Insights from hands-on experience
Use Cases: Chat. Process. Automate. Act
AI-enabled chatbots connected to your data sources that can answer questions, process data, automate tasks, and take action. 'Chatbot' is probably a misnomer for today's AI Bots. Chatting is just one function they can do. They are more like business power tools, with potential to supercharge your business. I call them PowerBots
Power Bots: Their integration with Zapier and Make.com allows them to take actions like: update record in leads CRM, qualify incoming leads, set appointments with calendar events, send out email, make and receive phone calls / SMS. And connecting them to an API server, allows them to process and analyze data based on chat instructions and return back results (See my live example of YFIN Bot below)
There are another 200K+ (estimated) examples in GPT store. Legal Advisors, SEO Analysts, Lead Generation Agents, Web Designers, Adventure Planners, Resume Makers, Job Application Wizards ....covering areas like: health, law, education, productivity, travel, adventure, web design, plant care, books, food, jobs.
And yes, their 'regular' chatbots function for customer service, company policy, document Q&A, lead generation, etc., connected to your data sources: PDFs, Text, Tables, URLs.
In this article, I am looking at no-code custom chatbot builders that allow for rapid build and deployment. Fast-evolving space with options available across a range of requirements. Point to note: some use cases may be beyond the capabilities of no-code builders, but platforms like Flowise offer great options to integrate AI solutions with fully coded platforms.
Summary of Insights:
Classifying into four categories:
Pure Play Chatbot Builders
ChatFlow based
Open AI Assistant - Wrappers
LLM (Large Language Model) Apps Development Platform.
Discussing 4 chatbot builders in this post covering the four categories: Botsonic / Zapier / Botpress / Flowise. This is a curated and opinionated list, based on my experiences. Many other great platforms out there.
Selecting the right platform would depend on the specific use case. Check out the article to help assess.
Each of these chatbots can manage simple use cases with equal ease. They are all no-code GUI-based builders.
For the simplest and fastest deployment: Botsonic and Zapier.
If you need to incorporate multiple forms, interfaces, pages: Zapier.
If chat flow-management functionality is required: Botpress.
For complex use cases : Flowise.
To publish your GPTs on GPT store to web: Flowise / Botpress
Costing, functionalities, limitations, security considerations, learning curve for tool, ease of use, indicative speed to deploy, and other considerations covered in the article below.
Basis of Article
This article is based on my firsthand experience with these platforms, data from official platform resources as well as information from other credible web sources. It includes factual details, insights and opinions based on these experiences and information, as well as my experience with Gen AI technologies. Please note that not every feature mentioned has been individually tested by me, but I have made every effort to ensure accuracy and reliability.
Live App & Prototypes
YFIN bot, developed with Flowise, is live on my website tigzig.com. This live chatbot demonstrates capability of a bot to take instructions, carry out data processing and return back results.
YFIN bot extracts financial data from Yahoo Finance, including P&L, Balance Sheet, Cash Flows, and Quarterlies for multiple periods. It uses Flowise custom functions to make an API call to a custom-built FastAPI server running my Python code.
As part of live testing, I have also published prototype chatbots on my website for Botsonic, Botpress and Flowise. For this, I took a simple document Q&A use case.....Warren Buffett's 2023 letter shareholders uploaded to knowledge base. Around 21 pages, PDF converted to text. Zapier was only partially tested on their platform itself, as their free tier doesn't allow document upload / website embeds.
Different categories for different requirements
Pure Play Chatbot Builders
Fastest to deploy. Easiest to use. Simple chatbots live in <30 minutes. Fully managed. No Code.
Players: Botsonic, Zapier, Dante-AI, ChatBase, ChatSimple, and others.
Suitability: Super fast deployment. Very easy to use. For relatively simpler use cases.
Limitations : Limited customizations.
Chatflow-based
Traditional chatbots integrating with LLM Models like ChatGPT.
Players: Botpress, Voiceflow, and others.
Suitability: Use cases requiring chat flow-management options.
Limitations: UI will have a learning curve, though relatively easy to get upto speed. Relatively limited AI specific customizations.
OpenAI Assistant API Wrappers:
Wrapper for OpenAI Assistant. Setup a custom GPT/Bot in Open AI Playground and publish on the web via ready to use templates. Deploy in <1 hour. Simple to use. Full power of ChatGPT, so to say, as no RAG/ processing layer in between.
Players: Flowise, Botpress, and others.
Suitability: To publish your GPT on GPT store to web. Not all GPTs can be 'migrated' easily though. Depends on GPT configuration.
Limitations: Deploying a GPT outside GPT store increases the cost, sometimes exponentially, depending on the GPT configuration....especially if the GPT uses ChatGPT4. Reason being: for Assistant's Open AI charges based on tokens, and token counts can run into millions very quickly for many uses cases. Pricing (openai.com)
LLM Apps Development Platforms
Platforms like Flowise offer huge customization, no data limits, custom code support, API calls, Retrieval Augmented Generation (RAG) with Langchain & LlamaIndex, connectivity to vector databases and RDBMS. Deploy in <1 hour. Offer great options to integrate AI solutions with fully coded platforms, in case of highly complex apps.
Players: Flowise, Dify, and others.
Suitability: From simple to fairly complex LLM applications.
Limitations: UI will have a learning curve, though relatively easy to get upto speed. No option to create custom frontend / GUI. Flowise needs to be self hosted.
Factors to be considered and comparisons.
Fully Managed Vs. Self Hosted
Flowise is Open Source and needs self-hosting, while all the rest are fully managed platforms. See costing details in the section below. Can be hosted on any cloud platform of your choice: Render, Railway, AWS, GCP, Azure. If you are starting up, I would suggest Render for ease of use. Render deployment just needs Flowise GitHub repository to be forked, then connect to Render, choose your plan and disk size, add a few details and that's it. The whole process takes less than an hour.
Great video tutorial : Flowise AI Tutorial #5 - Deploying to Render (youtube.com). Flowise documentation also has a great step-by-step guide for deployment across cloud platforms. Deployment | FlowiseAI. Reference section at the end includes tutorial and video links for Flowise and Botpress.
Quality of Answers & LLM Choice
For simple use cases, there's unlikely to be much difference in the quality of answers across platforms. However, as the number and complexity of data sources increase, the quality of answers is likely to vary and would need to be tested out.
The quality of answers depends on the LLM being used, RAG setup (if used), and factors like token size limits if configured.
Botsonic provides LLM choices including GPT-4, Mistral, Google Gemini Pro, and Command R, while Zapier offers ChatGPT-4. Flowise offers whole range of LLMs, while Botpress currently only supports GPT-3.5 and 4 models.
GPT-4 would provide better quality answers, but it is expensive (see LLM cost paragraph for more info). Free tier models like Gemini Pro and Groq (Mistral) are great for many day-to-day use cases.
Flowise provides huge configuration options, including the choice of LLM model and RAG architecture.
Costing
Botsonic and Zapier have fixed pricing, with add-on options for various functionalities. But pricing is much higher if you use GPT-4 / Mistral large. See LLM cost section below.
Botpress bots are fixed price, but LLM/ChatGPT costs would be extra.
Flowise is open-source and therefore free, but LLM and server costs would be separate.
Server costs for Flowise on Render: The Starter pack costs $7 per month for 512MB RAM and 0.5 CPU, with support for up to 512GB RAM and 64 CPUs. Additional disk space at $0.25 per GB per month. The Starter pack and 1 GB are enough to start off with for testing. Render has a great free tier if you just want to check out Flowise, but otherwise the free tier is not feasible for Flowise. With the free tier, the server goes to sleep after half an hour of inactivity and takes around a minute to 'wake up' and also you lose your saved chatbots, though you can export them to save locally and reimport later.
LLM cost, for both Flowise and Render, can vary depending on the LLM you choose. ChatGPT-4, though very powerful, is also very expensive at around $30 per million tokens (mtk) for output and $10/mtk for input. In comparison, ChatGPT-3.5 is $1.5 mtk for output and $0.5 mtk for input. Google Gemini Pro has a great free tier with a rate limit of 60 rpm (requests per minute). Groq is currently free, with rate limits of 40 rpm, 40K tokens per minute, and 14.4K requests per day, and I believe it uses one of the Mistral models in the integration with Flowise. Other models' pricing and free tiers would vary. Flowise also provides access to open-source models.
Security and Risk of Proprietary Data Leakage
This is a known issue with all LLM Apps, including chatbots. For unsecured chatbots, with simple prompts, the bots will share all custom instructions as well as file content from uploaded files.
See my article on LinkedIn for more details about the hacks and potential countermeasures: Code Red: Unprotected GPTs & AI Apps exposed by simple hacks.
Botsonic offers protection against basic hacks. I didn't check for more advanced hacks. Zapier didn't seem to have basic protection built-in and would need to be incorporated. Botpress and Flowise are more customized solutions and would need security measures to be incorporated separately.
Automation and Actions with Zapier & Make
Connecting a chatbot to Zapier / Make allows a chatbot to 'take action'. For instance based on information collected during a chat, the chatbot can update a leads database / google sheets / CRM. Or a chatbot can potentially be automated to trigger an inbound or an oubound call (see link at end).
Zapier and make connect to thousands of platforms allowing huge potential for such actions and automations. YouTube has some amazing examples. Sharing a short list in resources section at the end.
Customization Options
Botsonic and Zapier are pre-configured chatbots with limited customization options, but they do offer a fair degree of functionality. One great feature with Zapier is its interfaces, forms, and pages. Botpress provides a good range of customization options, particularly for configuring chatflows. Flowise offers the highest level of customization as it is a full-fledged LLM Apps development platform. Complex customization is relatively straightforward in Flowise.
Large data corpus / RAG
If you have a large corpus of documents, then it might need an RAG setup. This video from IBM provides a nice and simple explanation of RAG: What is Retrieval-Augmented Generation (RAG)?
RAG architecture and optimization are still evolving areas. Whether to use RAG or not, and if so, what kind of architecture to use and how best to optimize is a whole different area in itself.
But if you do need to use RAG, Flowise is probably the best choice given that it's based on the Langchain framework and also supports Llama Index, both with great RAG solutions.
At the same time, do keep in mind that RAG may not always be required or necessary. With increasing context size windows and decreasing LLM costs, many use cases can do without RAG. Here's a nice video from Lyzer AI. Argues for RAG, but explains concept very nicely: Is RAG Dead?
Rapidly Evolving Field
Gen AI space as well as chatbot builders is a rapidly evolving field. Traditional chatbot builders like Botpress and Voiceflow are rapidly integrating with Gen AI technologies. In fact, Botpress is moving towards what it calls a 'GPT Native' platform. Platforms like Flowise are adding features by the day. New players like Zapier are coming in . On the LLM side, LLM costs are dropping, context windows are increasing, free tiers are getting larger and more and more 'high quality' open source models are coming into play. And deployments as well as integrations are getting easier and cheaper.
And this is just the beginning.
As the whole Gen AI space evolves, very likely the landscape would look very different a year from now.
Community Support, References and Links
There are lots of excellent video tutorials on YouTube on AI Chatbots, covering functionalities, usage and applications. Very grateful to community members for sharing such valuable information. Below is a small list.
Flowise Tutorial from Leon van Zyl. Excellent tutorial. Makes getting upto speed on Flowise fast and easy Flowise AI (2024) Tutorial - YouTube
Flowise Tutorials: Thomas Ingram has some excellent videos on Flowise apps: Thomas Ingram - YouTube
Botpress Channel on YouTube has great videos to help get upto speed: Botpress YouTube Channel
Botpress GPT-native platform : blog releasing its new GPT-native platform, along with a Video guide to new platform. Unveiling the new Botpress: a GPT-native bot-building platform | Botpress Blog
Link to Botpress template for rapid deployment of Open AI Assistant: How To Deploy a Custom OpenAI Assistant to Your Website or Messaging Channel | Botpress Blog
How to build a classic lead capture chatbot in Botpress by connecting to Zapier and Make. Can be used for any chatbots that can connect to Zapier / Make / Webhook. Video from Alex Make | Automation Software | Connect Apps & Design Workflows
Enable chatbot to make or receive call: Amazing video and receiving and making calls. This is slightly different use case, but I believe the framework can be utilized in any chatbot that can link up with Zapier/ Make. Sending and Receiving a call to chatGPT using Whisper, Twilio & Zapier - YouTube
Enable chatbot to send personalized SMS : Excellent video from Corbin on automation with Zapier and ChatGPT. Here the trigger is a Mailchimp link click which ultimately sends out a personalized SMS. This can easily be adapted to use with chatbot by just changing the trigger to an action taken by user in your chatbot. Zapier and ChatGPT For Twilio: OpenAI For Making SMS Text | Tutorial - YouTube