Retrieval-Augmented Generation (RAG) systems and large language model (LLM)-powered chatbots have significantly advanced conversational AI by combining generative capabilities with external knowledge retrieval. Despite their success, enterprise-scale deployments face critical challenges, including diverse user queries, high latency, hallucinations, and difficulty integrating frequently updated domain-specific knowledge. This paper introduces a novel hybrid framework that integrates RAG with intent-based canned responses, leveraging predefined high-confidence responses for efficiency while dynamically routing complex or ambiguous queries to the RAG pipeline. Our framework employs a dialogue context manager to ensure coherence in multi-turn interactions and incorporates a feedback loop to refine intents, dynamically adjust confidence thresholds, and expand response coverage over time. Experimental results demonstrate that the proposed framework achieves a balance of high accuracy (95\%) and low latency (180ms), outperforming RAG and intent-based systems across diverse query types, positioning it as a scalable and adaptive solution for enterprise conversational AI applications.

In this paper we present the results of our experiments in training and deploying a self-supervised retrieval-based chatbot trained with contrastive learning for assisting customer support agents. In contrast to most existing research papers in this area where the focus is on solving just one component of a deployable chatbot, we present an end-to-end set of solutions to take the reader from an unlabelled chatlogs to a deployed chatbot. This set of solutions includes creating a self-supervised dataset and a weakly labelled dataset from chatlogs, as well as a systematic approach to selecting a fixed list of canned responses. We present a hierarchical-based RNN architecture for the response selection model, chosen for its ability to cache intermediate utterance embeddings, which helped to meet deployment inference speed requirements. We compare the performance of this architecture across 3 different learning objectives: self-supervised contrastive learning, binary classification, and multi-class classification. We find that using a self-supervised contrastive learning model outperforms training the binary and multi-class classification models on a weakly labelled dataset. Our results validate that the self-supervised contrastive learning approach can be effectively used for a real-world chatbot scenario.

Your time is invaluable and it is truly the only non-renewable resource. Once that minute, that hour, or that day is over, it's gone forever. In this way, the objective isn't really about finding more time to do the things that need to be done. Rather, it's about figuring out how best to utilize your time and work in the most effective and most efficient way possible. When you save time, you boost your productivity.

There's been an explosion of chatbots on Facebook messenger over the last 12 months, but the majority of these rely on scripted conversations rather than Natural Language Processing. This means they answer your question with a canned response, or guide you through a pre-set decision tree, rather than genuinely understanding what you say, or responding with true'intelligence'. On the day, the team behind Just Eat's popular Facebook bot explained how it was built. The process began, as all good processes do, with a spreadsheet. This listed out every plausible question a user might ask, remark they might make, or sweary insult they might type in shouty capital letters. The team then wrote a canned response for each.

A couple years ago, Google came up with an idea for eliminating the pesky task of writing emails. Instead of asking people to type sentences into a blank box like they've always done, the company started providing automatic replies that could be sent with a tap of a button. The feature, called Smart Reply, relied on Google's machine learning brains to generate quippy responses based on the content of the received email. A note from a friend suggesting a dinner plan might be met with a "Sounds great!" or "Looking forward to it!" Meanwhile, a question from a colleague about an upcoming work call might surface suggestions like "I'll call you in a few" or "Are you available now?" Google's AI-assisted Smart Reply feature will soon be available to all Gmail users on mobile.

Has digital customer service gone a step too far? We haven't mastered Artificial Intelligence, yet for some reason, it seems like robots and automation are primed to be the next trends for customer service. Automation can save you money in the short run, compared to traditional methods (like human power), because of its increased efficiency. But not all customers are happy with all automated, all the time. While automation and digital customer service are tools you should be using (they save resources and allow you to serve customers faster), making sure your customer service strategy still includes a human element is massively important.

AI and natural language understanding (NLU) is evolving quickly and made quite some progress over the last couple of years. Still, it seems that fully automated support via chatbots is not there yet. Therefore, a human intersection is necessary for every successful customer care bot. There are many different ways humans and bots can work together to increase the performance of every customer service team. Here are four examples of how human operators can successfully work together with bots in a clear and more-or-less seamless way.

As I drift through the lobby of the Nautilus, a derelict spaceship I've found myself stranded on, the shipboard AI starts playing music. I see a terminal across from me in the room. I go to it, and Event[0], out now for PC, starts revealing itself to me. Developed by an independent team called Ocelot Society, Event[0] wants you to talk to it. The terminal has a real-time typing interface and a conversation partner in the form of that shipboard AI.

Artificial Intelligence (or AI for short) has become quite the buzzword recently. Everyone in the tech world seems to want to use it. A ton of different applications cropped up in the last couple of weeks, every more amazing than the last. We've seen AI that can tell us what it sees on an image, one that can suggest an answer based on an email contents and one that could tell us the weather if we ask the right questions. We wanted to check if there is any way we could use artificial intelligence in LiveChat to improve it in any way.