In this comprehensive guide, we compare BotsCrew and SimplyRetrieve 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 SimplyRetrieve, 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 SimplyRetrieve if: you value completely free and open source
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 SimplyRetrieve
SimplyRetrieve is lightweight retrieval-centric generative ai platform. SimplyRetrieve is an open-source tool providing a fully localized, lightweight, and user-friendly GUI and API platform for Retrieval-Centric Generation (RCG). It emphasizes privacy and can run on a single GPU while maintaining clear separation between LLM context interpretation and knowledge memorization. Founded in 2019, headquartered in Tokyo, Japan, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
82/100
Starting Price
Custom
Key Differences at a Glance
In terms of user ratings, BotsCrew in overall satisfaction. From a cost perspective, SimplyRetrieve offers more competitive entry pricing. The platforms also differ in their primary focus: Chatbot Platform versus RAG Platform. 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
SimplyRetrieve
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
Uses a hands-on, file-based flow: drop PDFs, text, DOCX, PPTX, HTML, etc. into a folder and run a script to embed them.
A new GUI Knowledge-Base editor lets you add docs on the fly, but there’s no web crawler or auto-refresh yet.
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
Ships with a local Gradio GUI and Python scripts for queries—no out-of-the-box Slack or site widget.
Want other channels? Write a small wrapper that forwards messages to your local chatbot.
Embeds easily—a lightweight script or iframe drops the chat widget into any website or mobile app.
Offers ready-made hooks for Slack, Zendesk, Confluence, YouTube, Sharepoint, 100+ more.
Explore API Integrations
Connects with 5,000+ apps via Zapier and webhooks to automate your workflows.
Supports secure deployments with domain allowlisting and a ChatGPT Plugin for private use cases.
Hosted CustomGPT.ai offers hosted MCP Server with support for Claude Web, Claude Desktop, Cursor, ChatGPT, Windsurf, Trae, etc.
Read more here.
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
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
Defaults to WizardVicuna-13B, but you can swap in any Hugging Face model if you have the GPUs.
Full control over model choice, though smaller open models won’t match GPT-4 for depth.
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
Interaction happens via Python scripts—there’s no formal REST API or SDK.
Integrations usually call those scripts as subprocesses or add your own wrapper.
Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat.
API Documentation
Target Customer: Enterprises with $10,000+ budgets vs developers and SMBs seeking self-service
Use Case Mismatch: Comparing BotsCrew to CustomGPT.ai is architecturally misleading - fundamentally different product categories
Platform Type: NOT A RAG-AS-A-SERVICE PLATFORM - Open-source academic research project for local Retrieval-Centric Generation experimentation and learning
Core Mission: Provide localized, lightweight, user-friendly interface to Retrieval-Centric Generation (RCG) approach for machine learning community exploration and research
Academic Foundation: Published research tool from RCGAI with arXiv paper (2308.03983) explaining RCG methodology and architectural design decisions
Target Market: Researchers, developers, and organizations experimenting with RAG locally without cloud dependencies—NOT commercial service users
Self-Hosted Infrastructure: MIT-licensed tool requiring user-managed GPU hardware or cloud compute—no managed infrastructure, APIs, or service-level agreements
Developer-First Design: Python-based with Gradio GUI and script execution—intended for technical users comfortable with GPU infrastructure and model management
RAG Implementation: Retrieval-Centric Generation (RCG) philosophy emphasizing retrieval over memorization—FAISS vector search with open-source LLMs (WizardVicuna-13B default, any Hugging Face model supported)
API Availability: NO formal REST API or SDKs—interaction via Python scripts and local Gradio interface requiring subprocess calls or custom wrappers
Data Privacy Advantage: 100% local execution with zero external transmission—ideal for classified, PHI, PII, or confidential data requiring air-gapped processing
Pricing Model: Completely free (MIT license) with no subscription fees—only cost is GPU hardware or cloud compute infrastructure
Support Model: Community-driven GitHub Issues and lightweight documentation—no paid support, SLAs, or customer success teams
LIMITATION vs Managed Services: NO managed infrastructure, automatic scaling, production-grade monitoring, enterprise security controls, or commercial support—users responsible for all operational aspects
LIMITATION - No Service Features: NO authentication systems, multi-tenancy, user management, analytics dashboards, or SaaS conveniences—pure research/development tool
Comparison Validity: Architectural comparison to commercial RAG-as-a-Service platforms like CustomGPT.ai is MISLEADING—SimplyRetrieve is open-source research tool for on-premises experimentation, not production service
Use Case Fit: Perfect for offline/air-gapped RAG research, developers learning RAG internals with full transparency, organizations with strict data isolation requirements (defense, healthcare PHI compliance), and teams wanting zero cloud costs with existing GPU infrastructure
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
Market position: MIT-licensed open-source local RAG solution running entirely on-premises with open-source LLMs (no cloud dependency), designed for developers and tinkerers
Target customers: Developers experimenting with RAG locally, organizations with strict data isolation requirements (healthcare, government, defense), and teams wanting complete control without cloud costs or vendor dependencies
Key competitors: LangChain/LlamaIndex (frameworks), PrivateGPT, LocalGPT, and cloud RAG platforms for teams needing simplicity
Competitive advantages: Completely free and open-source (MIT license) with no fees or subscriptions, 100% local execution keeping all data on-premises, full control over model choice (any Hugging Face model), Python-based with full source code access for customization, "Retrieval Tuning Module" for transparency into answer generation, and zero external dependencies beyond local compute
Pricing advantage: Completely free with MIT license; only cost is GPU hardware or cloud compute; best value for teams with existing GPU infrastructure wanting to avoid subscription costs; requires technical expertise and hands-on maintenance
Use case fit: Ideal for offline/air-gapped environments requiring complete data isolation (defense, healthcare with strict PHI requirements), developers learning RAG internals and experimenting locally, and organizations with GPU infrastructure wanting zero cloud costs and complete control over LLM stack without vendor dependencies
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
Hugging Face Compatibility: Swap in any Hugging Face model with sufficient GPU resources (Llama 2, Falcon, Mistral, etc.)
Full Local Control: Models run entirely on-premises with no external API calls or cloud dependencies
Embedding Model: Default multilingual-e5-base for retrieval with option to swap for other embedding models
Model Customization: Fine-tune or quantize models for specific use cases and hardware constraints
No Vendor Lock-In: Complete flexibility to use any open-source LLM without subscription fees or API limits
GPU Requirements: Smaller models may not match GPT-4 depth but enable complete data isolation and zero operational costs
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
Air-Gapped Environments: Defense, classified research, and secure facilities requiring complete offline operation without external connectivity
Healthcare PHI Compliance: HIPAA-regulated organizations needing 100% data isolation for protected health information
RAG Research & Education: Developers learning RAG internals with full visibility into retrieval and generation processes
Local Experimentation: Prototype RAG applications locally before committing to cloud infrastructure and subscription costs
Data Sovereignty: Organizations with strict data residency requirements preventing cloud storage or processing
Zero-Cost RAG: Teams with existing GPU infrastructure wanting to avoid subscription fees for RAG capabilities
Custom Model Development: Research teams fine-tuning and testing custom LLMs and embedding models for specific domains
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)
Setup/Implementation: $3,000+ one-time costs for initial deployment, configuration, and integration
Advanced Features: Up to $5,000/month for enterprise-grade capabilities with custom integrations
Development Services: $50-99/hour for custom development, integrations, and ongoing optimization
Minimum Project Size: $10,000+ investment required - blocks small businesses and startups from entry
No Free Tier: Only free trial, demos, and consultations available - no self-service free option for evaluation
White-Label Partner Benefit: Free GPT-4 chatbot prototype for reseller partners to demonstrate capabilities
Pricing Factors: Scales based on message volume, integrations, LLM usage costs, private hosting requirements, complexity
Market Feedback: Reviews note "on the more expensive side" and "really more of an enterprise solution" vs SMB-friendly pricing
Entry Barrier: Premium pricing excludes affordable RAG solution seekers and small business budgets ($600/mo vs $99/mo competitors)
Completely Free: MIT open-source license with no subscription fees, API charges, or usage limits
Infrastructure Costs Only: GPU hardware or cloud compute (AWS/GCP/Azure GPU instances) are the only expenses
No Per-Query Charges: Unlimited queries without per-request pricing or rate limits
No Vendor Fees: Zero payments to SaaS providers or LLM API vendors (OpenAI, Anthropic, etc.)
GPU Requirements: Single GPU sufficient for development; scale hardware based on throughput needs
Open-Source Ecosystem: Leverage free Hugging Face models, FAISS library, and PyTorch without licensing costs
Best Value For: Teams with existing GPU infrastructure or ability to provision cloud GPU instances economically
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
GitHub Repository: Open-source at github.com/RCGAI/SimplyRetrieve with code, documentation, and examples
Research Paper: Academic publication on arXiv (2308.03983) explaining RCG approach and architecture
Community Support: GitHub Issues for bug reports, feature requests, and community troubleshooting
Lightweight Documentation: README and docs directory with setup instructions and usage examples
No Paid Support: Community-driven support only; no SLAs or enterprise help desk available
Code Examples: Example scripts and Jupyter notebooks demonstrating core functionality
Academic Background: Built on established libraries (Hugging Face, Gradio, PyTorch, FAISS) with extensive external documentation
Documentation hub: Rich docs, tutorials, cookbooks, FAQs, API references for rapid onboarding
Developer Docs
Email and in-app support: Quick support via email and in-app chat for all users
Premium support: Premium and Enterprise plans include dedicated account managers and faster SLAs
Code samples: Cookbooks, step-by-step guides, and examples for every skill level
API Documentation
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
Great for offline / on-prem labs where data never leaves the server—perfect for tinkering.
Takes more hands-on upkeep and won’t match proprietary giants in sheer capability out of the box.
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
Developer-Only Tool: Requires Python expertise, GPU knowledge, and technical setup—not suitable for non-technical users
GPU Infrastructure Required: Needs dedicated GPU hardware or cloud GPU instances with associated costs and management overhead
Basic UI: Gradio interface is functional but not polished—requires custom front-end development for production use
Limited Scalability: Scaling requires manual infrastructure management and load balancing vs auto-scaling cloud platforms
No Enterprise Features: Missing multi-tenancy, user management, advanced analytics, and production-grade monitoring
Slower Inference: Open-source models on single GPU (few to 10+ seconds per reply) vs sub-second cloud API responses
Manual Knowledge Base Updates: No automatic web crawling, syncing, or scheduled reindexing capabilities
No Pre-Built Integrations: Requires custom development to integrate with Slack, websites, or support platforms
Limited Context Memory: Primarily single-turn Q&A with minimal conversation history retention
Maintenance Burden: User responsible for updates, model management, troubleshooting, and infrastructure maintenance
Managed service approach: Less control over underlying RAG pipeline configuration compared to build-your-own solutions like LangChain
Vendor lock-in: Proprietary platform - migration to alternative RAG solutions requires rebuilding knowledge bases
Model selection: Limited to OpenAI (GPT-5.1 and 4 series) and Anthropic (Claude, opus and sonnet 4.5) - no support for other LLM providers (Cohere, AI21, open-source models)
Pricing at scale: Flat-rate pricing may become expensive for very high-volume use cases (millions of queries/month) compared to pay-per-use models
Customization limits: While highly configurable, some advanced RAG techniques (custom reranking, hybrid search strategies) may not be exposed
Language support: Supports 90+ languages but performance may vary for less common languages or specialized domains
Real-time data: Knowledge bases require re-indexing for updates - not ideal for real-time data requirements (stock prices, live inventory)
Enterprise features: Some advanced features (custom SSO, token authentication) only available on Enterprise plan with custom pricing
Core Agent Features
N/A
Retrieval-Centric Generation (RCG): Research-backed approach separating LLM reasoning capabilities from knowledge memorization—more efficient than traditional RAG architectures
Retrieval Tuning Module: Developer-focused transparency layer showing which documents were retrieved, how queries were constructed, and how answers were generated
Knowledge Base Mixing (MoKB): Route queries across multiple selectable knowledge bases with intelligent source selection and weighting
Explicit Prompt Weighting (EPW): Fine-grained control over retrieved knowledge base influence in final answer generation
Single-Turn Q&A Focus: Primarily designed for single-turn question answering—limited multi-turn conversation and context memory
Analysis Tab Transparency: Visual debugging interface showing document retrieval process and query construction for answer inspection
Local Agent Execution: All agent processing happens on-premises with zero external API calls—complete control over agent behavior and data
LIMITATION - No Chatbot UI: Gradio interface for developers only—no polished conversational interface for end users or production deployment
LIMITATION - No Lead Capture: No built-in lead generation, email collection, or CRM integration capabilities—manual implementation required
LIMITATION - No Human Handoff: No escalation workflows, live agent transfer, or fallback mechanisms for complex queries—developer must build these features
LIMITATION - No Multi-Channel Support: No native integrations with Slack, Teams, WhatsApp, or website widgets—requires custom wrapper development
LIMITATION - No Session Management: Stateless interactions without conversation history tracking or multi-turn context retention
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 SimplyRetrieve 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
Switching between BotsCrew and SimplyRetrieve 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 SimplyRetrieve begins at custom pricing. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.
Our Recommendation Process
Start with a free trial - Both platforms offer trial periods to test with your actual data
Define success metrics - Response accuracy, latency, user satisfaction, cost per query
Test with real use cases - Don't rely on generic demos; use your production data
Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
Check vendor stability - Review roadmap transparency, update frequency, and support quality
For most organizations, the decision between BotsCrew and SimplyRetrieve comes down to specific requirements rather than overall superiority. Evaluate both platforms with your actual data during trial periods, focusing on accuracy, latency, ease of integration, and total cost of ownership.
📚 Next Steps
Ready to make your decision? We recommend starting with a hands-on evaluation of both platforms using your specific use case and data.
• Review: Check the detailed feature comparison table above
• Test: Sign up for free trials and test with real queries
• Calculate: Estimate your monthly costs based on expected usage
• Decide: Choose the platform that best aligns with your requirements
Last updated: December 11, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
The most accurate RAG-as-a-Service API. Deliver production-ready reliable RAG applications faster. Benchmarked #1 in accuracy and hallucinations for fully managed RAG-as-a-Service API.
DevRel at CustomGPT.ai. Passionate about AI and its applications. Here to help you navigate the world of AI tools and make informed decisions for your business.
People Also Compare
Explore more AI tool comparisons to find the perfect solution for your needs
Join the Discussion
Loading comments...