The need: product knowledge at the moment a customer asks
The answer to a product question often already exists within a company’s documents. Finding the right file, relevant section and understandable explanation can still take time. We developed an AI assistant automation for Borgmain LLC to make that information more accessible through conversation.
The project for US-based Borgmain LLC used company documents to inform an AI assistant. Within a chat interface, the assistant helped customers with product questions and guided them towards a suitable next step. The focus was company-specific support rather than a general-purpose chatbot.
What document-based AI means
The engineering problem goes beyond sending a question to a language model. Information needs to be prepared, connected to the right product or version and related to the customer’s question. Product descriptions, user documentation and support material provide the context behind natural-language enquiries.
Retrieval-augmented generation, or RAG, is a common architectural approach in this field: relevant information is retrieved and supplied as context for generation. Chunking, metadata, semantic search and embeddings are related components. Fine-tuning changes model weights and is a different method; saying that a system was trained on documents does not distinguish between these approaches. The Borgmain work focused on bringing customer product questions together with company information for support and guidance.
From a chat interface to a support workflow
An AI support assistant needs to interpret a question within the product context. The customer may want to understand a feature, follow a procedure or find the right support channel. Answer generation and guidance design belong together: providing information, asking a clarifying question and handing over to a person are different actions.
The assistant we developed supplied a conversational layer for product questions and customer guidance. Conversation context, product version and document freshness are important design considerations in comparable enterprise AI projects. Unknown information should not be presented as a confident, definitive answer.
Knowledge quality and responsible answer design
The reliability of an AI support assistant depends on the quality of its knowledge base. Conflicting documents, obsolete descriptions or incomplete procedures are not fixed by a better chat interface alone. Document ownership and updating processes need to be considered alongside technical implementation.
Evaluation for this type of project should include realistic questions, questions that should remain unanswered and the risk of incorrect guidance. Access boundaries, personal data minimisation, prompt injection scenarios and human handover are also relevant engineering topics. They help establish a clear scope and a useful relationship between the assistant and human support.
AI automation and AI visibility serve different purposes
This project concerns AI working within a company’s own customer experience. Our AI visibility service addresses discoverability in external search and AI answers. Information quality matters to both, but their users, data sources and success criteria differ. İzlek is a separate product that complements our approach to sources and content management.
Explore our AI and automation expertise if you need a company-document assistant, product support chatbot or customer service automation. In an initial conversation, we can clarify your knowledge sources, support scope and points requiring human intervention.
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