AI Сhatbot Development

Development timeframes are based on the workload, the availability of data, and the complexity of the integration system. A chatbot that answers questions from a set of preselected documents in a single channel takes much less time to develop than a chatbot that processes authenticated requests across multiple systems. Enterprise AI chatbot development service with multiple departments, multiple user roles, and on-premises deployment would take longer because access rights and testing would require more attention.  For your information: it took us 2–4 weeks to develop our AI Knowledge Agent project; however, this applies only to that project, not to all other projects. We recommend starting with one use case first.

Yes, we design chatbots that are built with the help of GPT and other big language models offered by OpenAI, Google Cloud, Microsoft Azure, and others, as well as open-source models. Depending on your business case, response quality, speed, cost, and computing power, we choose a suitable model for you. Customer service chatbots and employee assistance tools that handle confidential data may require different implementation and hosting patterns. As a vendor of AI-based chatbots for enterprises, we can also deploy our chatbot on your infrastructure if you prefer to retain control of your data. In most cases, no extra setup is needed, as training the model on your content includes all corporate-specific knowledge.

The AI-enabled chatbot cannot ensure 100% accuracy in every situation; therefore, we design it to be reliable but not perfect. The chatbot answers based on information from approved sources and cites them accordingly; it also informs users when the sources don’t have an answer to their questions. Before going live, we test our chatbot on dialogues that include ambiguous questions, off-topic queries, and even questions related to data that is restricted from access. For our AI-enabled chatbot for banks, we restrict the responses to approved product information and direct all other requests to the workflow or the corresponding representative.

The chatbot integrates with your data to provide relevant responses for your company. To do this, we use a technology called “Retrieval-Augmented Generation” (RAG). This means the chatbot extracts relevant information from your documentation, help center, policies, and knowledge base before answering any question on your behalf. If the source information is updated, the chatbot will start using the new information without retraining the model. For instance, an AI e-commerce chatbot can answer questions based on your merchandise catalog, return policy, and order statuses (if this information is integrated). Access restrictions ensure that no unnecessary information is provided to the user.

You can implement an AI chatbot in most systems that support templates, including CRM systems like HubSpot and Pipedrive, product catalogs, order management systems, and identity providers. It can be run in other applications such as Slack, Telegram, WhatsApp, or even on your website. The chatbot's ability to perform tasks in a particular system depends on the system’s API capabilities, permissions, and request limits. When actions have consequences, users must verify them.

Yes, developing multilingual chatbots is quite possible. Natural language processing can handle different languages, but performance varies by language; therefore, each language must be tested with relevant real-world cases. We also need to consider which language your knowledge base is translated into; if it is available only in English, we can help you decide whether it is reasonable to translate your reference materials or respond in English. In addition, an AI chatbot operating in e-commerce and working with several languages will also need localization of currency and other factors.