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Custom GPT
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A Custom GPT is a tailor-made AI model from OpenAI based on a generative pre-trained transformer (GPT) and optimized for specific use cases and business contexts. By integrating individual instructions (prompts), proprietary data sets (knowledge base), and specific behavior patterns, such models can be precisely tailored to defined requirements.
In advanced stages of development, especially when they gain access to external tools, custom GPTs often function as specialized AI agentsthat not only generate text but also perform tasks autonomously.
Areas of application in conversational AI and AI workflows
In the field of conversational AI, custom GPTs are used in the development of specialized chatbots and voicebots. They can be configured to provide specific product information in customer service, for example, or to handle technical inquiries in internal support. Adapting them to company-specific terminology and processes ensures a high degree of precision in the responses.
For AI workflows, custom GPTs enable the automation of complex work steps. This includes creating content according to specified style guidelines, analyzing large amounts of data to support decision-making, or processing emails with predefined response templates. The ability to integrate external tools and APIs significantly expands their functionality and allows for seamless embedding into existing enterprise systems.
Benefits of Custom GPTs for Businesses
Companies benefit from custom GPTs by increasing efficiency and reducing manual workload. There is no need to repeatedly formulate detailed prompts, as the model is already trained for specific tasks. This leads to standardization of output and consistent quality of generated content or responses. The scalability of these specialized AI models helps companies optimize their digital processes.
Consideration of data security and configuration of CustomGPTs
Data security aspects are important when implementing custom GPTs. When processing sensitive company information in particular, it must be ensured that this data is protected. Careful configuration of the models and compliance with data protection guidelines are necessary to minimize risks such as data leaks. The options for sharing the custom GPT (privately, via link, or publicly) require a conscious decision with regard to the confidentiality of the stored information.
FREQUENTLY ASKED QUESTIONS
Conventional large language models offer broad functionality. Custom GPTs, on the other hand, are specialized variants of ChatGPT that are adapted to specific business requirements through targeted instructions, proprietary training data, and predefined behaviors. This enables more precise and relevant output for the respective use case.
In Custom GPTs, your own documents and files can be integrated as "knowledge" to impart company-specific knowledge to the model. This could include internal guidelines, product documentation, or historical communication data, for example. External data sources can also be accessed via interfaces.
When using custom GPTs in companies, risks in the area of data security must be taken into account. Especially when using sensitive information, there is a possibility of "GPT leakage," in which stored data could potentially be extracted through targeted queries. Careful configuration and compliance with data protection guidelines are necessary to minimize these risks.
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Aleph Alpha
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Aleph Alpha is a German company specializing in the development and provision of artificial intelligence (AI). Founded in Heidelberg in 2019, it offers fully integrated AI solutions tailored primarily to the needs of businesses and public institutions. The focus is on achieving digital sovereignty through controllable and transparent AI systems.
Key features of Aleph Alpha AI solutions
Aleph Alpha's technology is characterized by several core principles. A key aspect is sovereignty, which ensures complete control over data and intellectual property (IP) and avoids vendor lock-in. Furthermore, great importance is attached to transparency and compliance by ensuring the explainability of AI outputs. This promotes trust and enables compliance with internal and external requirements. The architecture of the systems is designed to be future-proof, allowing adaptation to rapidly evolving AI models without major risks or blockages. Finally, human-machine collaboration is promoted by empowering experts in complex environments to maintain control over processes through transparent and traceable AI support.
Applications and products of the PhariaAI Suite
Aleph Alpha's core product line is the PhariaAI Suite, a comprehensive solution for various AI applications. This suite includes PhariaAssistant for knowledge-based tasks, customized solutions for domain experts, PhariaCatch for structuring expert knowledge, PhariaStudio for developing and optimizing AI models, and PhariaOS for monitoring and scaling AI applications. In addition, open and proprietary large language models (LLMs) are offered. These modules help companies implement AI agents and AI workflows that are tailored to specific business needs and enable efficient automation.
Relevance for conversational AI and AI workflows
The sophisticated and specialized AI systems developed by Aleph Alpha are used in the field of conversational AI—including chatbots and voicebots—as well as in the automation of AI workflows.
One example of this is the AI assistant BAKIRA (“BA KI Research Assistant”), which is being implemented at the Federal Employment Agency. This assistant supports employees with word processing, research, and translation into over 40 languages, and also includes a “self-controlling chatbot” for controlling and finance.
Another example of its application is the collaboration with BMW in the field of engineering, where AI models are used to link requirements with system functions in order to reduce manual workloads and accelerate engineering processes. By focusing on domain-specific AI solutions, greater precision and relevance of results are achieved in business-critical applications.
Frequently Asked Questions (FAQ)
At Aleph Alpha, sovereign AI means guaranteeing complete control over your own data and intellectual property. We place great importance on an architecture that does not create vendor lock-in and offers the possibility to operate, adapt, and scale AI systems independently. This serves to protect sensitive corporate and government data.
Aleph Alpha offers the PhariaAI Suite. This includes various components such as PhariaAssistant for knowledge workers, customer-specific AI solutions, PhariaCatch for capturing expert knowledge, PhariaStudio for development, and PhariaOS for operating AI applications. In addition, the company provides its own large language models (LLMs).
Aleph Alpha's technology is used in various contexts. One prominent example is the AI assistant BAKIRA at the Federal Employment Agency, which supports word processing, research, and translations. Furthermore, collaborations with companies such as BMW are underway to make engineering processes more efficient using AI and to automate specific workflows.
Conversational AI and AI workflows deliver very concrete benefits for companies: 24/7 automation of inquiries, shorter waiting times, and significantly reduced workloads for support teams. Data from CRM, shop systems, ticketing, or knowledge databases is automatically incorporated. This reduces service costs, makes processes more stable, and provides customers with a consistent experience across all channels. With BOTfriends X, BOTfriends provides a suitable platform for companies that want to enable scalable automation and more efficient processes.
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Artificial General Intelligence (AGI)
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Definition and delimitation of AGI
Artificial General Intelligence (AGI) refers to a hypothetical artificial system that has the ability to understand and learn any intellectual task that a human being can understand and learn. It is assumed that AGI will have at least equal or superior capabilities to humans in all cognitive and intellectual areas. The development of AGI is a central research goal in the field of artificial intelligence, but has not yet been achieved.
In contrast, the artificial intelligence that is widely used today is referred to as artificial narrow intelligence (ANI) or weak AI. ANI systems are limited to specific tasks and areas of application and cannot perform in other domains. Examples of this include current chatbots, voice assistants, and image recognition systems. AGI, on the other hand, would be universally applicable and could transfer and apply knowledge and skills across different areas.
Properties and characteristics of an AGI
Artificial general intelligence would replicate the entire spectrum of human cognitive and intellectual abilities. These include learning ability, understanding natural language, the ability to reason and plan, problem-solving skills, and the ability to adapt to new situations. Such a system would possess "common sense" and be able to solve complex problems in environments and contexts for which it has not been explicitly trained. Open questions remain regarding the consciousness or sentience of AGI, which also touches on philosophical dimensions of the research.
Current status and research perspectives
Despite significant advances in the field of artificial intelligence, artificial general intelligence is still considered a distant dream. Current AI systems, including advanced language models, demonstrate remarkable performance in specific areas, but do not yet reach the level of AGI. It is believed that the development of AGI will require various AI concepts and technologies such as machine learning, deep learning, artificial neural networks, natural language processing (NLP) and computer vision. Researchers have different estimates of the exact time frame for the possible realization of AGI, with predictions ranging from a few years (interview with Demis Hassabis, CEO of Deepmind, and Sergey Brin, co-founder of Google) to several decades.
AGI in the context of conversational AI and AI agents
In the field of conversational AI and the development of AI agents at BOTfriends, specialized AI systems are used. These systems, such as chatbots and voicebots, are examples of artificial narrow intelligence, as they are trained for specific conversation flows and tasks. Future artificial general intelligence could fundamentally change the possibilities of conversational AI and AI agents. AGIs could be capable of conducting highly complex dialogues autonomously, applying knowledge flexibly, and adapting independently to new conversation requirements and topics without the need for extensive reconfiguration or retraining. The vision of AGI therefore represents a long-term horizon for the further development of intelligent automation solutions.
Frequently Asked Questions (FAQ)
Artificial General Intelligence (AGI) is characterized by the ability to solve any intellectual task at human level or better, without being limited to a specific domain. In contrast, conventional or "weak" AI (Artificial Narrow Intelligence) specializes in performing specific tasks in predefined domains. Current chatbots or image recognition systems fall into the category of weak AI, while AGI would be a universally applicable and adaptive system.
There is no general consensus on the exact point in time when fully functional artificial general intelligence (AGI) could become a reality. Expert estimates vary considerably, ranging from possible development within the next five to ten years to forecasts that estimate it will take another 20 years or more. Despite their advanced capabilities, current AI systems are not yet considered AGI.
For conversational AI systems, such as those developed by BOTfriends, artificial general intelligence (AGI) would have a transformative significance. Today's conversational AI is trained for specific use cases. AGI, on the other hand, could independently develop new conversation strategies, use knowledge across contexts, and adapt flexibly to unforeseen conversation situations, which would mean far greater autonomy and problem-solving ability in interaction. AGI thus represents a long-term goal in the development of highly intelligent conversation systems.
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Hybrid Human Chatbot
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A hybrid human chatbot is a solution in which chatbot and human service representatives work together to handle customer communications. The chatbot first processes incoming inquiries automatically. If it cannot answer a question or the situation requires personal assistance, it seamlessly transfers the inquiry to a human agent: this is known as human handover. For the user, this switch takes place within the same chat window, without any media discontinuity. The human employees receive the complete conversation history and context, so that no information is lost. At the same time, the chatbot learns from the human responses and continuously expands its knowledge base.
Why is Hybrid Human Chatbot important?
The hybrid approach is particularly valuable for companies in Germany, as it combines efficiency with service quality. Around 80% of customer inquiries are recurring and can be answered by a chatbot in a matter of seconds—24/7, without any waiting times. At the same time, human employees remain available for complex issues, emotional situations, or high-quality consultations. This leads to increased customer satisfaction, as each inquiry is assigned to the appropriate agent. For service teams, this means less routine inquiries and more time for challenging cases. The advantage is particularly evident during peak periods such as the Christmas season: the chatbot acts as an intelligent firewall that filters and prioritizes inquiries.
Hybrid human chatbot in practice
A typical example of application is customer service in e-commerce: The chatbot automatically answers questions about shipping times, returns, or product availability. For customers with high shopping cart values or complex product consultations, the chatbot transfers them to a service representative.
BOTfriends enables such hybrid solutions through seamless integration with a wide variety of live chat systems, such as Zendesk or Userlike. The platform continuously analyzes unanswered questions and automatically suggests new topics, so that the chatbot constantly learns and improves.
Frequently Asked Questions (FAQ)
The human handover takes place seamlessly in the same chat window. The chatbot uses intent analysis, sentiment, or defined triggers to recognize when a human takeover is necessary. The agent receives the complete conversation history, customer history, and context-related information. BOTfriends offers this feature with integration into common live chat systems, eliminating the need for duplicate infrastructure.
Hybrid solutions combine the best of both worlds: 24/7 availability and scalability of AI with human empathy and problem-solving skills. This prevents frustration due to bot limitations, increases the first-contact resolution rate, and enables continuous learning. Companies can start with a smaller bot scope and expand it organically based on real user queries.
The hybrid approach is particularly suitable for customer service, e-commerce, human resources, IT support, and sales. This solution is ideal wherever a wide range of questions is expected or certain process steps require human decisions. BOTfriends supports companies in identifying suitable use cases and offers a testing option prior to full automation using the Wizard of Oz method.
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Prebuilts
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Prebuilts, or ready-made knowledge bases, are a collection of content for specific chatbot use cases. Chatbots live on content that is taught to them. To develop a chatbot, it is important to enrich it with content, i.e. questions and answers. The more content, the broader its knowledge and thus the answers it can give. Prebuilts help with the development and can be used as "boosters". If you want to build a chatbot in the area of IT support, for example, it is very helpful to be able to fall back on such prefabricated knowledge bases so that a basic stock of content is already available depending on the use case.
How are Prebuilts structured?
Prebuilts consist of question options, entities and answers. To increase the speech recognition of a chatbot, it is important to store many different question options for an intent. Furthermore, entities and answers belong to each intention. Prebuilt exemplary answers exist for prebuilts, which the customer can adopt 1:1, but of course also adapt to the company, character and tonalityof the chatbot.
How can Prebuilts be used?
Prebuilts are usually easy to integrate. In most cases, the prebuilts are integrated via the NLP service used. Here, there is often a one-click integration with which you can quickly and easily import the required content. Another possibility to work with prebuilts is the integration within Intent Management Platforms (IMP). These platforms are there to make content-specific changes quickly and easily.
Which Prebuilts are already used sensibly?
There are already many available sample prebuilts that can be integrated into a chatbot. The Smalltalk Prebuilt is certainly one of the most frequently used prebuilts. This also makes a lot of sense, because if you look at a typical communication of a user with a chatbot, especially at the beginning some smalltalk questions like "How are you?", "How old are you?" are asked. However, there are also other pre-builts, such as weather, news or restaurant bookings. Here you have to pay attention to the languages in which these prebuilts are available. All in all, prebuilts can provide start-up help in some areas and support the development of a chatbot.
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Chatbot
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A chatbot is a computer program designed to conduct conversations with people via digital channels and automatically answer inquiries. The term is a combination of the English word “chat” for conversation and “bot,” short for “robot.” Chatbots are integrated into websites, messaging services, or other digital interfaces and often process user input using artificial intelligence.
Differences from conventional bots
In computer science, the term "bot" generally refers to a program that runs autonomously, performing its tasks without human intervention and requiring only a single initial setup. Bots can serve various purposes, including malicious ones, such as in botnets, which enable coordinated denial-of-service attacks.
A chatbot is a specialized form of this concept. It is explicitly designed for communication with humans and is geared toward processing conversations in natural language and providing meaningful responses. The key difference lies in its ability to interact: While general-purpose bots perform tasks in the background, a chatbot is designed for direct interaction with users.
The Role of Artificial Intelligence
The processing of natural language input is only possible through the use of artificial intelligence. Human language does not follow rigid rules and cannot be processed directly using traditional programming logic. Instead, Natural Language Processing , or NLP for short. In this process, a user’s input is interpreted by so-called large language models to identify the underlying intent.
Once an intent is recognized, it is converted into an action. This action can be a direct response to a question or the execution of a process, such as retrieving information from a connected system. For a chatbot to provide meaningful responses, the NLP component must be backed by a structured knowledge base containing answers and defined actions.
Applications and Channel Integration
Chatbots are used across a wide range of text-based channels. In customer service, they are often set up as web chats on a company’s website or integrated into social media platforms such as Facebook Messenger or Instagram. They can also be integrated into HR workflows via Slack or Microsoft Teams.
Various platforms are available for developing and operating chatbots. Providers such as Google (with Dialogflow), IBM (with Watson), and Microsoft (with LUIS) offer their systems as publicly accessible web services. These platforms provide access to sophisticated machine learning systemswithout the need to build your own infrastructure.
BOTfriends offers a high-performance enterprise platform that allows you to build intelligent chatbots in-house and manage them centrally across all channels. Thanks to its intuitive no-code architecture, you can automate complex customer interactions all the way to the backend without relying on expensive external integrators.
A chatbot processes text-based inputs and provides text-based responses. A voicebot, on the other hand, is designed for spoken language and also uses speech recognition and speech synthesis technologies. In practice, both types can be based on the same conversational logic and differ only in their input and output modalities. BOTfriends X supports both variants within a single platform.
Not necessarily. There are rule-based chatbots that rely on fixed decision trees and predefined response paths without using AI components. These are suitable for simple, clearly structured use cases. However, as soon as natural language inputs in variable forms need to be processed, the use of NLP—and thus AI methods—is necessary to ensure reliable recognition of user intent.
Chatbots are used in a variety of fields, such as customer service to answer frequently asked questions, internal IT support, recruitment, and e-commerce. They are deployed in situations where there is a high volume of similar inquiries and automated processing is beneficial. They are integrated through channels such as web chat, WhatsApp, Microsoft Teams, or other messaging platforms.
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Conversational office
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A conversational office, also known as a conversational workplace, refers to a business environment in which a large portion of internal communication and service processes is handled through conversational systems. Instead of navigating through intranet menus, self-service portals, or ticket forms, employees speak or write directly to an AI agentwho understands their requests, retrieves data from the appropriate backend systems, and initiates the necessary actions.
This creates a new communication standard for companies that brings together HR, IT support, and facility management on a unified conversational platform.
What Makes a Conversational Office
A conversational office is more than just a single FAQ bot on the intranet. It combines multiple AI agents, each of which covers a specific area of expertise—such as human resources, IT services, travel management, or office logistics. Through multi-agent orchestration, these agents access shared identities, permissions, and knowledge sources, and seamlessly forward inquiries to the appropriate specialist agent.
Technically, a Conversational Office is based on a unified conversational AI platform that integrates voice, chat, and email under a single logic. Knowledge AI ensures that internal policies, manuals, and tool documentation are accessible without employees needing to know which system contains the answer.
Typical areas of application within a company
The applications range from recruiting to office management. A Conversational Office brings these use cases together into a single, unified conversational experience instead of spreading them across ten different tools.
- HR and Recruiting: Job searches, application status, vacation requests, pay stubs, and onboarding steps are handled through the dialog.
- IT Support: Password resets, device orders, software approvals, and trouble tickets are handled via AI workflows and escalated as needed.
- Office services: Room reservations, cafeteria menus, shuttle schedules, visitor registration, and taxi orders can all be handled via self-service.
- Knowledge Access: Employees ask about policies, processes, or contract terms and receive answers backed by reliable sources.
Implications for Voice and Chat
In the Conversational Office, the voice channel is playing an increasingly important role. An internal service hotline that used to be handled by a traditional IVR system can now be managed by a phonebot that immediately understands the issue and directs the caller to the right place without a menu of button presses. Traditional IVR is a body without a brain. AI-native voice with multi-agent orchestration, on the other hand, combines natural language understanding with actual task processing. An employee who needs to reset their password while on the go can do so over the phone instead of in a browser.
In a chat, such as via Microsoft Teams, Slack, or an internal web widget. Does the voice experience complement intent-based dialogues featuring attachments, buttons, and structured responses. Email inquiries sent to service inboxes can also be automatically classified and answered by the same AI agent, ensuring a consistent knowledge base across all channels.
Conversational Office in Multi-Agent Setups
A productive Conversational Office rarely consists of a single all-around agent. Hybrid intelligence—combining rule-based process logic and generative models—forms the brain, while individual specialized agents act as the body for HR, IT, or facilities. An orchestration layer determines which agent handles a request, when a human employee takes over, and what information flows between the systems. This allows the Conversational Office to scale with the company’s structures, rather than managing each department in an isolated tool.
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Conversational testing
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Conversational testing refers to the systematic testing of the processes defined in the conversation flow for an AI agent for naturalness and comprehensibility before they go into production.
The goal is to identify early on whether the wording sounds natural, whether the dialogue achieves its objective, and whether the desired tone is maintained. This process is a central component of conversational design and supplements automated testing methods with human evaluation. In this way, conversational testing combines traditional quality assurance with the hybrid intelligence requirements of modern conversational AI.
What Conversational Testing Reveals
Conversational testing reveals areas where the AI agent's conversational capabilities still need improvement. These include:
- Convoluted or tediously long sentences that don't work in voice chat.
- Missing or unclear follow-up questions when the intent is ambiguous.
- Shifts in tone between formal and informal passages.
- Gaps in the conversation flow where users cannot identify a logical next step.
- Answers that are factually correct but miss the point.
Implications for Voice and Chat
In the voice channel, for example, with a voicebot in hotline triage, conversational testing is particularly valuable. Spoken language does not tolerate convoluted constructions, and users expect short, clear responses.
In the context of chat and email, the focus shifts to readability, tone, and striking the right balance between precision and empathy. Here, too, testing reveals whether responses are perceived as helpful or whether users need to ask follow-up questions to clarify the issue.
Conversational Testing in Multi-Agent Setups
In complex scenarios, multiple specialized AI agents work together—for example, for authentication, case handling, and escalation. Conversational testing becomes particularly relevant here at the handoff stage, because gaps in communication between agents can quickly lead to repetitions or lost contextual information. In conjunction with Knowledge AI and defined AI workflows, this approach helps identify process boundaries and clearly delineate the areas of responsibility for each agent.
For effective implementation, an iterative approach is recommended: the results of testing are incorporated into revised training phrases, adjusted fallback paths, and refined workflow steps. This leads to continuous improvement, making conversational AI significantly more robust over time.
Frequently Asked Questions (FAQ)
It makes sense to use this approach once a conversation flow has been roughly established and the key responses have been formulated. In practice, testing takes place between the conversational copywriting phase and technical implementation in order to identify weaknesses early on. However, it can also be repeated later for new use cases or during major revisions to the dialogue.
Automated tests primarily assess the NLU model’s recognition performance and the technical stability of workflows. Conversational testing supplements this by incorporating human evaluation of tone, conversational flow, and perceived helpfulness. Both approaches are complementary and should be used together in professional conversational AI development.
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Insult Rate
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The insult rate is a metric from chatbot or voicebot analytics that indicates how many conversations users insult or abuse the bot in. It is calculated as the ratio of conversations with insults to the total number of interactions. Insults often occur when the AI agent does not provide a suitable response, causes misunderstandings, or reaches its technical limits. Sometimes insults are made for no reason, for example by so-called trolls who take advantage of the anonymity of the internet. The insult rate is therefore an indicator of frustration, unfulfilled expectations, or poor user experience.
Why is the insult rate important?
The insult rate is a key metric for evaluating efficiency and user acceptance. A high rate often signals technical or content-related deficiencies: incorrect responses, poor issue recognition, or inadequate dialogue management. However, it can also depend on the target group or use case. In sensitive areas or among younger users, the inhibition threshold for insults often increases. The insult rate is valuable for companies because it identifies specific areas for optimization: Which dialogs cause frustration? Where are answers missing? Systematic evaluation of this KPI helps to continuously improve the performance of chatbots or voicebots and increase customer satisfaction.
Insult rate in practice
In practice, the insult rate is used to identify specific weaknesses in chatbot dialogues. Example: A customer service bot uses offensive language in 15% of all conversations. Analysis shows that users react with frustration, especially when asked about delivery times. The Chatbot does not provide clear answers here. After optimizing the intents and responses, the rate drops to 5%.
BOTfriends relies on data-driven chatbot optimization: dialogues are continuously improved by evaluating the insult rate and specifically training the AI models. This creates a positive user experience that minimizes insults and sustainably increases acceptance of the chatbot.
Frequently Asked Questions (FAQ)
The insult rate is calculated as a percentage: the number of conversations containing insults divided by the total number of conversations, multiplied by 100. Example: 3 insults in 30 chats result in an insult rate of 10%. Detection is usually carried out using keyword analysis or natural language processing (NLP).
A high insult rate indicates problems: poor response quality, missing intents, technical errors, or a frustrating user experience. It is a warning sign that the chatbot or voicebot needs to be revised. BOTfriends systematically analyzes such cases in order to implement targeted improvements.
Yes, the insult rate depends heavily on the target group and use case. In sensitive areas such as health or finance, it is often lower, while it is higher among younger target groups or in informal contexts. The time of day and anonymity also have a significant influence on user behavior.
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Quick Reply / Chips / Chips
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Quick Replies are a form of interaction in messaging platforms and appear as buttons under a message. Unlike cards, Quick Replies disappear after they have been pressed. Quick replies allow users to click through content in the chatbot instead of typing their request in the input field. If, for example, the chatbot offers a button with the name "Help", it is no different in the end than if the user types in "Help" and sends it. The communication is thus accelerated and the users get an orientation about the topics that the chatbot offers.
Depending on the output channel or messaging platform, different names are used for Quick Replies.
| Messaging platform | Designation |
| Facebook Messenger | Quick Reply, Quick Reply |
| Google Assistant | Suggestion Chips |
| Microsoft Bot Framework | Suggested Actions |
| slack | Slack interactive buttons |
| Skype | HeroCard |
| telegram | Keyboard Buttons |
| viber | keyboards |
Here is an example picture for Quick Replies in Facebook Messenger:
Application in Dialogflow
If you create or edit intents in Dialogflow, you will find several options for replying as a chatbot under the heading Response. Among other things, you have the option of selecting the Quick Reply format and naming the buttons in the form of a list.
A Quick Reply Response is displayed in the messaging platforms as a predefined User Response, which is sent back to Dialogflow by clicking on it.
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