FAQ
Rule-based chatbots and AI chatbots represent two different approaches in the field of chatbot technology. Rule-based chatbots work on the basis of firmly defined rules and a predefined structure to respond to user queries. Their mode of operation is essentially deterministic, meaning they can only respond to specific inputs with specific outputs and require clear instructions to function effectively.
In contrast, AI chatbots, especially those with ChatGPT support, use machine learning and natural language processing (NLP) to gain an understanding of human language. They continuously learn from the data they interact with, improving their ability to have natural conversations and respond to a wide range of queries.
The main advantage of AI chatbots is their ability to understand complex queries and respond flexibly to a variety of user requests, making them more effective in dynamic and unpredictable conversational contexts. They can better understand the intent behind users’ queries and provide personalized and contextualized responses, resulting in an improved user experience. Their amazing feature is, that they can be trained with an individual knowledgebase consisting of diverse document types such as pdf, word or exel docs, google docs, sheets, webpages and many more, so that the unterlying language models such as gpt4 can just answer based on this information. Fallbacks zu chatgpt knowledge is, of course, possible.
On the other hand, rule-based chatbots are more effective in environments with set parameters and clearly defined processes, where their predictable and consistent performance is valued. The choice between a rule-based chatbot and an AI chatbot ultimately depends on the specific requirements and goals of a project, with the more advanced AI chatbots offering a higher initial investment but also greater potential for more natural and effective user interaction.
The decision between a rules-based chatbot and an AI chatbot depends on several factors, and it’s important to conduct a thorough analysis to make the best choice. Here are some key questions you should ask yourself:
Complexity of requests:
- What kind of queries do you expect? Are they simple and direct or complex and ambiguous?
- Is the user query often unstructured and requires interpretation?
Adaptability and learning:
- Do you want your chatbot to learn from the interactions and improve over time?
- How often do you expect changes in data or requests, and how flexible does your chatbot need to be to respond?
Smart answering from a knowledgebase:
- Do you want your chatbot to answer from a knowledgebase, that the chatbot is trained with?
- How often do you expect changes in this data?
- Is chatgpt as a fallback necessary, when no anwer is found in the knowledgebase?
Budget and resources:
- What financial and human resources do you have available, both for initial development and long-term maintenance?
- Are you willing to invest in more advanced AI technology to deliver a better user experience?
Privacy and compliance:
- There are sometimes strict privacy and compliance requirements that need to be considered. AI chatbots are more difficult to deploy in a privacy-compliant manner than rule-based chatbots due to the integration of AI and thus, for example, language models from external providers (from overseas).
- Do you have enough data to train an AI chatbot and is this data secure and compliant with privacy policies?
Technical infrastructure:
- What kind of technical infrastructure and expertise do you have available?
- Is your organization ready to support the necessary infrastructure for an AI chatbot?
User interaction goal:
- Are you looking for a tool for simple information queries or a sophisticated assistant for deeper user interaction?
- What are your users’ expectations for interacting with the chatbot?
Integration capability:
- Does the chatbot need to integrate with existing systems and processes?
- How complex are the required integrations?
By answering these questions, you can better understand what type of chatbot best fits your organization’s needs and goals. Clearly defining your requirements and weighing the pros and cons of each chatbot type will help you make an informed decision that can positively impact the success of your chatbot project.
A hybrid chatbot combines the capabilities of rule-based and AI-driven (typically machine learning or natural language processing-based) chatbots with a trained knowledgebase. It is designed to deliver a more comprehensive solution, leveraging the strengths of both methods. Here’s a closer look at its components:
- Rule-Based Chatbot: This operates based on a set of predefined rules. It can answer questions that fall within these rules, which means its responses are predictable. However, it might not handle questions outside of its defined rules well.
- AI-Driven Chatbot: Utilizes machine learning and natural language processing to understand user input and provide relevant responses. It can learn from interactions, handle more complex queries, and even manage conversations that it hasn’t been explicitly programmed for. However, it might sometimes produce unexpected or inaccurate answers.
Reasons to Choose a Hybrid Chatbot:
- Balanced Interaction: Hybrid chatbots can provide predictable and accurate answers using rule-based systems for known queries, while leveraging AI for more complex, nuanced, or new questions.
- Reliability: Rule-based systems ensure that for specific, well-defined queries, the bot will always give the correct answer, ensuring consistency and reliability.
- Learning and Adaptability: The AI component allows the bot to learn from user interactions, improving its performance over time.
- Handling Complex Queries: AI-driven chatbots can process and understand more complex queries, making the chatbot more versatile.
- Scalability: As the business grows and the range of user queries expands, it’s easier to scale a hybrid system. You can add more rules to the rule-based system and the AI component can learn from newer interactions.
- Error Handling: In case the AI component fails to understand a user’s query or provides a less than optimal response, the rule-based system can be used as a fallback mechanism.
- Flexibility: Businesses can customize the balance between rule-based and AI-driven components based on their needs. For instance, critical queries where accuracy is paramount can be routed through the rule-based system, while general queries can be handled by the AI component.
- Knowledge Base Integration with AI Fallback: Hybrid chatbots seamlessly merge structured information retrieval from a knowledge base for predefined queries with the dynamic response capabilities of AI like ChatGPT. If a query isn’t in the knowledge base, the AI component generates a contextually relevant response, ensuring comprehensive coverage and continuous adaptation to user needs.
- Improved User Experience: Combining the strengths of both systems provides users with a more seamless and efficient interaction, enhancing their overall experience.
In summary, hybrid chatbots offer a combination of predictability and adaptability, making them suitable for a wide range of applications. They can be particularly beneficial for businesses that have a mix of standard, frequently asked questions and more complex, varied user queries.
The privacy-compliant use of rules-based and AI chatbots on European websites is a critical aspect that needs to be carefully considered, as countries in the European Union have strict data protection laws. Here are some considerations and differences between the two types of chatbots in terms of data privacy:
- Data collection and processing: Rule-based chatbots typically collect and process less data compared to AI chatbots because they are based on fixed rules rather than machine learning. AI chatbots, on the other hand, may require large amounts of data to function and learn effectively.
- Transparency: With rule-based chatbots, data processing is often more transparent and predictable because they are based on fixed rules. With AI chatbots, data processing can be less transparent due to the complexity of the algorithms, which can make it more difficult to comply with data protection requirements.
- Consent and information requirements: Regardless of the chatbot type, website operators must ensure that they obtain user consent before collecting and processing personal data. They must also provide clear information about how the data will be used.
- Data minimization: The principles of data minimization and purpose limitation are central to European data protection laws. Rule-based chatbots may have an advantage here, as they may require less data and data processing can be more easily controlled.
- Data deletion and correction: There must be mechanisms that allow users to request deletion or correction of their data. Implementing such mechanisms might be easier for rule-based chatbots.
- Data security: Both chatbot types require robust security measures to protect the data collected and processed.
- Training data for AI chatbots: When using AI chatbots, care must be taken to ensure that training data is privacy compliant and does not contain sensitive or personal information unless clear consent has been obtained.
- Data Processors and Controllers: Under GDPR, entities that handle personal data are categorized as either data controllers or data processors. The organization that determines the purposes and means of processing personal data is the data controller. Meanwhile, any entity that processes the data on behalf of the controller is the data processor. In many cases, chatbot providers act as data processors. Therefore, a contract delineating responsibilities is essential to ensure both parties understand and adhere to their obligations.
- Data Processing Agreements (DPAs): Article 28 of the GDPR specifies that controllers must only use processors that provide adequate guarantees to implement appropriate technical and organizational measures. This ensures that processing will meet the GDPR requirements and protect data subjects’ rights. Consequently, controllers often enter into DPAs with processors, which are specific contracts outlining how data is to be handled, protected, and processed.
- Sub-processing: Some chatbot providers might use third parties to handle certain aspects of their service. GDPR requires that any sub-processors used must also comply with the regulation. Therefore, contracts with chatbot providers should stipulate conditions for engaging sub-processors, including obtaining the data controller’s consent and ensuring that sub-processing agreements uphold GDPR standards.
- Data Transfer: If a chatbot provider transfers personal data outside the European Economic Area (EEA), they must ensure that the data remains protected and that the receiving country provides an adequate level of data protection. Contracts might include standard contractual clauses or other mechanisms to ensure this compliance.
- Data Breach Notifications: Contracts should detail the procedures and timelines for notifying the data controller in the event of a data breach. Quick and appropriate notifications are crucial, as GDPR has strict timelines (within 72 hours) for notifying authorities of breaches.
- Audits and Inspections: To ensure ongoing compliance, data controllers might wish to periodically audit or inspect the data processing practices of their chatbot providers. Contracts can stipulate the rights of controllers to conduct such audits and the responsibilities of processors to cooperate.
- Data Retention and Deletion: GDPR emphasizes data minimization and the right to erasure. Contracts with chatbot providers should detail how long data is retained and the processes for securely deleting data when it’s no longer necessary.
- Liabilities and Indemnities: In case of breaches of GDPR, penalties can be severe. Contracts often delineate liabilities, determining who is responsible for potential fines or legal actions.
It is advisable to seek legal advice on data protection and compliance, especially if you plan to use a chatbot on your website. Data protection officers and legal experts can offer valuable insights into the specific requirements and best practices for implementing chatbots in Germany in compliance with data protection laws.
As part of our chatbot full service, we provide you with all conceivable documents (DPAs) and text templates for data protection compliance (to the best of our knowledge) when using our chatbots. It is important to note that the data protection compliance of these wordings and also the technical solution has not yet been data protection tested. However, we have had the templates compiled for you with data protection lawyers and can also provide contacts here.
Providing a full-service chatbot service to German or European companies is more expensive than compared to countries with less regulated privacy environments due to strict privacy and compliance requirements. Here are some specific factors that lead to the increased costs:
- Legal counsel and compliance management: To ensure compliance with data protection laws such as the General Data Protection Regulation (GDPR), chatbot service providers like us with Chatbot-Service.com must invest in legal advice and compliance management. This includes reviewing and adapting data protection policies and procedures, as well as reporting to regulators.
- Technical and organizational measures: To meet data protection requirements, service providers must implement robust technical and organizational measures. This may include the development of dedicated data protection capabilities, secure data processing and storage solutions, and advanced encryption and anonymization techniques.
- Data storage in the EU: Local data storage requirements increase the cost of providing chatbot services, as data must be stored within the EU. This means the need to invest in local server infrastructure or use more expensive but compliant cloud services.
- Audits and certifications: Regular data protection audits and obtaining data protection certifications are often necessary to demonstrate compliance with data protection requirements. These processes are time-consuming and costly.
- Training and education: Service providers must invest in training and education campaigns to ensure their staff and customers understand and comply with data privacy requirements.
- Customization and support: Providing customization to meet the unique data protection needs of different organizations, as well as ongoing data protection-specific support, significantly increases operational costs.
- Liability and insurance: Increased liability due to stricter data protection laws in the EU results in higher insurance costs.
- Development and maintenance costs: Continuously updating chatbot services to keep up with changing privacy laws can lead to increased development and maintenance costs.
All of these factors require us to make a significant investment in resources, time, and capital, which is reflected in higher costs for our services to German and European companies.
Training the AI chatbot is the key task to feed knowledge to the chatbot equipped with the help of a large language model like chatgpt. AI chatbots process information differently than humans and need to have data prepared differently to make sense of it.
For example, tables must be taught as row-by-row data points, sections that are related in content must be marked, and more. The definition of the data, the cleaning (redundant data), the preparation and the fine tuning for the AI chatbot takes most of the time.
In the finetuning, small blocks are tested to see if content was understood. If it was not understood, commands and instructions are used to instruct the bot to understand the information correctly. In general, one must also structure source data correctly so that data can be exchanged or updated at a later time with as little effort as possible.
Training is somewhat easier with information on web pages or other data available online, since here live updates can also be completed through page-by-page instructions in the programming. This is mainly the subject of finetuning, where it is also tested which information has been interpreted correctly and which has not. In most cases, errors can be corrected with appropriate instructions despite live retrieval.
Currently, the AI chatbot can do what chatgpt 4.0 can do, but with your data in mind, that it is trained with. Since chatgpt is a language model, you can use text input – and soon voice and image input, to have the chatbot respond in writing or verbally.
We have created a training environment that can handle up to 5,000,000 letters of training data (even more on demand). This corresponds to a volume of more than 3.000 pages with about 550 words of written text each for Word or text files (for web pages it is about 1/3 more, because all elements of the page count as letters/words). Thus, our chatbot is capable of generating competent answers from a very extensive knowledge.
The language processing of data is extremely flexible and we can currently process the following types of data:
- Web pages (single pages or all pages through sitemap)
- pdf files
- txt, csv files
- word, excel, powerpoint
- google docs, sheets, etc.
- images
- vidos
- other data types on request
Data security and GDPR compliance is of great concern to us. Yes, your data is safe with us. We implement industry-leading security measures including SSL encryption during transmission and 256-bit AES encryption during storage. We are also actively working to implement SOC 2 standards to provide additional security controls. Data processing takes place in a secure environment, and strict access controls are in place to ensure that only authorized users have access. Our systems and processes are designed to protect your data at all times.
Linking to chatgpt, OpenAI’s language model, also poses no data risk as we do not store the data with OpenAI, we only use the language technology through API usage: https://openai.com/policies/api-data-usage-policies. OpenAI also does not use data to improve ChatGPT’s learning. Your training data is limited to your specific bot, so your content remains local and private.
Language models (currently chatgpt) are integrated directly into our training environment via an API interface. Queries from the chatbot are thus processed with the A.I. of chatgpt while taking into account the compressed (vectorized) knowledge provided in the training environment (knowledgebase). As a backup, if the knowledgebase does not provide adequate answers, the bot can answer via chatgpt knowledge (optional).
Thus it is possible to have fluent conversations with the chatbot and (by prompt engineering) to get complex answers in various output formats – just like it is possible with chatgpt without your training data. Currently, we only use the more capable chatgpt 4.0, because answers generally are precise and hallucinations are rare.
The latest chatgpt language model in version 4.0 is based on far more training data than chat gpt 3.5, making the newer language model far more accurate. That’s why we only currently use chatgpt4. In the near future, clients can also choose other language models such as Llama 2 and Falcon.
Rule-based chatbots operate on a predetermined set of rules and patterns, which are usually designed for a specific language. This design inherently limits them to understanding and responding in the language they were programmed for. Any input in a different language or deviation from the set patterns would render the chatbot unable to respond accurately. This limitation stems from the fact that the rules are fixed and do not have the capability to adapt to new languages unless a new set of rules is created for each additional language. This process can be labor-intensive and time-consuming, making rule-based chatbots less suitable for multilingual operations.
On the other hand, AI-driven chatbots have a fundamentally different architecture that allows them to learn from data. They can be trained on datasets that include multiple languages, enabling them to understand and respond in many languages. The underlying technology often includes advanced language models like GPT-4, which have been trained on a vast amount of text data from various sources, encompassing a multitude of languages. This broad training base provides AI chatbots with a level of language understanding and flexibility that is not available in rule-based chatbots. Furthermore, AI chatbots can have translation features integrated into their systems. These features allow them to translate user input into a language they were trained on, and then translate the response back into the user’s language. This way, even if they were primarily trained in one language, they can still interact in many other languages. The capability of AI chatbots to translate languages on-the-fly makes them highly adaptable and suitable for multilingual interactions in up to 100 languages.
the costs for chatbot services are generally composed of several factors:
- Development: This includes the initial construction of the chatbot, integrating rule-based and AI training mechanisms, and ensuring cohesive functioning within existing systems. Also third party software integrations might make sense which are different in scope. Also, GDPR compliance is an additional cost factor.
- Usage Costs: These are costs related to API calls, processing, and any other platform-related usage fees.
- Optimization and Re-Trainings: This includes ongoing work with check-ups, fixing issues etc. to ensure both the rule-based and AI chatbot perform optimally, addressing any issues and adapting it to handle new or evolved user queries and conversational contexts effectively.
- Security and Compliance: Implementing and sustaining rigorous security protocols to safeguard user data and prevent breaches while meeting data protection standards and industry-specific regulations (such as GDPR compliance).
- Upgrades and Enhancements: Costs related to expanding the chatbot’s capabilities, adding new features, or refining existing functionalities to better meet user expectations and business objectives.
- Monitoring: Employing analytical tools to evaluate chatbot performance and interaction data, ensuring informed future development and refinement.
Chatbot-Service.com’s pricing model consists of one-off development costs and monthly usage costs that cover all cost factors from above. There are three types of bots: Bot Simple, Bot Smart and Bot Max. Each bot has different startup costs and monthly fees based on the included features such as rule count, knowledge base size, GDPR package and software integrations. Additional costs are incurred for additional branches or words in the knowledge base and for more users or interactions per month. The number of users refers to the number of complete chats. It is irrelevant whether the same user asks again a few days later or whether there are always different users. At the end of the chat, one interaction is counted as one user.
See our chatbot types with a cost calculator to exactly see, how much cost will be associated with a chatbot solution. Do not forget that the potential cost savings, lead boosts etc. clearly outweigh these cost.
Rule-based chatbots guide the user through various options, similar to a decision tree. When the user selects an option, specific further options to choose from appear. Each selection of buttons is therefore a node from which branches branch off. If the customer has 4 choices, there are 4 branches. The more extensive e.g. a website is, the more branches must be included in the design.
The training data is always determined from the scan of the transmitted data. The number of characters used on a web page is about 1/3-1/2 higher than in a Word file. However, with a web page you have an easy update option, since online resources can be updated regularly with appropriately rules.
Also, you have to add approximately 1/4 of all text characters for prompt engineering (rules, adjustments, reprocessing etc.) Only after extracting all data we have an idea of the amount of training data (in characters) and can make an approximate estimate with an appropriate markup for customizations. With our free and non-binding chatbot test dummy, you will get a feeling for the amount of processed data, where a few adjustments are already included.
Rule-based chatbots are best suited for simple, structured queries and tasks with clear, predefined processes. They are less suitable for complex or ambiguous queries that require natural language processing or an understanding of the context.
Here are some examples of queries that rule-based chatbots can handle effectively:
- Simple FAQs:
- Rule-based chatbots can respond to frequently asked questions by matching user queries (trigger words) to predefined answers.
- Example: A user asks, “What are your operating hours?” The chatbot recognizes “opening hours” as a trigger word and responds, “We are open from 9 am to 5 pm, Monday to Friday.”
- Structured Data Collection:
- They excel in situations where data can be collected through structured dialogues.
- Example: A user wants to book a flight. The chatbot, triggered again through the words “book” and “flight” asks, “What is your departure city?” followed by “What is your destination city?” and “What date would you like to depart?”
- Basic Troubleshooting:
- They can provide step-by-step troubleshooting assistance based on predefined rules.
- Example: A user reports that their internet isn’t working. The chatbot, again triggered by words such as “internet” and “working” may ask, “Have you tried turning your router off and on again?”
- Navigation Help:
- They can help users navigate through websites or applications by guiding them to the right pages or sections based on their queries.
- Example: A user asks, “Where can I find your contact information?” The chatbot, triggered by words such as “find” and “contact” provides a link to the contact page.
- Form Filling:
- Rule-based chatbots can assist in filling out forms by asking users for necessary information in a structured manner.
- Example: A user wants to sign up for an account. The chatbot, triggered by words such as “sign-up”, asks for their name, email address, and preferred password in a sequential manner. This information can then be connected to third party plugins managing password recovery.
- Appointment Scheduling:
- They can also handle appointment scheduling by asking users for their preferred dates and times.
- Example: A user wants to schedule a service appointment. The chatbot asks, “What date and time works for you?”. This information is then connected to a third-party booking engine to check, whether these dates are available. If not, the user is asked for different dates.
Updating or modifying a rule-based chatbot requires our developers to customize the rule sets and endpoints. We provide ongoing support and maintenance services to ensure that your chatbot keeps pace with changing requirements or business processes. Small changes are done within the regular usage fees, bigger updates are separate projects.
Hiring a service provider like us to set up, install and maintain chatbots can be critical for many businesses, for several reasons:
- Expertise and experience: Professional service providers bring extensive expertise and experience in chatbot development, implementation, and maintenance. They can apply best practices and avoid common pitfalls, which greatly improves the quality and efficiency of the project.
- Time and cost savings: By outsourcing these tasks to specialists, companies can save time and resources that would otherwise be spent on learning, developing and also troubleshooting. This can also help reduce the overall cost of the project and improve ROI.
- Focus on core competencies: Companies can focus on their core competencies while the service provider takes care of the technical aspects of the chatbot project. This allows the company to focus on strategic goals and core business without getting distracted by technical challenges.
- Customized solutions: Service providers can offer customized chatbot solutions that are tailored to a company’s specific needs and goals. They can also ensure that the chatbot is effectively integrated into existing systems and processes.
- Continuous maintenance and support: With a service provider, businesses receive ongoing maintenance and support to ensure that the chatbot is always functioning optimally and remains up-to-date. This includes fixing issues quickly, training with new data (AI chatbots), and adapting to changing requirements.
- Data privacy and compliance: Data protection and compliance are critical aspects in many regions, especially in Europe. We have the expertise with data protection specialized legal partners to ensure chatbot solutions meet legal and regulatory requirements.
- Measurement and optimization: Service providers can provide valuable analytics and insights to measure chatbot performance and make continuous improvements. This helps optimize the user experience and achieve business goals.
- Future-proof technology: With a service provider, businesses can ensure that their chatbot technology is future-proofed and they benefit from the latest developments and innovations in the field.
By partnering with a service provider like Chatbot-Service.com, companies get a high-quality, reliable and privacy-compliant chatbot solution that can significantly improve their customer relationships and business processes. The service of a professional service provider is therefore not only beneficial, but essential for many companies.
Of course, you can cancel at anytime. If notice of termination is submitted up to and including the 15th. day of the current month, the subscription shall end at the end of the following month. If the cancellation is submitted after the 15th. day of the current month, the subscription ends at the end of the month after next.
As a provider of chatbot services, charging usage fees at the beginning of the month is a pragmatic approach that addresses several business and financial imperatives. Here’s a breakdown of the rationale behind this billing cycle:
- Ensuring Cash Flow:
- Collecting the usage fees upfront facilitates a steady cash flow, which is instrumental in covering our operational expenses. This includes the payment of usage fees to any underlying service or infrastructure providers that our chatbot service relies on. A robust cash flow is pivotal for maintaining the financial health and stability of our operations.
- Mitigating Financial Risks:
- Charging the fees at the beginning of the month considerably mitigates the risk of non-payment. If the fees were to be collected at the end of the month and a customer fails to pay, we would be left to cover the costs incurred during that month on their behalf. This scenario could potentially lead to financial losses, especially when it comes to covering the usage fees.
- Allocating Resources Efficiently:
- With the funds collected upfront, we are better positioned to allocate resources efficiently to ensure seamless service delivery throughout the month. This includes ensuring adequate server capacity, staffing our support teams appropriately, and other operational requisites that contribute to delivering the promised service levels.
- Maintaining Service Continuity:
- The upfront payment model ensures uninterrupted service delivery. It allows us to avoid service discontinuation due to non-payment, which not only disrupts our customers’ operations but also could tarnish the user experience and our reputation.
- Facilitating Budgeting and Planning:
- Upfront payments provide a clear view of the revenue we can expect for the month, aiding in better budgeting and financial planning. It also aligns with our customers’ expectations as they are aware of the exact amount due to keep the service operational.
- Adhering to Contractual Obligations:
- Our terms of service or contractual agreements often stipulate the requirement for payment in advance to access the services. This is a common and accepted practice in the industry, which we adhere to for maintaining a standardized service delivery model.
- Aligning with Competitive Standards:
- The practice of charging upfront aligns with the competitive standards in the SaaS and cloud service industries. This standard billing cycle is something our customers are accustomed to and ensures our alignment with industry norms.
By adhering to an upfront billing cycle, we aim to uphold a financially robust and operationally efficient framework that not only serves our business interests but also ensures a positive and consistent service experience for our customers.
Yes! We offer the truly unique service of creating a free demo chatbot for you and training it especially for your business (limited rules and limited AI). You then get access to the chatbot, and you can test it for 1 week as much as you want. Along with that, you’ll also get a short video welcome and a mini-guide on what we’ve prepared for you (and what we haven’t, of course).
We are so convinced of the usefulness of the chatbot that we are happy to take the risk here that you might not be convinced of the benefits of such as chatbot 😉 So what are you waiting for? Let us convince you!