Getting an intelligent AI Chatbot for information retrieval

Many software companies and enterprises are adopting modern-day chatbot solutions to help external visitors and internal knowledge users gain quicker, more effective access to information. These solutions are called RAG chatbots and help to Retrieve information, Augment it with AI knowledge, and then Generate a better response. A regular chatbot differs from a RAG chatbot in that the latter is based on up-to-date knowledge from PDFs, databases, or websites, making it essential for accurate enterprise AI.

 

 

RAG vs standard chatbot

This tool is an AI framework that improves the accuracy of Large Language Models (LLMs) by fetching trusted external data before generating a response. Instead of relying only on static training data, RAG retrieves current, specific information (e.g., from databases or company documents) to provide more accurate, relevant, and context-aware answers, significantly reducing AI hallucinations. AI hallucinations are confident, false, or nonsensical outputs generated by AI models when they fail to process information correctly, often treating data gaps as opportunities to create convincing fiction. Therefore,  RAGs are best used for complex Q&A, internal policy searches, or technical support, whereas a standard chatbot is best for basic, rigid FAQs.

 

Quick access to improved information

A RAG solution provides quick access to information by having AI search all databases and documents, augment them, and deliver the requested information. It saves your knowledge workers valuable time searching for the needed data, letting them focus on using the outcome. The same applies if companies use RAG solutions for external websites. Providing your visitors with quick, high-quality information helps you retain your customers or your employees.

 

From finding information to applying knowledge

If desired, the RAG systems can apply source citations, giving users transparency into the information the systems used to find an answer. The RAG solution will provide up-to-date information as LLMs have “cut-off dates” for their knowledge; RAG allows them to access the latest news or live data. It also deals with information hallucinations. These RAG systems are significantly cheaper and faster to update a search database than to “retrain” or “fine-tune” a massive AI model.

 

 

Yonder’s method for RAG solutions

We have created RAG solutions for many of our clients and developed an efficient 2-step method to quickly release v1 and then update the solution to v2. For the first version, which usually takes up to 1.5 months, we prioritize rapid deployment and operability over initial accuracy measurements. The first version is a functional chatbot that is onboarded for quick adoption. Then comes version 2, which takes approximately 2 months. Here, we focus on enhancements, adding more information sources to improve the chatbot’s accuracy and knowledge base.

 

Key results

• Provide quick, intelligent information to your users, whether they are external clients or employees.
• Go from expensive and extensive AI searches to a dedicated app that is trained to search the right data sets and that passes all security checks, avoiding AI hallucinations.
• Reduce repetitive work and long searches, and have actionable information readily available.
• An operational chatbot within 1 to 1,5 months.
• Continuous improvements of the v1 version in 2 months after the first release.

 

Examples of RAG chatbots

For a Canadian software company active in the Oil & Gas industry, we built a secure, cloud‑hosted RAG chatbot that lets the client’s employees query the company knowledge base (PDF manuals, FAQs, training docs) conversationally. The solution used Azure AI Search and Azure AI Foundry, delivering a working V1 in 1.5 months, prioritizing rapid deployment and operability over initial accuracy measurement.

For a multi-vertical software company, we implemented a phased program to design and pilot AI Copilot RAG agents for the client. This includes a Support Assist agent that provides reliable answers from product documentation and ticket data. And the plan for the next phase is a Consultant and SOW/WBS Assist agent for templated sales and delivery tasks. The solution leveraged Microsoft-based integrations, including SharePoint, Autotask, and Copilot Studio, with robust guardrails, human-in-the-loop validation, and enterprise-grade security.

We built a multi-tenant Retrieval Augmented Generation (RAG) chatbot PoC that scrapes site content, ingests controlled documents, and returns grounded, site-specific answers via a web/chat API to online web visitors. We delivered a clickable prototype in month 1 and a pilot-ready first version in 1.5 months with planned improvements to accuracy, evaluation, and ops.

 

By Gabriel Lucaciu,
Delivery Manager and AI Transformation Enthusiast

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