Sitefinity Smart Search

Project Overview

The Sitefinity Smart Search solution is an AI-powered search assistant designed to provide natural language responses to user queries based on the official Sitefinity documentation. The goal is to enable users to find relevant answers quickly without manually navigating through extensive documentation.

This solution is built to enhance the efficiency of developers, content managers, and administrators working with Sitefinity CMS by delivering precise, context-aware responses while ensuring security, neutrality, and reliability.

Business Objectives

The primary business goals of the Sitefinity Smart Search Solution include:

• Improved Productivity: Reduce the time users spend searching for answers, leading to faster troubleshooting and implementation.
• Enhanced User Experience: Provide intuitive, conversational interactions, making the documentation more accessible.
• Support Scalability: Enable businesses using Sitefinity CMS to train their teams (and their customers) efficiently by offering a centralized knowledge hub.
• Reduce Support Costs: Minimize reliance on customer support teams by resolving common issues through automated search.

Data-Driven Insights: Analyze frequently asked questions to identify areas for documentation improvement.

Functional Scope

The Sitefinity Smart Search Solution is designed to:

In-Scope Functionalities

• Contextual Search: Users can ask questions in natural language, and the system will retrieve precise answers from the Sitefinity CMS documentation.
• Error Troubleshooting: The system helps users debug and fix common issues (e.g., database connection errors, widget rendering issues, API authentication failures).
• Step-by-Step Guidance: Provides structured responses for tasks such as installation, configuration, deployment, and performance optimization.
• API & Integration Support: Offers guidance on using Sitefinity CMS APIs, third-party integrations, and custom module development.
• Best Practices & Security Recommendations: Delivers insights on security, performance, and SEO optimization for Sitefinity-based websites.
• Multi-Tier User Support: Answers technical queries for developers, content managers, and business administrators
• Intent Recognition: The solution understands users’ intents and responds accordingly, ensuring contextually relevant answers.

Out of Scope Functionalities

• The system does not provide real-time interaction with Sitefinity CMS (e.g., executing commands, modifying configurations directly).
• Security-sensitive queries such as “provide admin credentials” or “disable security features” are strictly prohibited.
• Biased or sensitive content related to race, gender, religion, or politically motivated queries is filtered out to ensure ethical AI practices.

UI/UX Overview

We have developed a range of mockups for Sitefinity Smart Search based on the Sitefinity documentation interface. However, it is to be noted that the look of such solutions can be developed according to the customer’s needs.

Picture 1. Sitefinity Smart Search button

Picture 2. Sitefinity Smart Search chatbot

Picture 3. Chatbot dialog example and answer rate options

Technical Overview

Sitefinity Smart Search is an application created using the RAG approach, with an Azure Function to host a serverless API and UI interfaces, allowing communication with the application. 

• We use a PostgreSQL database with PGVector extension to store data from documents as vectorized chunks.
• Data is loaded using the data loader functionality, which supports multiple file types and various chunking approaches. In this case, we successfully converted HTML pages to Markdown and uploaded them using Markdown chunking. Markdown chunking splits the content of a page by headings, unlike the text chunking method, used for PDF and text type of documents, which splits documents based on length. 
• Vectorization is done with the help of LangChain and the OpenAI embedding model. To retrieve data from our vector store, we use LangChain retriever functions.
• An important feature of the Sitefinity Smart Search application is the LLM agent, created with LangGraph and LangChain. With custom prompts and tools, the agent can perform reasoning, decide which retrieval tool to invoke at the right time, and fetch relevant documents. After retrieving the documents, we use a custom prompt to generate an answer for the user, providing both the question and relevant document chunks to the LLM. 
• The LLM model is GPT-4o, hosted on Azure, along with the embedding model.

Next Steps

To move forward with the Sitefinity Smart Search, the following steps can be considered:

1. Prototype Development: Build an initial AI-powered search module.
2. User Testing & Feedback: Validate responses with real-world Sitefinity users.
3. Security & Compliance Review: Ensure adherence to data security best practices.
4. Integration & Deployment: Deploy in an enterprise 
5. Continuous Improvement: Possibility to offer similar solutions to Sitefinity’s clients 

November 2025