New employees spend time searching for answers about the product, procedures, and internal policies because information is scattered across different sources.
The support team regularly answers standard questions about the product, pricing, and terms that are already covered in the documentation.
Sales managers spend time searching for a case or contract terms and often find an outdated version instead of the current one.
You've tried using general-purpose AI chatbots to answer questions about the product, but they often gave inaccurate information about terms, pricing, or capabilities.
The company has a knowledge base in Notion, Confluence, or Google Drive, but finding the information you need takes up too much work time.
You're considering an AI assistant for customers or your team, but you want to be confident it will provide accurate information about the product.
Unlike general AI models, which lack company-specific information, a RAG assistant generates answers based on the company's knowledge base. The system searches for relevant data fragments and relies only on them, with a reference to the source.
A company's knowledge base changes constantly: new products appear, pricing and procedures get updated. Without regular reindexing and monitoring, answer accuracy gradually declines. That's why a RAG assistant is implemented as a solution with ongoing support, not a one-time project.
Documents are loaded from existing sources: Notion, Confluence, Google Drive, SharePoint, PDF files, Spiro, or any system with an API. They’re then split into meaningful chunks and stored in a vector database in a format suited for semantic search. This layer needs updating whenever the knowledge base undergoes a significant change.
Hybrid search: a combination of semantic search and keyword search (BM25) for maximum recall.
Reranking: selecting the most relevant fragments for a given query from the retrieved results.
Metadata filtering: searching only within the relevant document categories, for example, for a specific role or client segment.
The model generates an answer based solely on the retrieved context fragments, following strict system prompt rules: answer only from the provided documents and state when information is missing rather than make assumptions. Every answer is accompanied by a reference to the document and section where it was found.
Chat UI: a web widget for the website, a mobile component, or an integration with Slack, Teams, or Telegram.
Admin panel: managing documents and sources, manual index updates, query monitoring.
Analytics: most popular queries, answer confidence score distribution, and unanswered queries. These metrics are reviewed regularly as part of post-launch support.
Security: data is not used to train the model (API mode). If needed, the company can use a self-hosted open-source LLM (Llama, Mistral). Role-based access and an audit log.
Situation: a large internal knowledge base in Notion and Google Drive. Keyword search returned irrelevant results, which made onboarding new managers harder.
What we did: an internal bot connected to Notion and Google Drive. Hybrid search covers over 800 documents, which the system relies on for its answers.
Result: onboarding for new managers dropped from 4 weeks to 10 days. Finding the needed information now takes seconds.
Situation: most of the tickets handled by the support team were standard questions about pricing, limits, and procedures. Response time could reach several hours.
What we did: a RAG agent connected to the product documentation and FAQ. It answers within seconds, and when confidence is low, it hands off the query to a specialist along with the conversation context.
Result: about 70% of standard questions are resolved by the assistant without human involvement. Average response time dropped from hours to seconds.
Situation: Google Drive with hundreds of files and no clear structure, so managers spent significant time searching for cases, contract terms, and objection responses.
What we did: an assistant connected to Google Drive, the case library, and reference materials. Fast retrieval of relevant cases, objection responses, and standard contract terms.
Result: meeting preparation time dropped from 1–2 hours to 15 minutes. Analysis of unanswered queries in the first month revealed categories where materials were missing, and they were added.
ChatGPT is one option for a model that generates answers, but RAG covers much more: document processing and indexing, a vector database, hybrid search, reranking, source references, escalation to a human, an admin panel, and analytics. Without this architecture, connecting ChatGPT to documents is a solution that doesn’t scale, doesn’t update automatically, and gives no control over the sources behind an answer.
