Data Ingestion & Knowledge Sources
Document formats – PDF, DOCX, PPTX, CSV, TXT, HTML; 5MB free tier limitWebsite crawling – Hundreds of thousands of pages indexed under 5 minutesGoogle Drive – Native integration with real-time sync for cloud contentSQL databases – MySQL, PostgreSQL, Oracle, SQL Server, AWS/Azure/Google Cloud SQL⚠️ YouTube, Dropbox, Notion, OneDrive – Zapier middleware required (no native integration)
✅ Embeddings API – text-embedding models generate vectors for semantic search workflows⚠️ DIY Pipeline – No ready-made ingestion; build chunking, indexing, refreshing yourselfAzure File Search – Beta preview tool accepts uploads for semantic searchManual Architecture – Embed docs → vector DB → retrieve chunks at query time
1,400+ file formats – PDF, DOCX, Excel, PowerPoint, Markdown, HTML + auto-extraction from ZIP/RAR/7Z archivesWebsite crawling – Sitemap indexing with configurable depth for help docs, FAQs, and public contentMultimedia transcription – AI Vision, OCR, YouTube/Vimeo/podcast speech-to-text built-inCloud integrations – Google Drive, SharePoint, OneDrive, Dropbox, Notion with auto-syncKnowledge platforms – Zendesk, Freshdesk, HubSpot, Confluence, Shopify connectorsMassive scale – 60M words (Standard) / 300M words (Premium) per bot with no performance degradation
Hybrid Retrieval Architecture ( Core Differentiator)
✅ Three-component system – Elasticsearch + Milvus vectors + XGBoost ML reranking
75.33 NDCG@10 – MTEB vs 73.16 pure vector (3% improvement)96.50% Recall@20 – Anthropic benchmark vs 90.06% baselineModels – snowflake-arctic-embed-m, bge-en-icl, voyage-2, OpenAI text-embedding-3-large✅ Key finding – Open-source models match/exceed paid alternatives in benchmarks
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98.3% response accuracy – Claimed with 1.2-second average responseSource citation – Visual PDF highlighting with page-level references⚠️ No published uptime SLA – Service reliability not documented
✅ GPT-4 Top-Tier – Leading performance for language tasks; requires RAG for domain accuracy⚠️ Hallucination Risk – Can hallucinate on private/recent data without retrieval implementationWell-Built RAG Delivers – High accuracy achievable with proper indexing, chunking, prompt designLatency Considerations – Larger models (128k context) add latency; scales well under load
Sub-second responses – Optimized RAG with vector search and multi-layer cachingBenchmark-proven – 13% higher accuracy, 34% faster than OpenAI Assistants APIAnti-hallucination tech – Responses grounded only in your provided contentOpenGraph citations – Rich visual cards with titles, descriptions, images99.9% uptime – Auto-scaling infrastructure handles traffic spikes
Developer Experience ( A P I & S D Ks)
REST API + GraphQL – Bearer token auth with scored passage responsesdenser-retriever – MIT-licensed Python package (261 stars, 30 forks)Docker Compose – Full stack with Elasticsearch and Milvus containers⚠️ Self-hosted "not production suitable" – Requires additional persistence and secrets config
Rate limits – 200 API calls/month on free tier
✅ Excellent Docs – Official Python/Node.js SDKs; comprehensive API reference and guidesFunction Calling – Simplifies prompting; you build RAG pipeline (indexing, retrieval, assembly)Framework Support – Works with LangChain/LlamaIndex (third-party tools, not OpenAI products)⚠️ No Reference Architecture – Vast community examples but no official RAG blueprint
REST API – Full-featured for agents, projects, data ingestion, chat queriesPython SDK – Open-source customgpt-client with full API coveragePostman collections – Pre-built requests for rapid prototypingWebhooks – Real-time event notifications for conversations and leadsOpenAI compatible – Use existing OpenAI SDK code with minimal changes
Supported LLMs – GPT-4o, GPT-4o mini, GPT-3.5, Claude (version unspecified)API keys – Users provide OpenAI or Claude keys via environment⚠️ No custom fine-tuning – No private model hosting documented
✅ GPT-4 Family – GPT-4 (8k/32k), GPT-4 Turbo (128k), GPT-4o top-tier performance✅ GPT-3.5 Family – GPT-3.5 Turbo (4k/16k) cost-effective for high-volume use⚠️ OpenAI-Only – Cannot swap to Claude, Gemini; locked to OpenAI ecosystemManual Routing – Developer chooses model per request; no automatic selection✅ Frequent Upgrades – Regular releases with larger context windows and better benchmarks
GPT-5.1 models – Latest thinking models (Optimal & Smart variants)GPT-4 series – GPT-4, GPT-4 Turbo, GPT-4o availableClaude 4.5 – Anthropic's Opus available for EnterpriseAuto model routing – Balances cost/performance automaticallyZero API key management – All models managed behind the scenes
Website deployment – JavaScript widget (single line), iFrame, REST APIWordPress – Official plugin with page-specific targeting for no-code installZapier – 6,000+ apps with lead form triggers and events⚠️ No native Slack, Teams, Discord – WhatsApp via Zapier only
⚠️ CRM via Zapier only – HubSpot, Salesforce, Zendesk not native
⚠️ No First-Party Channels – Build Slack bots, widgets, integrations yourself or use third-party✅ API Flexibility – Run GPT anywhere; channel-agnostic engine for custom implementationsCommunity Tools – Zapier, community Slack bots exist but aren't official OpenAIManual Wiring – Everything is code-based; no out-of-the-box UI or connectors
Website embedding – Lightweight JS widget or iframe with customizable positioningCMS plugins – WordPress, WIX, Webflow, Framer, SquareSpace native support5,000+ app ecosystem – Zapier connects CRMs, marketing, e-commerce toolsMCP Server – Integrate with Claude Desktop, Cursor, ChatGPT, WindsurfOpenAI SDK compatible – Drop-in replacement for OpenAI API endpointsLiveChat + Slack – Native chat widgets with human handoff capabilities
Drag-and-drop builder – Theme colors, logos, button sizing, bubblesCustom domains – Available on paid tiers for white-labelingWelcome messages – Configure suggested questions and greetings
⚠️ No Turnkey UI – Build branded front-end yourself; no theming layer providedSystem Messages – Set tone/style via prompts; white-label chat requires developmentChatGPT Custom Instructions – Apply only inside ChatGPT app, not embedded widgetsDeveloper Project – All branding, UI customization is your responsibility
Full white-labeling included – Colors, logos, CSS, custom domains at no extra cost2-minute setup – No-code wizard with drag-and-drop interfacePersona customization – Control AI personality, tone, response style via pre-promptsVisual theme editor – Real-time preview of branding changesDomain allowlisting – Restrict embedding to approved sites only
No- Code Interface & Usability
Visual builder – Drag-and-drop theme customization without codingSetup – Single line JavaScript; WordPress plugin for no-code⚠️ Learning curve – Documentation fragmented across multiple sites
⚠️ ~4-person team – Impacts enterprise support capacity
⚠️ Not No-Code – Requires coding embeddings, retrieval, chat UI; no-code OpenAI options minimalChatGPT Web App – User-friendly but not embeddable with your data/branding by defaultThird-Party Tools – Zapier/Bubble offer partial integrations; not official OpenAI solutionsDeveloper-Focused – Extremely capable for coders; less for non-technical teams wanting self-serve
2-minute deployment – Fastest time-to-value in the industryWizard interface – Step-by-step with visual previewsDrag-and-drop – Upload files, paste URLs, connect cloud storageIn-browser testing – Test before deploying to productionZero learning curve – Productive on day one
Integrated lead capture – Configurable fields (name, email, company, role, phone)Conversation-triggered forms – Dynamic deployment based on conversation contextAnalytics dashboard – Lead quality, satisfaction scores, conversion trends✅ 24.8% conversion rate – Claimed on homepage demonstrating effectiveness
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Multi- Language & Localization
80+ languages – Automatic language detection for global deploymentsMultilingual rerankers – jinaai/jina-reranker-v2-base-multilingual support
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Conversation history – 30-360 days retention by tierHuman handoff – Triggers when complexity exceeds scopeEscalation workflows – Zendesk ticket creation for handoffs
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Observability & Monitoring
Conversation logs – Retention by tier (30-360 days)User engagement tracking – Common queries, conversation length, drop-off points⚠️ No A/B testing – No third-party BI integration (Tableau, PowerBI)
⚠️ No real-time alerting – No documented SLA tracking
⚠️ Basic Dashboard – Tracks monthly token spend, rate limits; no conversation analyticsDIY Logging – Log Q&A traffic yourself; no specialized RAG metricsStatus Page – Uptime monitoring, error codes, rate-limit headers availableCommunity Solutions – Datadog/Splunk setups shared; you build monitoring pipeline
Real-time dashboard – Query volumes, token usage, response timesCustomer Intelligence – User behavior patterns, popular queries, knowledge gapsConversation analytics – Full transcripts, resolution rates, common questionsExport capabilities – API export to BI tools and data warehouses
S Q L Database Chat ( Unique Feature)
✅ Direct SQL connectivity – Conversational BI across major databases
Supported databases – MySQL, PostgreSQL, Oracle, SQL Server, AWS/Azure/Google Cloud SQLNatural language to SQL – Ask questions, receive database query resultsAES-256 encryption – Secure connections with read-only access requirement
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Free – $0: 1 chatbot, 20 queries/month, 5MB limitStarter – $19-29/month: 2 chatbots, 1,500 queries/month, 30-day logsStandard – $89-119/month: 4 chatbots, 7,500 queries/month, custom domainBusiness – $399-799/month: 8 chatbots, 15,000 queries/month, priority supportEnterprise – Custom: Private cloud, dedicated support, AWS Marketplace⚠️ User feedback – "Plans quite restrictive, credit limits reached sooner"
✅ Pay-As-You-Go – $0.0015/1K tokens GPT-3.5; ~$0.03-0.06/1K GPT-4 token pricing⚠️ Scale Costs – Great low usage; bills spike at scale with rate limitsNo Flat Rate – Consumption-based only; cover external hosting (vector DB) separatelyEnterprise Contracts – Higher concurrency, compliance features, dedicated capacity via sales
Standard: $99/mo – 60M words, 10 botsPremium: $449/mo – 300M words, 100 botsAuto-scaling – Managed cloud scales with demandFlat rates – No per-query charges
⚠️ NO SOC 2, HIPAA, ISO 27001, GDPR certifications – Not for regulated industries
Private cloud deployments – Enterprise tier for data sovereigntyAES-256 encryption – Database connections with read-only accessAWS infrastructure – Data storage and processing on AWS
✅ API Data Privacy – Not used for training; 30-day retention for abuse checks✅ Encryption Standard – TLS in transit, at rest encryption; ChatGPT Enterprise adds SOC 2/SSO⚠️ Developer Responsibility – You secure user inputs, logs, auth, HIPAA/GDPR complianceNo User Portal – Build auth/access control in your own front-end
SOC 2 Type II + GDPR – Third-party audited complianceEncryption – 256-bit AES at rest, SSL/TLS in transitAccess controls – RBAC, 2FA, SSO, domain allowlistingData isolation – Never trains on your data
✅ denser-retriever – MIT-licensed, 261 GitHub stars, full RAG transparency
Docker Compose deployment – Local experimentation with Elasticsearch and MilvusValidate benchmarks – Test embeddings, rerankers, chunking on own data⚠️ Self-hosted "not production suitable" – Denser recommends managed SaaS
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Founded 2023 – Silicon Valley startup, ~4 employees (bootstrapped)✅ Founder Zhiheng Huang – Former Amazon Kendra scientist, Amazon Q lead
70+ research papers – 14,000+ citations; BLSTM-CRF 5,400+ citations
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R A G-as-a- Service Assessment
✅ TRUE RAG PLATFORM – Hybrid retrieval with open-source transparency
Data source flexibility – Good (documents, websites, Google Drive, SQL)LLM model options – Good (GPT-4o, Claude, multiple embeddings/rerankers)✅ Open-source transparency – Excellent (MIT-licensed core, published benchmarks)
⚠️ Compliance & certifications – Poor (no SOC 2, HIPAA, ISO 27001)
Best for – Technical teams prioritizing retrieval accuracy and validation
⚠️ NOT RAG-AS-A-SERVICE – Provides LLM models/APIs, not managed RAG infrastructureDIY RAG Architecture – Embed docs → external vector DB → retrieve → inject into promptFile Search (Beta) – Azure preview includes minimal semantic search; not production RAG⚠️ No Managed Infrastructure – Unlike CustomGPT/Vectara, leaves chunking, indexing, retrieval to developersFramework vs Service – Compare to LLM APIs (Claude, Gemini), not managed RAG platformsExternal Costs – RAG needs vector DBs (Pinecone $70+/month), hosting, embeddings API
Platform type – TRUE RAG-AS-A-SERVICE with managed infrastructureAPI-first – REST API, Python SDK, OpenAI compatibility, MCP ServerNo-code option – 2-minute wizard deployment for non-developersHybrid positioning – Serves both dev teams (APIs) and business users (no-code)Enterprise ready – SOC 2 Type II, GDPR, WCAG 2.0, flat-rate pricing
vs CustomGPT – Superior retrieval transparency, SQL chat; gaps in compliancevs Glean – Open-source vs proprietary, lower cost; lacks permissions-aware AI✅ Unique strengths – Hybrid retrieval benchmarks, founder pedigree, SQL chat
Target audience – Developers building AI chatbots without strict compliance
Market Position – Leading AI model provider; top GPT models as custom AI building blocksTarget Customers – Dev teams building bespoke solutions; enterprises needing flexibility beyond RAGKey Competitors – Anthropic Claude API, Google Gemini, Azure AI, AWS Bedrock, RAG platforms✅ Competitive Advantages – Top GPT-4 performance, frequent upgrades, excellent docs, massive ecosystem, Enterprise SOC 2/SSO✅ Pricing Advantage – Pay-as-you-go highly cost-effective at small scale; best value low-volume useUse Case Fit – Ideal for custom AI requiring flexibility; less suitable for turnkey RAG without dev resources
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 pricingvs OpenAI – 10% lower hallucination, 13% higher accuracy, 34% fastervs Botsonic/Chatbase – More file formats, source citations, no hidden costsvs LangChain – Production-ready in 2 min vs weeks of development
✅ Hybrid retrieval – ES + Milvus vectors + XGBoost reranking
75.33 NDCG@10 on MTEB – vs 73.16 pure vector (3% improvement)96.50% Recall@20 – Anthropic benchmark vs 90.06% baselineSource citation – Visual PDF highlighting with page references98.3% accuracy claimed – 1.2-second average response time
⚠️ NO Built-In RAG – LLM models only; build entire RAG pipeline yourself✅ Embeddings API – text-embedding-ada-002 and newer for vector embeddings/semantic searchDIY Architecture – Embed docs → external vector DB → retrieve → inject into promptAzure Assistants Preview – Beta File Search tool; minimal, preview-stage onlyFramework Integration – Works with LangChain/LlamaIndex (third-party, not OpenAI products)⚠️ Developer Responsibility – Chunking, indexing, retrieval optimization all require custom code
GPT-4 + RAG – Outperforms OpenAI in independent benchmarksAnti-hallucination – Responses grounded in your content onlyAutomatic citations – Clickable source links in every responseSub-second latency – Optimized vector search and cachingScale to 300M words – No performance degradation at scale
Customer support chatbots – Website deployment with 24.8% conversion rate✅ SQL database chat (unique) – Natural language queries against major databases
Technical documentation – Hundreds of thousands of pages indexed under 5 minutesMultilingual support – 80+ languages with automatic detectionDeveloper-focused RAG – MIT-licensed denser-retriever for validation
✅ Custom AI Applications – Bespoke solutions requiring maximum flexibility beyond pre-packaged platforms✅ Code Generation – GitHub Copilot-style tools, IDE integrations, automated review✅ Creative Writing – Content generation, marketing copy, storytelling at scale✅ Data Analysis – Natural language queries over structured data, report generationCustomer Service – Custom chatbots integrated with business systems and knowledge bases⚠️ NOT IDEAL FOR – Non-technical teams wanting turnkey RAG chatbot without coding
Customer support – 24/7 AI handling common queries with citationsInternal knowledge – HR policies, onboarding, technical docsSales enablement – Product info, lead qualification, educationDocumentation – Help centers, FAQs with auto-crawlingE-commerce – Product recommendations, order assistance
Documentation – docs.denser.ai, retriever.denser.ai, GitHub READMEs⚠️ Documentation fragmented – Information scattered across multiple sites
~4-person team – Impacts enterprise support capacityOpen-source community – 261 GitHub stars, 30 forks, MIT license
✅ Excellent Documentation – Comprehensive guides, API reference, code samples at platform.openai.com✅ Official SDKs – Well-maintained Python, Node.js libraries with examples✅ Massive Community – Extensive tutorials, LangChain/LlamaIndex integrations, ecosystem resources⚠️ Limited Direct Support – Community forums for standard users; Enterprise gets premium supportOpenAI Cookbook – Practical examples and recipes for common use cases including RAG
Documentation hub – Docs, tutorials, API referencesSupport channels – Email, in-app chat, dedicated managers (Premium+)Open-source – Python SDK, Postman, GitHub examplesCommunity – User community + 5,000 Zapier integrations
Limitations & Considerations
⚠️ No compliance certifications – Missing SOC 2, HIPAA, ISO 27001, GDPR
⚠️ Small team (~4 people) – Potential scaling constraints for enterprise
⚠️ Heavy Zapier dependency – No native Slack, Teams, CRM integrations
⚠️ Fragmented documentation – Scattered across docs, retriever docs, GitHub
⚠️ User feedback – "Plans restrictive, credit limits reached sooner"
⚠️ NO Built-In RAG – Entire retrieval infrastructure must be built by developers⚠️ Developer-Only – Requires coding expertise; no no-code interface for non-technical teams⚠️ Rate Limits – Usage tiers start restrictive (Tier 1: 500 RPM GPT-4)⚠️ Model Lock-In – Cannot use Claude, Gemini; tied to OpenAI ecosystem⚠️ NO Chat UI – ChatGPT web interface not embeddable or customizable for business⚠️ Cost at Scale – Token pricing can spike without optimization; needs cost management
Managed service – Less control over RAG pipeline vs build-your-ownModel selection – OpenAI + Anthropic only; no Cohere, AI21, open-sourceReal-time data – Requires re-indexing; not ideal for live inventory/pricesEnterprise features – Custom SSO only on Enterprise plan
AI agent capabilities – Process data for intelligent automation with customizationMulti-platform deployment – Launch across websites and messaging with single lineAdaptive learning – Chatbot learns over time using conversation analysis24/7 availability – Smart AI support with instant answers
✅ Assistants API (v2) – Built-in conversation history, persistent threads, tool access management✅ Function Calling – Models invoke external functions/tools; describe structure, receive calls with arguments✅ Parallel Tool Execution – Access Code Interpreter, File Search, custom functions simultaneouslyResponses API (2024) – New primitive with web search, file search, computer use✅ Structured Outputs – strict: true guarantees arguments match JSON Schema for reliable parsing⚠️ Agent Limitations – Less control vs LangChain for complex workflows; simpler assistant paradigm
Custom AI Agents – Autonomous GPT-4/Claude agents for business tasksMulti-Agent Systems – Specialized agents for support, sales, knowledgeMemory & Context – Persistent conversation history across sessionsTool Integration – Webhooks + 5,000 Zapier apps for automationContinuous Learning – Auto re-indexing without manual retraining
Conversational interface – Chat with customers in friendly mannerBusiness knowledge integration – Trained on documents, websites, Google DriveMulti-language support – 80+ languages with automatic detectionLead capture – Integrated forms (name, email, company, role)Human handoff – Triggers on complexity with Zendesk tickets
✅ Multi-Turn Chat – GPT-4/3.5 handle conversations; you resend history for context⚠️ No Agent Memory – OpenAI doesn't store conversational state; you manage itFunction Calling – Model triggers your functions (search endpoints); you wire retrievalChatGPT Web UI – Separate from API; not brand-customizable for private data
✅ #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 & Flexibility ( Behavior & Knowledge)
Behavior customization – Define name, tone, response preferencesFile support – PDF, DOCX, XLSX, PPTX, TXT, HTML, CSV, XMLWebsite crawling – Train bot by crawling URLs for knowledge baseEasy knowledge updates – Add documents, re-crawl, update without rebuildFlexible deployment – Web widget, dashboard, or API integration
✅ Fine-Tuning Available – GPT-3.5 fine-tuning for style; knowledge injection via RAG code⚠️ Content Freshness – Re-embed, re-fine-tune, or pass context each call; developer overheadTool Calling Power – Powerful moderation/tools but requires thoughtful design; no unified UIMaximum Flexibility – Extremely flexible for general AI; lacks built-in document management
Live content updates – Add/remove content with automatic re-indexingSystem prompts – Shape agent behavior and voice through instructionsMulti-agent support – Different bots for different teamsSmart defaults – No ML expertise required for custom behavior
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✅ Massive Community – Thorough docs, code samples; direct support requires EnterpriseThird-Party Frameworks – Slack bots, LangChain, LlamaIndex building blocks aboundBroad AI Focus – Text, speech, images; RAG is one of many use casesEnterprise Premium Support – Success managers, SLAs, compliance environment for Enterprise customers
Comprehensive docs – Tutorials, cookbooks, API referencesEmail + in-app support – Under 24hr response timePremium support – Dedicated account managers for Premium/EnterpriseOpen-source SDK – Python SDK, Postman, GitHub examples5,000+ Zapier apps – CRMs, e-commerce, marketing integrations
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✅ Maximum Freedom – Best for bespoke AI solutions beyond RAG (code gen, creative writing)✅ Regular Upgrades – Frequent model releases with bigger context windows keep tech current⚠️ Coding Required – Near-infinite customization comes with setup complexity; developer-friendly onlyCost Management – Token pricing cost-effective at small scale; maintaining RAG adds ongoing effort
Time-to-value – 2-minute deployment vs weeks with DIYAlways current – Auto-updates to latest GPT modelsProven scale – 6,000+ organizations, millions of queriesMulti-LLM – OpenAI + Claude reduces vendor lock-in
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✅ GPT-4 Family – GPT-4 (8k/32k), GPT-4 Turbo (128k), GPT-4o - top language understanding/generation✅ GPT-3.5 Family – GPT-3.5 Turbo (4k/16k) cost-effective with good performance✅ Frequent Upgrades – Regular releases with improved capabilities, larger context windows⚠️ OpenAI-Only – Cannot swap to Claude, Gemini; locked to OpenAI models✅ Fine-Tuning – GPT-3.5 fine-tuning for domain-specific customization with training data
OpenAI – GPT-5.1 (Optimal/Smart), GPT-4 seriesAnthropic – Claude 4.5 Opus/Sonnet (Enterprise)Auto-routing – Intelligent model selection for cost/performanceManaged – No API keys or fine-tuning required
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✅ API Data Privacy – Not used for training; 30-day retention for abuse checks only✅ ChatGPT Enterprise – SOC 2 Type II, SSO, stronger privacy, enterprise-grade security✅ Encryption – TLS in transit, at rest encryption with enterprise standards✅ GDPR/HIPAA – DPA for GDPR; BAA for HIPAA; regional data residency available✅ Zero-Retention Option – Enterprise/API customers can opt for no data retention⚠️ Developer Responsibility – User auth, input validation, logging entirely on you
SOC 2 Type II + GDPR – Regular third-party audits, full EU compliance256-bit AES encryption – Data at rest; SSL/TLS in transitSSO + 2FA + RBAC – Enterprise access controls with role-based permissionsData isolation – Never trains on customer dataDomain allowlisting – Restrict chatbot to approved domains
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✅ Pay-As-You-Go – $0.0015/1K tokens GPT-3.5; ~$0.03-0.06/1K GPT-4 token pricing✅ No Platform Fees – Pure consumption pricing; no subscriptions, monthly minimumsRate Limits by Tier – Usage tiers auto-increase limits as spending grows⚠️ Cost at Scale – Bills spike without optimization; high-volume needs token managementExternal Costs – RAG incurs vector DB (Pinecone, Weaviate) and hosting costs✅ Best Value For – Low-volume use or teams with existing infrastructure
Standard: $99/mo – 10 chatbots, 60M words, 5K items/botPremium: $449/mo – 100 chatbots, 300M words, 20K items/botEnterprise: Custom – SSO, dedicated support, custom SLAs7-day free trial – Full Standard access, no chargesFlat-rate pricing – No per-query charges, no hidden costs
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