email
  • 200+ marketplace integrations – Zapier (5,000+ apps), HubSpot, Salesforce, Zendesk, Intercom
  • E-commerce platforms – Shopify, WooCommerce, BigCommerce native plugins
  • ✅ Custom integrations – Webhooks with JSON payloads, 10s response timeout
  • Website embedding – Lightweight JS widget or iframe with customizable positioning
  • CMS plugins – WordPress, WIX, Webflow, Framer, SquareSpace native support
  • 5,000+ app ecosystem – Zapier connects CRMs, marketing, e-commerce tools
  • MCP Server – Integrate with Claude Desktop, Cursor, ChatGPT, Windsurf
  • OpenAI SDK compatible – Drop-in replacement for OpenAI API endpoints
  • LiveChat + Slack – Native chat widgets with human handoff capabilities
Core Chatbot Features
  • Powers advanced RAG agents with multi-hop retrieval and chain-of-thought reasoning for tough questions.
  • Uses a reranker plus groundedness scoring for factual answers with precise attribution. Source
  • “Instant Viewer” highlights the exact source text backing each part of the answer.
N/A
  • ✅ #1 accuracy – Median 5/5 in independent benchmarks, 10% lower hallucination than OpenAI
  • ✅ Source citations – Every response includes clickable links to original documents
  • ✅ 93% resolution rate – Handles queries autonomously, reducing human workload
  • ✅ 92 languages – Native multilingual support without per-language config
  • ✅ Lead capture – Built-in email collection, custom forms, real-time notifications
  • ✅ Human handoff – Escalation with full conversation context preserved
Customization & Branding
  • Lets you tweak system prompts, tone, and content filters to match company policies—on the back end.
  • No out-of-the-box UI builder; you’ll embed it in your own branded front end. Source
N/A
  • Full white-labeling included – Colors, logos, CSS, custom domains at no extra cost
  • 2-minute setup – No-code wizard with drag-and-drop interface
  • Persona customization – Control AI personality, tone, response style via pre-prompts
  • Visual theme editor – Real-time preview of branding changes
  • Domain allowlisting – Restrict embedding to approved sites only
L L M Model Options
  • Runs on its own Grounded Language Model (GLM) tuned for RAG—tests show ~88 % factual accuracy.
  • Exposes standalone model APIs (reranker, generator) with simple token-based pricing. Source
  • ⚠️ NO model selection – Proprietary AI engine only
  • ⚠️ NO GPT-4, Claude, Gemini – Cannot choose LLM providers
  • ⚠️ NO BYOLLM – No custom models or fine-tuning
  • ⚠️ Opaque processing – Architecture and training data not documented (3/10 flexibility)
  • GPT-5.1 models – Latest thinking models (Optimal & Smart variants)
  • GPT-4 series – GPT-4, GPT-4 Turbo, GPT-4o available
  • Claude 4.5 – Anthropic's Opus available for Enterprise
  • Auto model routing – Balances cost/performance automatically
  • Zero API key management – All models managed behind the scenes
Developer Experience ( A P I & S D Ks)
  • Offers solid REST APIs and a Python SDK for managing agents, ingesting data, and querying. Source
  • Endpoints cover tuning, evaluation, and standalone components—all with clear, token-based pricing.
  • Agent Chat API v3.5 – REST and WebSocket with OAuth 2.1 PKCE
  • Mobile SDKs – iOS (Swift, iOS 15.6+), Android (Kotlin via Gradle)
  • ✅ Strong documentation – Postman collections, tutorials, Discord community
  • ⚠️ NO Python SDK – JavaScript/mobile only, limits backend integration
  • REST API – Full-featured for agents, projects, data ingestion, chat queries
  • Python SDK – Open-source customgpt-client with full API coverage
  • Postman collections – Pre-built requests for rapid prototyping
  • Webhooks – Real-time event notifications for conversations and leads
  • OpenAI compatible – Use existing OpenAI SDK code with minimal changes
Performance & Accuracy
  • RAG 2.0 approach tops industry benchmarks for document understanding and factuality. Source
  • Handles large, noisy datasets with multi-hop retrieval and strong reranking for grounded answers.
  • Real-time delivery – Sub-second message delivery, praised for reliability
  • Scalability – 37,000+ businesses, enterprise customers (Adobe, PayPal, IKEA)
  • ⚠️ NO accuracy benchmarks – No published AI retrieval metrics
  • ⚠️ Operational focus – Metrics for queue times, agent response, not AI accuracy
  • Sub-second responses – Optimized RAG with vector search and multi-layer caching
  • Benchmark-proven – 13% higher accuracy, 34% faster than OpenAI Assistants API
  • Anti-hallucination tech – Responses grounded only in your provided content
  • OpenGraph citations – Rich visual cards with titles, descriptions, images
  • 99.9% uptime – Auto-scaling infrastructure handles traffic spikes
Customization & Flexibility ( Behavior & Knowledge)
  • Create multiple datastores and link them to agents by role or permission for fine-grained access.
  • Tune the LLM on your own data, add guardrails, and embed custom logic as needed. Source
N/A
  • Live content updates – Add/remove content with automatic re-indexing
  • System prompts – Shape agent behavior and voice through instructions
  • Multi-agent support – Different bots for different teams
  • Smart defaults – No ML expertise required for custom behavior
Pricing & Scalability
  • Usage-based pricing tailored for enterprises—cost scales with agent capacity, data size, and query load. Source
  • Standalone component APIs are priced per token, letting you mix and match pieces as you grow.
  • Starter – $20/agent/month, 60-day history, 1 user
  • Team – $41/agent/month, unlimited history, 10 files/3 sites
  • Business – $59/agent/month, staffing predictions, 30 files/10 sites
  • Enterprise – Custom pricing, min 5 seats, SSO/SAML, white-label, HIPAA
  • ⚠️ Cost escalation – 10 agents + ChatBot = $642/month ($7,704/year)
  • Standard: $99/mo – 60M words, 10 bots
  • Premium: $449/mo – 300M words, 100 bots
  • Auto-scaling – Managed cloud scales with demand
  • Flat rates – No per-query charges
Security & Privacy
  • SOC 2 compliant with encryption in transit and at rest; deploy on-prem or in a VPC for full sovereignty. Source
  • Implements role-based permissions and query-time access checks to keep data secure.
  • ✅ Seven certifications – SOC 2, GDPR, HIPAA, ISO 27001, PCI DSS, FedRAMP, CSA Star
  • Encryption – TLS transit, AES-256 at rest
  • Data residency – EU (Poland) compliance with account isolation
  • ⚠️ SSO/SAML Enterprise only – Significant gap for mid-market identity management
  • SOC 2 Type II + GDPR – Third-party audited compliance
  • Encryption – 256-bit AES at rest, SSL/TLS in transit
  • Access controls – RBAC, 2FA, SSO, domain allowlisting
  • Data isolation – Never trains on your data
Observability & Monitoring
  • Built-in evaluation shows groundedness scores, retrieval metrics, and logs every step. Source
  • Plugs into external monitoring tools and supports detailed logging for audits and troubleshooting.
  • Real-time monitoring – Agent status, queue depth, visitor activity dashboards
  • Chat metrics – Volume, missed chats, response times, agent performance, CSAT
  • Staffing predictions – AI scheduling optimization (Business+ plans)
  • ⚠️ NO AI metrics – No retrieval accuracy, semantic search, hallucination monitoring
  • Real-time dashboard – Query volumes, token usage, response times
  • Customer Intelligence – User behavior patterns, popular queries, knowledge gaps
  • Conversation analytics – Full transcripts, resolution rates, common questions
  • Export capabilities – API export to BI tools and data warehouses
Support & Ecosystem
  • High-touch enterprise support with solution engineers and technical account managers.
  • Grows its ecosystem via partnerships (e.g., Snowflake) and industry thought leadership. Source
  • ✅ 24/7 support – Live chat and email, consistently praised (4.5/5 G2, 4.6/5 Capterra)
  • Developer docs – Extensive at developers.livechat.com with Postman, tutorials
  • Enterprise SLA – Guaranteed response times on custom contracts
  • Common criticisms – Rising prices, per-agent scaling costs, fragmented ChatBot product
  • Comprehensive docs – Tutorials, cookbooks, API references
  • Email + in-app support – Under 24hr response time
  • Premium support – Dedicated account managers for Premium/Enterprise
  • Open-source SDK – Python SDK, Postman, GitHub examples
  • 5,000+ Zapier apps – CRMs, e-commerce, marketing integrations
Additional Considerations
  • Great for mission-critical apps that need multimodal retrieval and advanced reasoning.
  • Requires more up-front setup and technical know-how than no-code tools—best for teams with ML expertise.
  • Handles complex needs like role-based data access and evolving multimodal content. Source
  • ⚠️ Platform classification – Human-agent live chat with AI, NOT RAG-as-a-Service
  • ✅ Primary strength – Customer support workflows with 200+ integrations, seven certifications
  • ⚠️ Knowledge gap – PDFs/websites only, NO DOCX/CSV/Excel/audio/video/code files
  • ⚠️ NO RAG infrastructure – No vector DB, embeddings, chunking, similarity thresholds
  • Time-to-value – 2-minute deployment vs weeks with DIY
  • Always current – Auto-updates to latest GPT models
  • Proven scale – 6,000+ organizations, millions of queries
  • Multi-LLM – OpenAI + Claude reduces vendor lock-in
No- Code Interface & Usability
  • Web console helps manage agents, but there's no drag-and-drop chatbot builder.
  • UI integration is a coding project. APIs are full-featured, but non-tech users will need developer help.
N/A
  • 2-minute deployment – Fastest time-to-value in the industry
  • Wizard interface – Step-by-step with visual previews
  • Drag-and-drop – Upload files, paste URLs, connect cloud storage
  • In-browser testing – Test before deploying to production
  • Zero learning curve – Productive on day one
Competitive Positioning
  • Market position: Enterprise RAG 2.0 platform with proprietary Grounded Language Model (GLM) optimized for factual accuracy and multimodal retrieval capabilities
  • Target customers: Large enterprises and ML teams requiring mission-critical AI applications with advanced reasoning, multimodal content handling (images, charts), and strict accuracy requirements (88% factual accuracy benchmarked)
  • Key competitors: OpenAI Enterprise, Azure AI, Deepset, Vectara.ai, and custom-built RAG solutions using LangChain/Haystack
  • Competitive advantages: Proprietary GLM model with superior RAG performance, multimodal retrieval (images/charts), SOC 2 compliance with VPC/on-prem deployment options, Snowflake Native App integration, groundedness scoring with "Instant Viewer" for source attribution, and multi-hop retrieval with chain-of-thought reasoning
  • Pricing advantage: Usage-based enterprise pricing with standalone component APIs (reranker, generator) priced per token; flexible for organizations that want to mix and match components; best value for high-accuracy, high-volume use cases
  • Use case fit: Ideal for mission-critical enterprise applications requiring multimodal retrieval (technical documentation with diagrams), domain-specific AI agents with advanced reasoning, and organizations needing role-based data access with query-time permission checks
N/A
  • Market position – Leading RAG platform balancing enterprise accuracy with no-code usability. Trusted by 6,000+ orgs including Adobe, MIT, Dropbox.
  • Key differentiators – #1 benchmarked accuracy • 1,400+ formats • Full white-labeling included • Flat-rate pricing
  • vs OpenAI – 10% lower hallucination, 13% higher accuracy, 34% faster
  • vs Botsonic/Chatbase – More file formats, source citations, no hidden costs
  • vs LangChain – Production-ready in 2 min vs weeks of development
A I Models
  • Grounded Language Model (GLM): Proprietary model tuned specifically for RAG with ~88% factual accuracy on FACTS benchmark
  • Industry-Leading Groundedness: GLM achieves 88% vs. Gemini 2.0 Flash (84.6%), Claude 3.5 Sonnet (79.4%), GPT-4o (78.8%) on factuality benchmarks
  • Inline Attribution: Model provides citations showing exact source documents for each part of response as generated
  • Standalone APIs: Exposes separate reranker and generator APIs with simple token-based pricing for flexible integration
  • Model-Agnostic Option: Platform supports integration with other LLMs if needed for specific use cases
  • Optimized for RAG: GLM specifically designed for retrieval-augmented generation scenarios vs. general-purpose LLMs
  • Proprietary engine – Internal NLP, doesn't rely on OpenAI/Bard/Bing
  • ⚠️ NO LLM selection – Cannot choose GPT-4, Claude, Gemini
  • ⚠️ Opaque architecture – Model training data, capabilities not documented
  • ⚠️ NO BYOLLM – No model routing, fine-tuning, custom models (3/10 flexibility)
  • OpenAI – GPT-5.1 (Optimal/Smart), GPT-4 series
  • Anthropic – Claude 4.5 Opus/Sonnet (Enterprise)
  • Auto-routing – Intelligent model selection for cost/performance
  • Managed – No API keys or fine-tuning required
R A G Capabilities
  • RAG 2.0 Architecture: Advanced approach tops industry benchmarks for document understanding and factuality with multi-hop retrieval
  • Multimodal Retrieval: Turns images and charts into embeddings for unified search across text and visual content
  • Groundedness Scoring: Built-in evaluation shows groundedness scores with "Instant Viewer" highlighting exact source text backing each answer part
  • Reranker + Scoring: Uses reranker plus groundedness scoring for factual answers with precise attribution
  • Multi-Hop Retrieval: Advanced RAG agents with multi-hop retrieval and chain-of-thought reasoning for tough questions
  • Handles Noisy Datasets: Strong reranking and retrieval for large, noisy datasets with multiple datastores by role or permission
  • Query-Time Access Checks: Role-based permissions with query-time access validation for secure data retrieval
  • ⚠️ NOT RAG platform – No vector DB, embedding controls, retrieval pipeline
  • ⚠️ Limited sources – PDFs and websites only (max 2,000 pages, 10-30 files)
  • ⚠️ Human-agent focus – Agent workflows, not autonomous retrieval (2/10 RAG rating)
  • GPT-4 + RAG – Outperforms OpenAI in independent benchmarks
  • Anti-hallucination – Responses grounded in your content only
  • Automatic citations – Clickable source links in every response
  • Sub-second latency – Optimized vector search and caching
  • Scale to 300M words – No performance degradation at scale
Use Cases
  • Industries Served: Finance, technology, media, professional services, regulated industries (healthcare, telecommunications) requiring high-accuracy AI
  • Notable Customers: HSBC (banking), Qualcomm (technology), The Economist (media) demonstrating enterprise adoption
  • Mission-Critical Applications: Applications where factual accuracy is paramount and hallucinations must be minimized
  • Multimodal Use Cases: Technical documentation with diagrams, charts in business documents, visual content requiring understanding
  • Domain-Specific AI Agents: Custom agents requiring advanced reasoning with access to structured and unstructured data
  • Role-Based Access: Organizations needing fine-grained data access control with query-time permission enforcement
  • Team Sizes: Large enterprises and ML teams with technical expertise for integration and deployment
  • ✅ Customer support – Live chat for 37,000+ businesses with AI agent augmentation
  • ✅ E-commerce – Shopify/WooCommerce/BigCommerce native integrations
  • ✅ Multi-channel – Website, Facebook, WhatsApp, Apple Messages, Telegram, SMS
  • ⚠️ NOT suitable for – Autonomous knowledge retrieval, programmatic document search
  • Customer support – 24/7 AI handling common queries with citations
  • Internal knowledge – HR policies, onboarding, technical docs
  • Sales enablement – Product info, lead qualification, education
  • Documentation – Help centers, FAQs with auto-crawling
  • E-commerce – Product recommendations, order assistance
Security & Compliance
  • SOC 2 Compliant: Security compliance with encryption in transit and at rest for enterprise requirements
  • Deployment Options: Cloud, on-premise, or VPC deployment for full data sovereignty and compliance needs
  • Role-Based Permissions: Implements role-based permissions with query-time access checks to keep sensitive data secure
  • Encryption: Data encrypted in transit and at rest with enterprise-grade security protocols
  • Snowflake Partnership: Snowflake Native App option enables tight, secure data integration within customer environments
  • Data Sovereignty: On-prem and VPC options allow complete control over data location and access
  • ✅ Seven certifications – SOC 2, GDPR, HIPAA, ISO 27001, PCI DSS, FedRAMP, CSA Star
  • ✅ Data privacy – Customer data never used for training, zero-retention policies
  • Encryption – TLS transit, AES-256 at rest, regional data residency
  • ⚠️ HIPAA/SSO Enterprise only – Min 5 seats ($6,000/year minimum)
  • SOC 2 Type II + GDPR – Regular third-party audits, full EU compliance
  • 256-bit AES encryption – Data at rest; SSL/TLS in transit
  • SSO + 2FA + RBAC – Enterprise access controls with role-based permissions
  • Data isolation – Never trains on customer data
  • Domain allowlisting – Restrict chatbot to approved domains
Pricing & Plans
  • Free Tier: Credits for first 1M input and 1M output tokens to evaluate platform capabilities
  • Usage-Based Pricing: Enterprise pricing tailored by agent capacity, data size, and query load for scalability
  • Token-Based Components: Standalone component APIs (reranker, generator) priced per token for flexible mix-and-match
  • Enterprise Custom Pricing: Pricing details require sales engagement for production deployments and dedicated instances
  • Buy Additional Credits: Users can purchase credits as needs grow beyond free tier allocation
  • Best Value For: High-accuracy, high-volume enterprise use cases requiring multimodal retrieval and advanced reasoning
  • Starter – $20/agent/month, 60-day history, 1 user
  • Team – $41/agent/month, unlimited history, 400 users, AI features
  • Business – $59/agent/month, staffing predictions, full AI features
  • Enterprise – Custom pricing (min 5 seats), SSO/SAML, white-label, HIPAA
  • ⚠️ Cost escalation – Per-agent model criticized for scaling vs project pricing
  • Standard: $99/mo – 10 chatbots, 60M words, 5K items/bot
  • Premium: $449/mo – 100 chatbots, 300M words, 20K items/bot
  • Enterprise: Custom – SSO, dedicated support, custom SLAs
  • 7-day free trial – Full Standard access, no charges
  • Flat-rate pricing – No per-query charges, no hidden costs
Support & Documentation
  • High-Touch Enterprise Support: Solution engineers and technical account managers for dedicated customer success
  • API Documentation: Solid REST APIs and Python SDK documentation for managing agents, ingesting data, and querying
  • Endpoint Coverage: APIs for tuning, evaluation, standalone components with clear, token-based pricing transparency
  • Partnership Ecosystem: Grows via partnerships (Snowflake) and industry thought leadership for enterprise integration
  • Learning Resources: Technical documentation and integration guides for ML teams and developers
  • Response Times: Enterprise support includes dedicated resources for onboarding and technical assistance
  • ✅ 24/7 support – Live chat/email, consistently praised (4.5/5 G2, 4.6/5 Capterra)
  • Developer docs – Extensive at developers.livechat.com with Postman, tutorials
  • Enterprise SLA – Guaranteed response times, uptime commitments
  • SDK support – JavaScript, iOS (Swift), Android (Kotlin), Customer SDK
  • Documentation hub – Docs, tutorials, API references
  • Support channels – Email, in-app chat, dedicated managers (Premium+)
  • Open-source – Python SDK, Postman, GitHub examples
  • Community – User community + 5,000 Zapier integrations
Limitations & Considerations
  • Technical Expertise Required: Best for teams with ML expertise - more up-front setup and technical know-how than no-code tools
  • NO Drag-and-Drop Builder: Web console helps manage agents, but no drag-and-drop chatbot builder for non-technical users
  • UI Integration is Coding Project: APIs are full-featured, but non-tech users will need developer help for implementation
  • Learning Curve: Platform requires understanding of RAG concepts, embeddings, and AI agent architecture
  • NO Pre-Built UI: No out-of-the-box UI builder; customers embed in their own branded front end
  • API-First Platform: Built for API integration first - no plug-and-play web widget included
  • Enterprise Focus: Pricing and features target large enterprises vs. SMBs or individual developers
  • NOT Ideal For: Small teams without ML/AI expertise, organizations wanting no-code deployment, businesses needing immediate plug-and-play solutions
  • ⚠️ NOT RAG platform – Human-agent live chat, not autonomous retrieval (2/10 RAG)
  • ⚠️ Limited formats – PDFs/websites only, NO DOCX/CSV/Excel/audio/video/code
  • ⚠️ NO LLM flexibility – Proprietary engine only, no GPT-4/Claude/Gemini
  • ⚠️ Fragmented product – ChatBot requires separate $52/month purchase
  • ⚠️ Cost scaling – Per-agent pricing criticized, 10 agents = $642/month
  • Managed service – Less control over RAG pipeline vs build-your-own
  • Model selection – OpenAI + Anthropic only; no Cohere, AI21, open-source
  • Real-time data – Requires re-indexing; not ideal for live inventory/prices
  • Enterprise features – Custom SSO only on Enterprise plan
Core Agent Features
  • RAG 2.0 Agents: Specialized RAG agents for expert knowledge work with advanced contextual understanding and multi-hop retrieval capabilities
  • Multi-Hop Retrieval: Advanced RAG agents execute multi-hop retrieval and chain-of-thought reasoning for tough, complex questions
  • Task-Oriented Assistants: Domain-specific AI agents designed for mission-critical applications requiring high accuracy and minimal hallucinations
  • Multiple Datastore Support: Create multiple datastores and link them to agents by role or permission for fine-grained access control
  • Custom Logic Integration: Tune LLM on your own data, add guardrails, and embed custom logic as needed for specialized workflows
  • Agent APIs: Programmatic agent creation, management, and querying through comprehensive REST APIs and Python SDK
  • Grounded Generation: Inline citations showing exact document spans that informed each response part with built-in hallucination reduction
  • Document-Level Security: Enterprise controls for access permissions on sensitive data with query-time access validation
  • Platform Generally Available (January 2025): Helping enterprises build specialized RAG agents to support expert knowledge work
  • Benchmark Performance: Each component achieves leading benchmarks on BIRD (structured reasoning), RAG-QA Arena (end-to-end RAG), OmniDocBench (document understanding)
  • AI Reply Suggestions – Context-based response recommendations from knowledge sources
  • Text Enhancement – Grammar correction and tone polishing before sending
  • AI Insights – Analyzes 1,000+ queries in 30s for trends
  • ⚠️ NO anti-hallucination – No citation attribution or source tracing
  • Custom AI Agents – Autonomous GPT-4/Claude agents for business tasks
  • Multi-Agent Systems – Specialized agents for support, sales, knowledge
  • Memory & Context – Persistent conversation history across sessions
  • Tool Integration – Webhooks + 5,000 Zapier apps for automation
  • Continuous Learning – Auto re-indexing without manual retraining
R A G-as-a- Service Assessment
  • Platform Type: TRUE ENTERPRISE RAG 2.0 PLATFORM - Proprietary Grounded Language Model (GLM) optimized for factual accuracy and multimodal retrieval
  • RAG 2.0 Architecture: Advanced approach tops industry benchmarks for document understanding and factuality with multi-hop retrieval (announced general availability January 2025)
  • Proprietary GLM Model: ~88% factual accuracy on FACTS benchmark outperforming Gemini 2.0 Flash (84.6%), Claude 3.5 Sonnet (79.4%), GPT-4o (78.8%)
  • Built-in Evaluation Tools: Assess generated responses for equivalence and groundedness with comprehensive evaluation across every critical component
  • Multimodal Retrieval: Turns images and charts into embeddings for unified search across text and visual content in technical documentation
  • Groundedness Scoring: Built-in scoring with "Instant Viewer" highlighting exact source text backing each answer part for transparency
  • Reranker + Scoring: Uses reranker plus groundedness scoring for factual answers with precise attribution and hallucination reduction
  • Handles Noisy Datasets: Strong reranking and retrieval for large, noisy datasets with multiple datastores by role or permission
  • Production-Grade Accuracy: Delivers production-grade accuracy for specialized knowledge tasks with enterprise security, audit trails, high availability, scalability, compliance
  • Joint Tuning Capability: Retrieval and generation components can be jointly tuned by providing sample queries, gold-standard responses, supporting evidence
  • Comprehensive Assessment: Measures end-to-end RAG performance, multi-modal document understanding, structured data retrieval, and grounded language generation
  • Target Market: Large enterprises and ML teams requiring mission-critical AI applications with advanced reasoning and strict accuracy requirements
  • Use Case Fit: Ideal for mission-critical enterprise applications requiring multimodal retrieval, domain-specific AI agents, and role-based data access with query-time permission checks
N/A
  • Platform type – TRUE RAG-AS-A-SERVICE with managed infrastructure
  • API-first – REST API, Python SDK, OpenAI compatibility, MCP Server
  • No-code option – 2-minute wizard deployment for non-developers
  • Hybrid positioning – Serves both dev teams (APIs) and business users (no-code)
  • Enterprise ready – SOC 2 Type II, GDPR, WCAG 2.0, flat-rate pricing
200+ Integration Ecosystem ( Core Differentiator)
N/A
  • ✅ Mature marketplace – 200+ pre-built integrations after 20+ years development
  • ✅ Enterprise CRM depth – Deep Salesforce, HubSpot, Zendesk, Intercom integrations
  • ✅ E-commerce validated – Official Shopify/WooCommerce/BigCommerce plugins
  • ✅ Webhook flexibility – JSON payloads, 10s timeouts for custom workflows (8/10 differentiator)
N/A
Agent Chat A P I v3.5 ( Differentiator)
N/A
  • Dual transport – REST API and WebSocket (RTM) for real-time communication
  • Official SDKs – JavaScript/Node.js, iOS (Swift), Android (Kotlin), Customer SDK
  • OAuth 2.1 PKCE – Modern auth plus Personal Access Tokens
  • ⚠️ Rate limits – 180 requests/min may constrain high-volume apps
  • ⚠️ Chat ops focus – No RAG APIs for semantic search or embedding management
N/A
Chat Bot Automation ( Separate Product)
N/A
  • Visual builder – Drag-and-drop no-code chatbot with NLP
  • Proprietary engine – Internal NLP, doesn't use OpenAI/Bard/Bing
  • ⚠️ Additional cost – $52/month on top of LiveChat subscription
  • ⚠️ NO LLM selection – Proprietary only, no GPT-4/Claude/Gemini access
N/A
Widget Customization & White- Labeling
N/A
  • Live editor – Theme presets, custom hex colors, logo uploads, positioning
  • WCAG 2.1 AA – Accessibility with screen readers, keyboard navigation
  • Mobile responsive – Device-specific settings and hiding options
  • ⚠️ White-label Enterprise only – Requires custom pricing, minimum 5 seats
N/A
R A G Implementation & Accuracy
N/A
  • ⚠️ NOT RAG-as-a-Service – No vector database, embeddings, retrieval pipeline
  • ⚠️ NO chunking/embedding controls – Size, overlap, model selection not exposed
  • ⚠️ NO hybrid search – No keyword + semantic combination
  • ⚠️ NO anti-hallucination – No citations, source verification (2/10 RAG platform)
N/A
Full Compliance Portfolio ( Core Differentiator)
N/A
  • ✅ Seven certifications – SOC 2, GDPR, HIPAA, ISO 27001, PCI DSS, FedRAMP, CSA Star
  • ✅ FedRAMP unique – Rare federal authorization enables government contracts
  • ✅ PCI DSS masking – Built-in payment card protection for financial services
  • ✅ HIPAA BAA – Business Associate Agreement for healthcare compliance (9/10 differentiator)
N/A

Ready to experience the CustomGPT difference?

Start Free Trial →

Final Thoughts

Final Verdict: Contextual AI vs LiveChat

After analyzing features, pricing, performance, and user feedback, both Contextual AI and LiveChat are capable platforms that serve different market segments and use cases effectively.

When to Choose Contextual AI

  • You value invented by the original creator of rag technology
  • Best-in-class accuracy on RAG benchmarks
  • End-to-end optimized system vs cobbled together solutions

Best For: Invented by the original creator of RAG technology

When to Choose LiveChat

  • You value mature 20+ year platform with proven enterprise reliability (adobe, paypal, ikea, samsung, best buy)
  • 200+ integrations with robust Zapier support (5,000+ app connections) and comprehensive webhooks
  • Exceptional compliance portfolio: SOC 2 + GDPR + HIPAA + ISO 27001 + PCI DSS + FedRAMP + CSA Star Level 1

Best For: Mature 20+ year platform with proven enterprise reliability (Adobe, PayPal, IKEA, Samsung, Best Buy)

Migration & Switching Considerations

Switching between Contextual AI and LiveChat 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

Contextual AI starts at custom pricing, while LiveChat begins at $20/month. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.

Our Recommendation Process

  1. Start with a free trial - Both platforms offer trial periods to test with your actual data
  2. Define success metrics - Response accuracy, latency, user satisfaction, cost per query
  3. Test with real use cases - Don't rely on generic demos; use your production data
  4. Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
  5. Check vendor stability - Review roadmap transparency, update frequency, and support quality

For most organizations, the decision between Contextual AI and LiveChat 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: February 3, 2026 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.

See Also

Ready to Get Started with CustomGPT?

Join thousands of businesses that trust CustomGPT for their AI needs. Choose the path that works best for you.

Start Your Free Trial

Experience the full power of CustomGPT risk-free.

Get Started
RECOMMENDED

Request a Demo

See how CustomGPT can transform your business operations.

Get Started

Contact Sales

Get personalized guidance from our expert team.

Get Started

Why Choose CustomGPT?

97% Accuracy

Industry-leading benchmarks

5-Min Setup

Get started instantly

24/7 Support

Expert help when you need it

Enterprise Ready

Scale with confidence

Trusted by leading companies worldwide

Fortune 500Fortune 500Fortune 500Fortune 500Fortune 500Fortune 500

CustomGPT

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.

Get API KeyDocumentationRead our benchmarks
Get in touch
Contact Us

Join the Discussion

Loading comments...

Priyansh Khodiyar's avatar

Priyansh Khodiyar

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.

Watch: Understanding AI Tool Comparisons