Every website in the future will have an AI assistant. This assistant will represent a brand’s voice and serve as a digital guide for their customers and prospects. Got a question? The assistant will answer it. Need help completing a task? The assistant can help with that too. Looking for a product but there’s too many options to choose? It will recommend products based on what you’re looking for, and prompt you to buy.
Websites have tried to solve this in the past by going live with search. But when a visitor asks a question, they’re presented with a long list of links that they have to sort through manually. Some brands introduced chatbots to improve this experience, but many of those couldn’t handle complex questions. Others offered no assistance at all, leaving users on their own trying to find what they’re looking for. If your site navigation isn’t perfect, your site visitors may give up and turn to a competitor.
Website CMS and DXP vendors often talk about personalization, but we know it’s limited. Personalization usually means A/B or multivariate testing, or tailoring content based on broad personas. But creating ten personas still doesn’t account for the context of each individual visitor, who have their own preferences, purchase history, etc.
To transform this experience, your website needs an assistant. You can call it an AI assistant or a digital assistant, but to make it work, it must be powered by two key frameworks: RAG and ReAct.
Depending on your industry, this assistant could take many forms. In education, it might act as a digital admissions rep or student advisor. For travel and hospitality, it could serve as a concierge. For government, it might guide citizens through services, applications, or benefits. The list goes on. But what makes these assistants intelligent are the frameworks working behind the scenes.
What is RAG?
RAG stands for retrieval-augmented generation. RAG is able to answer questions based on your organization’s own content, such as website pages or PDF documents that’s stored and processed in a vector database. RAG retrieves the relevant content and uses an LLM (large language model) to generate coherent and contextually accurate responses to a user’s query using your organization’s content rather than using the knowledge of the LLM. That way, instead of a list of links in response to a search query or a chatbot that responds with “I don’t know,” the AI assistant will actually give the user an answer to their query.
Here’s how it works: Your content is uploaded into a vector database via a CMS connector (WordPress, Magnolia, Contentstack, RWS Tridion, Progress Sitefinity, Optimizely, Sitecore, etc.) or through a website scraper. The content is then transformed into embeddings – numerical representations that encode the meaning of words, images, and other data. These embeddings help the AI assistant understand the context of a user’s query and find matching content.
When someone asks a question, the AI assistant converts the query into embeddings and retrieves the most relevant matches. It may scan dozens of documents and re-rank them, ultimately selecting the best few to use as source material for its response. The generative model then formulates an answer using this content. That’s retrieval-augmented generation in action.
What is ReAct?
ReAct stands for Reasoning and Acting. It enables the AI assistant to take multi-step actions and access real-time data using agents.
Instead of just answering a question based on stored content, the AI assistant reasons through the request and calls upon agents to complete tasks. These agents can pull in live data from systems like Google Maps, CRMs or CDPs, inventory platforms, appointment scheduling tools, and more.
For example, imagine a hotel guest asks, “Can you book surfing lessons for me on a day with nice weather this week?” The assistant uses a weather agent to find ideal days and a scheduling agent to check lesson availability. It then replies with the best options and books the session. The same assistant could also make restaurant reservations or process a late checkout.
ReAct enables the assistant to plan its response step-by-step: gathering information through RAG, pulling real-time data through agents, and reasoning through the best course of action, all to deliver a complete, personalized response.
An assistant powered by RAG and ReAct can:
- Check order status using an order management system agent
- Confirm appointment times using a scheduling tool
- Guide users through form-fills
- Recommend products based on live inventory
- Display CTA buttons like “Buy,” “Donate,” or “Sign Up” to drive conversions
An AI assistant that uses both RAG and ReAct frameworks delivers a far superior experience than your traditional search and chatbot experience. It retrieves trusted content, reasons through user needs, pulls in real-time data, and encourages users to take action.
Instead of making users dig for answers or navigate a complicated site, your assistant guides your customers and prospects to what they need and moves them forward in their journey. Whether that’s buying a product, booking a trip, renewing a membership, or donating to a cause, it all happens faster and easier.
Your customers are now able to complete tasks through AI assistants in ways that were not previously possible with search or chatbots. This will be the future of a brand’s digital interactions with their customers ... you just need the right AI frameworks in place and the features that bring them to life.
If you’re interested in how your company can go live with an AI assistant, or have questions about AI in general, schedule a strategy call or book a demo to see how your team can put this technology to work.