Peruse our chatbot resources for getting started materials, research data, white papers, datasheets, best practices and more.



If you are new to conversational chatbots, the following resources should help you get off the ground. Also, check out our FAQs!

Chatbot Design Template

Don’t start from scratch if you don’t have to! This CUI (Conversational UI) Design template can be used as the foundation for your own chatbot project. It shows how a design of a bot can look like, and takes care of standard bot behavior such as responding to greetings or requests for an agent, so you don’t have to re-invent the wheel! Contact us if you’d like to use the original PPT resource.

10 Steps to Build a Customer Service Chatbot – White Paper

Feel overwhelmed? If the world of customer service chatbots is new to you, here are some guidelines on how to go about building one, from setting the right goals, creating the conversational architecture, designing the UX, to implementation, testing, and tuning. Our team of experts can help you with any and all of these steps for your own chatbot project needs.

10 Steps to Build a Customer Service Chatbot – Presentation

This presentation complements the above white paper with visuals and examples that bring the 10 different steps to life.


How we built a Facebook Messenger Chatbot for a Car Maker

Facebook Messenger is a great place for businesses to offer customer service. This paper describes how we designed and implemented a chatbot on Messenger to help new and existing customers alike learn more about alternative drives and the advanced online features of a new car.

How We Built the Wine Bot Margot for Lidl

We recently announced a partnership with the UK’s fastest growing retailer, Lidl UK, to deliver a fully automated, AI-powered, Facebook Messenger chatbot, which will help UK customers easily select the best wine for their meal or moment. Lidl’s new chatbot – named Margot – will become Lidl shoppers' virtual wine consultant at the tap of a screen. Read about how Margot came to life and what people are saying about her.


Knowledge Engineering for Chatbots, Voicebots, and AI-Enabled Enterprise Search

With the increased interest in AI to power not only customer self-service but also enterprise search, we need to acknowledge that technology alone is not enough. Misleading marketing claims by the likes of IBM Watson want people to believe that knowledge can now essentially sort itself out, creating frustration when actual systems go live and underperform. This is where Knowledge Engineering comes in, because without a robust information architecture in place (rather than numerous disconnected systems that might even contradict themselves or are simply impossible to manage), any implementation of a chatbot or similar solution is just a band-aid over a broken system.

Aspect Agent Experience Survey

Aspect’s new Agent Experience Survey reveals the attitudes and opportunities customer service agents see as chatbots move into the contact center. By handling more of the complex questions that chatbots don’t, customer service agents see a lot of potential to improve their skills and provide greater value to the company.

What's So Artificial About Artificial Intelligence?

There exists a great deal of misinformation and confusion regarding true artificial intelligence, and terms such as "machine learning" and "natural language understanding" are often included in AI announcements as if they are the same thing. In this podcast, Paul Stockford, Chief Analyst at Saddletree Research, has a frank discussion with Aspect's Tobias Goebel regarding the differences between AI and natural language understanding (NLU) and rules-based systems versus human-level performance. This podcast offers an honest look at the state of AI in the contact center industry today.

Customer Service Chatbots and Natural Language

What’s involved in teaching a computer how to deal with natural language? This white paper introduces relevant concepts and discusses different technical approaches that are available.

Customer Self-Service on Facebook Messenger Data Sheet

Customers are active on Facebook Messenger – the platform averages over 800 million monthly active users. Engage them on this familiar channel for customer acquisition, service, and support. Aspect’s unique approach, backed by Artificial Intelligence (AI) and Natural Language Understanding (NLU), routes your customers’ questions to answers, no matter whether that answer is in your knowledgebase, your CRM, on the Web, or needs the expertise of one of your agents.

Interactive Text Response Solution Spotlight

Text is conversational, convenient, familiar and fast. It includes mature forms like SMS and social media channels, as well as newer messaging applications like WhatsApp. In a recent consumer survey, over 70% of people told us they want the ability to solve product and service issues by themselves using their preferred communication method. Therefore, text-based self-service is the foundation of a re-imagined customer service strategy. And the foundational technology to implement that strategy is Aspect® CXP which leverages automation and natural language understanding and unlocks the power of text for your brand.

Learning about Learning and the Natural vs. Artificial Intelligence Debate

Recently, the Radisson Blu Edwardian hotel chain in London launched Edward, a new chatbot in the messaging channel that provides guests at the chain with a virtual host who can handle routine questions and route requests for services to appropriate staff departments while freeing up the front desk staff for interactions that more directly require a human touch.

How AI-Driven Search Could Bring Us Closer to the Intelligent Workplace

AI-enabled search promises to transform the way people interact with information and digital assets, driving new efficiencies and creating value from information that has been all but lost in the “digital junk drawers” that are our corporate information management systems. For AI to deliver on its promise, these junk drawers need preliminary organizing structures before the vision of conversational interactions can be realized. In other words, you can’t have AI (artificial intelligence) without IA (Information Architecture).