In this comprehensive guide, we compare BotsCrew and Glean 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 BotsCrew and Glean, 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 BotsCrew if: you value fortune 500-proven expertise: samsung next, honda, mars, adidas, virgin, bmc software clients
Choose Glean if: you value permissions-aware ai is genuinely differentiated - real-time enforcement across 100+ datasources addresses critical enterprise concern
About BotsCrew
BotsCrew is enterprise chatbot development services with custom ai solutions. Enterprise chatbot development services company with custom AI solutions, not self-service RAG platform. Founded 2016, acquired by CourtAvenue (Feb 2025). Serves Fortune 500 with white-glove development starting at $600/month + $3,000+ setup costs. Founded in 2016, headquartered in London, UK / Lviv, Ukraine, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
88/100
Starting Price
$600/mo
About Glean
Glean is enterprise work ai with permissions-aware rag across 100+ apps. Glean is a premium enterprise RAG platform with permissions-aware AI as its core differentiator. Founded by ex-Google Search engineers, Glean achieved $100M ARR in three years and a $7.2B valuation (2025). It connects 100+ enterprise apps with real-time access controls, supports 15+ LLMs, and offers comprehensive APIs with 4-language SDKs. Trade-offs: enterprise-only sales (~$50/user/month, ~$60K minimum), no consumer messaging channels, and premium positioning over plug-and-play simplicity. Founded in 2019, headquartered in Palo Alto, CA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
96/100
Starting Price
$50/mo
Key Differences at a Glance
In terms of user ratings, Glean in overall satisfaction. From a cost perspective, Glean offers more competitive entry pricing. The platforms also differ in their primary focus: Chatbot Platform versus Enterprise RAG. 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
BotsCrew
Glean
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Supported Formats: 100+ document file types for knowledge base building (PDFs, websites, help center content, plain text)
Scale Proven: Kravet deployment processed 125,000 product pages + 1,000+ static files across various formats
NoForm.ai: Website content learning from single URL 'almost immediately' - chatbot learns 'almost everything about our company' from website link
Knowledge Updates: Manual uploads required - no automatic cloud syncing or retraining from connected sources
Missing Cloud Integrations: No Google Drive, Dropbox, or Notion automatic syncing - significant gap vs modern RAG platforms
Content Management: Updates flow through platform's content management system with manual intervention required
API Limitation: No programmatic document upload or knowledge base management via API
Enterprise Proven: FIBA Basketball World Cup chatbot handled 72,000 conversations during tournament
Critical Gap: Knowledge ingestion requires UI-based uploads or professional services engagement vs self-service API access
100+ native connectors covering major enterprise categories
Cloud Storage: Google Drive, SharePoint, OneDrive, Dropbox, Box
Communication: Slack, Microsoft Teams, Gmail, Outlook, Zoom
Indexing API: 10 requests/second for bulk operations, ProcessAll limited to once per 3 hours
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
Messaging Platforms: Facebook Messenger (primary channel), WhatsApp Business API, Instagram, Telegram (G2 verified), SMS via Plivo integration
Enterprise Channels: Slack deployments, website widget via copy-paste code snippet added before </body> tag
Microsoft Teams: Blog content exists but native support unconfirmed - unclear if production-ready
CRM Integrations: Salesforce, HubSpot, Zendesk Suite for lead capture and case management
Enterprise Systems: Google Workspace, Slack, Shopify, PayPal, SAP (e-commerce implementations)
Zapier: NOT natively confirmed - integration approach emphasizes custom development services vs pre-built marketplace connectors
Webhooks: Availability implied but not explicitly documented for self-service use
Unified Inbox: Manages all channel conversations from single interface with full context preservation
Integration Model: 'Connect your bot with any software you use' through development services rather than self-service APIs
Slack: Official Marketplace app, Gleanbot auto-responses, Real-Time Search API
Microsoft Teams: Native Teams app and agent integration
Zoom: Custom AI Companion integration
No WhatsApp: No native integration
No Telegram: No native integration
No Zapier: No native integration (different product "Glean.ly" exists)
Multi-Lingual: 100+ languages supported with verified deployment operating simultaneously in English, French, German, Dutch, Polish, Turkish, Arabic (WhatsApp implementation)
Conversation History: Single inbox preserves full context across all channels and conversation turns
Dialog & User Journey Management: Not just messages with buttons - manage complex conversations using decision trees to ensure smooth and engaging dialogue with intent recognition capabilities
Analytics: Advanced performance tracking including goal completion rates, fallback rates, user satisfaction scores, revenue attribution
Human Handoff: Seamless live chat transfer with full conversation transcript passed to agents - documented Freshchat integration
Context Management: Context-aware multi-turn dialogue management across conversation sessions with personalized responses based on previous interactions and customer data
Conversation Quality: Target accuracy rate 80%+ with real-time monitoring and quality tracking
Business Outcomes: Leads generated, revenue attributed, conversion rate tracking integrated into analytics
Glean Chat interface: Primary interface for interacting with Glean Assistant offering familiar chat-like experience enabling natural conversations with company knowledge base
Multi-turn conversations: Supports conversational AI with natural language and context awareness maintaining context across conversation turns
Streaming responses: Real-time response streaming for better user experience with automatic source citations for transparency
Chatbot context understanding: Understands thread and sequence of conversations tracking references like "their" and "they" across multiple exchanges
Enterprise knowledge integration: Works across all company apps and knowledge sources including Microsoft 365, Google Workspace, Salesforce, Jira, GitHub and nearly 100 more applications
Personalization and security: Delivers answers highly customized to each user based on deep understanding of company content, employees, and activity while adhering to real-time enterprise data permissions and governance rules
Citation and transparency: Provides full linking to source information across documents, conversations and applications for transparency and trust
Simple chatbot API: Powerful tool for integrating conversational AI into products creating custom conversational interfaces leveraging Glean's AI capabilities
Use case flexibility: Build chatbots answering customer questions using help documentation, FAQs, knowledge bases or create internal tools helping employees find company policies, procedures, documentation
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.
Customization & Branding
Comprehensive White-Label Program: Complete BotsCrew brand removal with zero mentions on white-labeled platforms
Custom Domains: Full domain rebranding capability for complete brand ownership
Custom Dashboards: Dedicated client management interfaces under reseller branding
Zero-Commission Reselling: Partners set their own pricing without BotsCrew revenue share - unique in market
Marketing Support: Access to demos, prototypes, case studies, and sales materials for partners
Vector Database: Pinecone for vector database implementations in enterprise RAG deployments
Hybrid Optimization: 'Build chatbot with DialogFlow and add GPT only to certain parts of conversation flow' - selective LLM usage
Critical Limitation: Model selection NOT self-service - determined during discovery phase with BotsCrew development team
No Automatic Routing: No dynamic model switching or automatic model selection capabilities
Services-Driven: LLM choices made by professional services team vs user dashboard toggles
Model Hub supports 15+ LLMs across multiple hosting providers
OpenAI: GPT-3.5, GPT-4
Azure OpenAI: GPT models
Google Vertex AI: Gemini 1.5 Pro
Amazon Bedrock: Claude 3 Sonnet
Per-step model selection: Different LLMs for each workflow step
Temperature controls: Factual, balanced, or creative output settings
Model tiers: Basic, Standard, Premium (premium consumes FlexCredits on Enterprise Flex)
Two access options: Glean Universal Key (managed) or Customer Key (BYOK)
Zero data retention: Customer data never used for model training
Automatic model updates: Deprecated models replaced with latest versions
Automatic routing: Optimizes using best-in-class models per query type
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)
Critical Distinction: BotsCrew does NOT provide a public RAG API - fundamentally NOT a developer-first platform
Misleading Claim: 'RAG API: Yes - extensive integration with any open API' means platform can consume external APIs, NOT expose RAG capabilities through APIs
Available API (common.botscrew.net): Limited utility API for chatbot flow operations only - datetime formatting, math calculations, string operations, email sending, user redirect
NOT a RAG API: Cannot create agents, upload knowledge, query knowledge base, or access embeddings/vector store via API
Java SDK Only: Spring Boot framework (bot-framework-core, bot-framework-nlp, bot-framework-messenger) - last updated February 2020 (4+ years outdated)
No Python SDK: Major limitation for data science teams and backend developers
No JavaScript SDK: Blocks modern web development workflows
Documentation Quality: Basic with no developer portal, cookbook examples, or RAG-specific guides comparable to developer-first platforms
GitHub Activity: Open-source Java framework exists but last commit February 2020 - effectively abandoned
Use Case Mismatch: Cannot use BotsCrew as RAG backend for self-service development - requires professional services engagement
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
Primary Advantage: Fortune 500-proven enterprise chatbot development services with comprehensive white-label program and full-cycle expertise
White-Label Leadership: Zero-commission reselling, complete brand removal, custom domains/dashboards - one of market's best partner programs
Enterprise Credentials: HIPAA with BAA, GDPR, SOC 2, ISO 27001 compliance enables regulated industry adoption
Professional Services Depth: 8+ years experience, conversational design team, 14-day pilot program, post-delivery support beyond scope
CourtAvenue Backing: February 2025 acquisition provides US market access and enterprise resources
Primary Challenge: NOT a RAG-as-a-Service platform - cannot compare directly to CustomGPT.ai or developer-first RAG APIs
Developer Friction: No RAG API, no knowledge upload API, no Python/JS SDKs, outdated Java framework (2020)
Pricing Barrier: $600/month + $3,000+ setup + $50-99/hour services + $10,000 minimum vs competitors with sub-$100 self-service tiers
Time-to-Value: 2+ weeks implementation vs minutes for self-service platforms - 'not a platform where you can build chatbot in couple of hours'
Market Position: Competes with enterprise chatbot development agencies (IBM Watson consultants, Accenture) vs RAG API platforms (CustomGPT.ai, Pinecone Assistant)
Use Case Fit: Exceptional for enterprises seeking fully managed custom chatbot development; poor fit for developers seeking self-service RAG APIs
Comparison Warning: Direct feature comparison with RAG-as-a-Service platforms is fundamentally misleading due to different business models and architectures
vs CustomGPT: Enterprise-premium vs developer-friendly; permissions-aware AI vs flexible customization
vs Zendesk: Enterprise search + RAG vs customer service platform
Unique strength: Real-time permissions-aware AI across 100+ datasources (no competitor matches this)
Target audience: Large enterprises (1K-100K users) with complex permission hierarchies
Pricing barrier: ~$50/user/month with ~$60K minimum excludes SMBs
Enterprise focus: Security, governance, compliance over plug-and-play simplicity
Market position: Leading all-in-one RAG platform balancing enterprise-grade accuracy with developer-friendly APIs and no-code usability for rapid deployment
Target customers: Mid-market to enterprise organizations needing production-ready AI assistants, development teams wanting robust APIs without building RAG infrastructure, and businesses requiring 1,400+ file format support with auto-transcription (YouTube, podcasts)
Key competitors: OpenAI Assistants API, Botsonic, Chatbase.co, Azure AI, and custom RAG implementations using LangChain
Competitive advantages: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, SOC 2 Type II + GDPR compliance, full white-labeling included, OpenAI API endpoint compatibility, hosted MCP Server support (Claude, Cursor, ChatGPT), generous data limits (60M words Standard, 300M Premium), and flat monthly pricing without per-query charges
Pricing advantage: Transparent flat-rate pricing at $99/month (Standard) and $449/month (Premium) with generous included limits; no hidden costs for API access, branding removal, or basic features; best value for teams needing both no-code dashboard and developer APIs in one platform
Use case fit: Ideal for businesses needing both rapid no-code deployment and robust API capabilities, organizations handling diverse content types (1,400+ formats, multimedia transcription), teams requiring white-label chatbots with source citations for customer-facing or internal knowledge projects, and companies wanting all-in-one RAG without managing ML infrastructure
A I Models
OpenAI Models: GPT-4, GPT-4o, GPT-4.5 documented and supported for production deployments
Anthropic Claude: Claude 3 Opus integration available for enterprise applications
Open Source LLMs: Llama 3 support for cost optimization and on-premise deployment flexibility
Hybrid NLU: DialogFlow integration via SDK for combined traditional NLU + LLM approaches
Legacy Compatibility: LUIS, Rasa.ai support for existing enterprise infrastructure
Vector Database: Pinecone integration for enterprise-scale RAG deployments and vector search
Selective LLM Usage: "Build chatbot with DialogFlow and add GPT only to certain parts of conversation flow" - cost/performance optimization strategy
Professional Services Model: Model selection NOT self-service - determined during discovery phase with BotsCrew development team
No Automatic Routing: No dynamic model switching or automatic model selection capabilities available
Model Hub supports 15+ LLMs across multiple hosting providers with per-step model selection
OpenAI: GPT-3.5, GPT-4 via OpenAI or Azure OpenAI endpoints
Google Vertex AI: Gemini 1.5 Pro with multimodal capabilities
Amazon Bedrock: Claude 3 Sonnet for high-accuracy enterprise use cases
Temperature controls: Factual, balanced, or creative output settings per workflow
Model tiers: Basic, Standard, Premium (premium consumes FlexCredits on Enterprise Flex plan)
Two access options: Glean Universal Key (managed) or Customer Key (BYOK) for data sovereignty
Zero data retention: Customer data never used for model training with automatic model updates
Automatic routing: Optimizes using best-in-class models per query type for accuracy and cost
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
Documented Accuracy Improvement: Kravet Inc. case study shows AI answer accuracy improved from under 60% to approximately 90% through professional optimization
White-Label Reselling: Complete brand removal with zero-commission model for agencies building chatbot services
Regulated Industries: HIPAA, SOC 2, ISO 27001 compliance enables healthcare, finance, government sector adoption
Enterprise knowledge retrieval: Unified search across 100+ datasources (Google Drive, SharePoint, Confluence, Salesforce, Zendesk, GitHub, Slack) for 10K-100K user organizations
Permissions-aware search: Complex permission hierarchies requiring real-time enforcement - healthcare, finance, legal industries with sensitive data access controls
AI agents and automation: 30+ prebuilt agents for sales, engineering, IT, HR use cases with workflow automation capabilities
Developer-friendly RAG: Official SDKs (Python, Java, Go, TypeScript), LangChain integration, MCP Server for Claude Desktop/Cursor/VS Code
Active Data Governance: Continuous scanning with 100+ predefined infotypes (PII, PCI, M&A) and customizable policies with auto-hide
Cloud-Prem deployment: Customer-hosted in AWS or GCP for regulated industries requiring full data residency control
NOT suitable for: SMBs with <100 users or <$60K budgets, simple document Q&A without permission requirements, consumer messaging channels (WhatsApp, Telegram)
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)
FlexCredits (Enterprise Flex): For premium LLM usage with consumption-based billing
Support tiers: Standard (24x5, included) or Premium (24x7 critical, additional fee)
Dedicated CSMs: Assigned to enterprise accounts with regular business reviews and hands-on onboarding
Pricing barrier: Excludes SMBs and startups - targets Fortune 500 and mid-market enterprises with 1K-100K users
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
High-Touch Support: Phone and email support with dedicated project management attention
Dedicated Project Management: Weekly meetings, backlog system, continuous engagement throughout project lifecycle and beyond
Post-Delivery Support: Assistance continuing beyond project scope and original engagement (BMC Software testimonial: "helpful and responsive, continuing to assist us post-delivery")
Training Resources: Documentation, webinars, and in-person training available for enterprise clients
Blog Content: Extensive technical content at botscrew.com/blog covering RAG, LLM evaluation, enterprise deployment best practices
AI Newsletter: Bi-weekly newsletter with 1,000+ readers from Google, Meta, Amazon for industry insights
No Community Forum: Limited peer-to-peer support resources - relies on professional services model for all support
Open-Source Framework: Java bot framework on GitHub (bot-framework-core, bot-framework-nlp, bot-framework-messenger) last updated February 2020
Awards Recognition: Top AI Chatbot Development Company 2024 (Clutch), Clutch Champion 2023, #1 AI Developer worldwide 2017
Service Level Agreement: SLA available as part of comprehensive enterprise chatbot services package
Standard support: 24x5 (Mon-Fri) via portal, email, Slack Connect channels
Premium support: 24x7 for critical issues with additional fee
Dedicated CSMs: Enterprise accounts with hands-on onboarding and regular business reviews
Excellent documentation: developers.glean.com with OpenAPI specs, CodeSandbox demos, comprehensive API references
Official SDKs: Python (pip install glean), Java (Maven), Go, TypeScript with async support and framework integrations
Web SDK: @gleanwork/web-sdk for embeddable components (chat, search, autocomplete, recommendations)
GitHub repositories: github.com/gleanwork with SDK repositories and sample projects
Active community: User community plus 5,000+ app integrations through Zapier ecosystem
Regular updates: Platform stays current with ongoing GPT and retrieval improvements automatically
Additional Considerations
Proven Flexibility: Platform is very flexible with the ability to add custom integrations and features if needed through professional services engagement
Multilingual Strength: Native integrations for FB Messenger and website widgets with on-demand connections to WhatsApp, Twitter, Telegram - bot lives on multiple platforms without duplication
Learning Curve: At first look everything can seem very complicated for new users, requiring time investment beyond quick setup expectations
Time Investment Required: Not a platform where you can build a chatbot in couple of hours and immediately test - users should be prepared to spend more time though the result pays off
Helpful Support Team: BotsCrew team very helpful, providing guidance and assistance throughout the whole process with post-delivery support beyond scope
Intuitive Once Learned: After initial complexity, platform becomes very intuitive and easy to use for quickly setting up and connecting chatbots on websites
Cost Consideration: Product is on the more expensive side with $600/month platform + $3,000+ setup + $50-99/hour services positioning it as enterprise solution
Premium Positioning: Really more of an enterprise solution with Fortune 500 clients (Samsung NEXT, Honda, Mars, Adidas, Virgin) vs SMB-focused platforms
Limited AI Intuitiveness: Chatbot not as intuitively driven by artificial intelligence with conversations predefined based on pre-written scripts requiring manual setup
No Mobile App: No mobile application available which would be great addition for on-the-go management
Best Fit: Enterprises with $10,000+ budgets seeking fully managed custom chatbot development with white-label reselling opportunities
Cannot create content directly: Glean focuses purely on search and retrieval - not suitable for organizations needing content creation within platform
Platform designed for large organizations: Feature set and pricing optimized for large enterprises - smaller teams may find it overkill and less cost-effective
AI production challenges: 68% of organizations report moving only 30% or fewer AI experiments into full production highlighting persistent scaling difficulties beyond proof-of-concept
Integration complexity: Requires strategic overhaul of processes to ensure seamless technology incorporation into existing workflows
Change management: Overcoming resistance to change demands strong leadership and commitment to fostering innovation and adaptability environment
Data reliability monitoring: Potential for inaccuracies in AI outputs necessitates rigorous monitoring frameworks to ensure data reliability and trustworthiness
Cybersecurity concerns: As AI deployment expands, cybersecurity threats become more pronounced requiring enhanced protective measures for sensitive information
Bias in AI models: Models can inadvertently learn and replicate biases in training data leading to unfair or discriminatory outcomes particularly in hiring, customer service, legal decisions
Training investment required: Enterprises must invest in training workforce to effectively use AI tools - upskilling employees, hiring AI talent, or partnering with consultants
Security risks and shadow IT: Many organizations hesitate due to uncertainties from security risks and shadow IT - ad hoc generative AI adoption comes with heavy risks and costs
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
NOT a Self-Service Platform: Custom development services company vs self-service SaaS - fundamentally different product category
No RAG API: Cannot create agents, upload knowledge, query knowledge base, or access embeddings via API programmatically
Misleading API Claims: "RAG API: Yes" means platform consumes external APIs, NOT expose RAG capabilities through developer APIs
Outdated SDK: Java SDK only (Spring Boot framework) last updated February 2020 (4+ years outdated), effectively abandoned on GitHub
No Python/JavaScript SDKs: Major limitation blocks data science teams and modern web development workflows
Manual Knowledge Updates: No automatic cloud syncing or retraining - requires UI-based uploads or professional services engagement
Missing Cloud Integrations: No Google Drive, Dropbox, Notion automatic syncing - significant gap vs modern RAG platforms
No API for Content Management: No programmatic document upload or knowledge base management capabilities
Requires Professional Services: Advanced features and enterprise deployments need development team engagement vs self-service configuration
Long Implementation Time: 2+ weeks minimum for highly customized solutions - "not a platform where you can build chatbot in couple of hours"
High Cost Barrier: $600/mo + $3,000 setup + $50-99/hr + $10,000 minimum vs $99/mo self-service competitors
Use Case Mismatch: Cannot use BotsCrew as RAG backend for self-service development - requires professional services for all implementations
Limited Documentation Quality: Basic with no developer portal, cookbook examples, or RAG-specific guides comparable to developer-first platforms
Comparison Warning: Architectural comparison to CustomGPT.ai fundamentally misleading - different business models, target customers, delivery methods
High cost barrier: ~$50/user/month with ~$60K minimum ACV excludes SMBs, startups, and budget-conscious teams
No public pricing: Requires sales contact creating friction for evaluation and budget planning vs transparent competitors
Paid POCs: No free trial, POCs reportedly cost up to $70K for large enterprise pilots
NO FedRAMP certification: Not suitable for US federal government deployments
Limited consumer channels: No native WhatsApp, Telegram integrations - designed for internal enterprise use only
Complex implementation: Initial indexing takes "few days" depending on data volume, requires enterprise IT coordination
Cross-language queries in early access: English query finding Spanish documents still in testing phase
Best for: Large enterprises (1K-100K users) with complex permission hierarchies, $60K+ budgets, and need for permissions-aware AI across 100+ datasources
NOT suitable for: SMBs, startups, simple document Q&A without permission requirements, organizations prioritizing transparent pricing
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 R A G Features
N/A
Hybrid search: Combines semantic (vector-based) and lexical (keyword) approaches
Knowledge Graph Framework: Proprietary anchors and signals across enterprise data
Rich, Scalable Crawler: Permission rule synchronization at scale
LLM Control Layer: Optimizes and controls LLM outputs
Cloud (SaaS): Standard deployment on Glean infrastructure
Cloud-Prem: Customer-hosted in AWS or GCP environment for full data residency control
Single-tenant architecture: Isolated infrastructure per customer
Browser extensions: Chrome, Firefox, Safari, Edge
Web SDK embedding: Custom integration in customer applications
N/A
Core Agent Features
N/A
Autonomous AI agents: Agents use AI to understand tasks and take action on behalf of users from answering questions and retrieving information to executing work autonomously
Natural language agent builder: Build agents by describing desired output in simple natural language - Glean understands goal and designs complex multi-step workflows
Agentic reasoning engine: LLM-agnostic engine enables agents to go beyond retrieval and generation - powers sophisticated automation and decision-making by understanding outcomes, building multi-step plans, and using action library
100+ native actions: Supports 100+ new native actions across Slack, Microsoft Teams, Salesforce, Jira, GitHub, Google Workspace and other applications
MCP host support: Gives agents dramatically larger surface area to operate across enterprise applications
Human-in-the-loop design: Agents can autonomously do work end-to-end with human review checkpoints - process customer support tickets, conduct research, prepare responses for employee review before execution
Vibe coding: Upgraded builder makes agent creation as simple as chatting - anyone (not just developers) can create and refine agents without understanding or interacting with code
Grounded in enterprise data: Autonomous agents grounded in most relevant authoritative information for confident work automation
Automatic agent triggering: Orchestrates agents automatically based on schedules or events and surfaces agent recommendations across enterprise
Visual and conversational workflow design: Turn ideas into structured workflows using simple natural language prompts or visual builder
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
After analyzing features, pricing, performance, and user feedback, both BotsCrew and Glean are capable platforms that serve different market segments and use cases effectively.
When to Choose BotsCrew
You value fortune 500-proven expertise: samsung next, honda, mars, adidas, virgin, bmc software clients
Model flexibility without vendor lock-in - 15+ LLMs with per-step selection and bring-your-own-key option
Best For: Permissions-aware AI is genuinely differentiated - real-time enforcement across 100+ datasources addresses critical enterprise concern
Migration & Switching Considerations
Switching between BotsCrew and Glean 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
BotsCrew starts at $600/month, while Glean begins at $50/month. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.
Our Recommendation Process
Start with a free trial - Both platforms offer trial periods to test with your actual data
Define success metrics - Response accuracy, latency, user satisfaction, cost per query
Test with real use cases - Don't rely on generic demos; use your production data
Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
Check vendor stability - Review roadmap transparency, update frequency, and support quality
For most organizations, the decision between BotsCrew and Glean comes down to specific requirements rather than overall superiority. Evaluate both platforms with your actual data during trial periods, focusing on accuracy, latency, ease of integration, and total cost of ownership.
📚 Next Steps
Ready to make your decision? We recommend starting with a hands-on evaluation of both platforms using your specific use case and data.
• Review: Check the detailed feature comparison table above
• Test: Sign up for free trials and test with real queries
• Calculate: Estimate your monthly costs based on expected usage
• Decide: Choose the platform that best aligns with your requirements
Last updated: December 12, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
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DevRel at CustomGPT.ai. Passionate about AI and its applications. Here to help you navigate the world of AI tools and make informed decisions for your business.
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