AI Solutions
AI Website Assistant for Customer Questions
A Retrieval-Augmented Generation (RAG) assistant that retrieves relevant website content, generates an answer using an LLM, and provides citations so users can check the source.
NORTHSTAR AUDIO
Find the right audio gear
Ask Northstar
Help based on product and support pages
Customer question
My EchoSound Pro headphones stopped charging. How do I get help?
Start with the troubleshooting steps in our support guide. If the issue continues, the warranty policy explains how to request further assistance.
Troubleshooting & Warranty Guidenorthstaraudio.example / support / warrantyFictional retailer and product details; source link shown for illustration only.
Customers and internal teams often have to search through several website pages to find an answer. Product details, help articles, and support information are spread across the site, so finding the right answer can take time and lead to more support requests.
We designed an AI assistant that lets visitors ask questions in their own words. It searches the website for relevant information and uses it to provide a clear answer, with links to the original content so visitors can check the details. This makes information easier to find without changing the website’s existing content.
The assistant adds a natural-language question-and-answer layer over indexed website content, with source-backed answers and a repeatable evaluation workflow. It is designed as a modular web application that can be adapted to different websites and business needs.
From website content to source-backed answers
A high-level view of how a visitor’s question becomes an answer they can verify.
Website Content
Product pages, FAQs, support articles, and policies.
Content Indexing
Organize website information so relevant content can be found.
Hybrid Search
Find relevant passages by meaning and keywords.
AI-Generated Answer with Sources
Give a clear response with links to the original content.
Processes source content provided through llms.txt or configured website pages and prepares it for indexing.
Vector search and BM25 keyword retrieval help find conceptually relevant passages as well as exact terms, names, or technical identifiers.
The LLM uses retrieved content as context and provides source references so users can inspect the underlying information.
Response guardrails support answer quality, while Ragas provides an LLM-as-a-Judge evaluation workflow for selected RAG metrics.
Make it easier for customers to find accurate information, get timely answers to routine questions, and move through the customer journey with less friction.
Faster access to information: Customers can ask questions in natural language rather than searching through multiple website pages.
More responsive customer support: Routine questions can receive immediate answers, helping customers move forward without waiting for a support representative.
A smoother customer experience: Clear, source-backed responses make information easier to understand and give customers a way to verify answers.
Less repetitive support work: Self-service answers can help reduce the volume of routine questions handled manually, leaving support teams more time for complex enquiries.
This approach can help people find trusted answers across customer-facing websites, technical documentation, internal knowledge, and approved sales material.
answer common questions about products, services, policies, and processes.
help customers and developers find information in guides, API references, setup instructions, and troubleshooting steps.
help employees find information in approved policies, operating procedures, and internal guidance.
answer common questions about offerings, features, requirements, and implementation approaches using approved source material.
Explore other projects with related business or technical patterns.
Tell us about the process, customer experience, or research challenge you want to improve. We can discuss an appropriate approach and practical next steps.