Conversational AI

The power of Conversational AI for Hotels

Aplysia OS

The first Guest Communications Operating System

Self-learning NLP

Self-supervised learning for unlabeled data, removing the need for manually labeling

Sentiment Analysis

A technique used to determine whether data is positive, negative or neutral

Syntax & Semantic

To understand the intent that is being requested by the user

Behavioral Analysis

Analyse Big Data to create clusters and segments based on user patterns

Prediction & Forecasting

Use Machine Learning models to predict business variables as sales, risks or trends

Customer Profiling

Build comprehensive profiles of customers using AI techniques

Neural Text-to-Speech

Capacity to reproduce human-like natural prosody and clear articulation of words


Using advanced techniques to process conversations and interpret customer intent

Data Mining

Extracting and collating the most useful elements from the rich data from conversations


The 4 C’s that compose this revolutionary solution

Understanting emotion conversational ai


Understanding emotion

Aplysia is able to address a variety of human language challenges related to Syntax, Semantic Analysis and Emotion. By understanding the emotion associated, conversations are prioritized and automatically escalated to the right department.


Removing friction

Aplysia OS brings together the guest’s favourite communication channels, from social media to messaging apps, and connects them to the different hotel management systems, from booking engines to PMS or CRMs.

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Boost productivity conversational ai


Boosting productivity

Aplysia OS features a powerful Console, accessible via Desktop, browser or the Android or iOS apps, that allows hotels to manage, automate and measure all aspects of their guest communications. It’s one platform, with all the channels, for all teams and all the work.


Unlocking scalability

Aplysia OS gives hoteliers the flexibility of connecting their business anywhere, at any time, avoiding having to purchase expensive systems and equipment. It allows hotels to easily scale operations with total security.

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Elevated guest interactions

Automating over 85% of the guests’ interactions, Aplysia OS is able to handle communications in 99% of the spoken languages in the world.


Doing more, with less

Aplysia OS is the right solution for hoteliers looking for

Guest Satisfaction

Improve customer satisfaction with better (and digitalized) guest experiences.


Increase productivity with powerful automation, including custom escalations.


Generate more revenue with increased direct booking, upgrades and upsells opportunities

Conversational AI for Hospitality: FAQs


Conversational AI refers to the set of technologies that enable human-like interactions between computers and humans through automated messaging and speech-enabled applications. By detecting speech and text, interpreting intent, deciphering different languages, and replying in a fashion that mimics human conversation, AI-powered chatbots can converse like a human. This process combines Natural Language Processing (NLP) with conversational AI machine learning.

In order to create effective applications that combine context, personalization, and relevance within human-computer interaction, applied conversational AI requires both science and art. Conversational design, a science focused on creating natural-sounding processes, is a critical component of creating conversational AI systems.


It appears uncomplicated on the surface; a customer interacts with a virtual assistant and receives an appropriate response. However, a variety of different technologies are at work behind the scenes to ensure that everything goes smoothly.

  • Natural Language Processing is the first phase (NLP):

Conversational AI NLP is responsible for correcting spellings, identifying synonyms, interpreting grammar, recognising sentiments, and breaking down a request into words and sentences that are easier to grasp for the virtual agent.

Several Deep Learning and conversational AI machine learning models take over once the request has been prepared using NLP. Natural Language Understanding (NLU) refers to a set of techniques that allow conversational AI to determine the correct intent (or topic) of a request and extract more information that can be used to trigger additional actions, such as context, account preferences, entity extraction, and so on.

  • Proprietary Algorithms:

To improve a virtual agent’s overall NLU capabilities, proprietary algorithms are also important. In order to boost AI conversational platform, Automatic Semantic Understanding (ASU) is created. It is a safety net that works alongside Deep Learning models to further limit the likelihood of conversational AI misinterpreting user intent.

Now that the request has been fully comprehended, it’s time to respond to the customer. Conversational AI outperforms traditional chatbot solutions because it allows a virtual agent to communicate in a personalised manner. 

Conversational AI learns new variations to each intent and how to develop over time as the virtual agent answers more questions and AI Trainers help to boost its understanding.


The following are the key components of conversational AI:

1. An user value proposition that is clearly defined.

Now consumers and employees connect with your company via the web, mobile, social media, email, and other platforms. Consider the scenarios where there is friction or annoyance if the engagement is already conversational. For example, where people may have to wait a long time for a response, switch between apps, or frequently input data. 

If the conversations are mostly informational, they may be suitable candidates for conversational AI automation or partial automation. However, they may be appropriate candidates for conversational augmentation if they are more intricate.

2. Conceptualisation.

This includes creating an appealing character, selecting the correct messaging platform and channel, polishing the dialogue flow, and ensuring that a conversational interface is well-suited to the work at hand. For conversational upgrades, you’ll need to figure out when the system should provide ideas to the human agents or users and then design the interactions to make them seamless and natural without being obtrusive.

3. Information.

Since both conversational agents and conversational improvements allow people to communicate with you, you’ll need to figure out how to generate the material they provide. If you already have conversational data, you may curate the best of it and utilize it as the foundation for your best conversational AI application’s responses. To fill in the gaps where conversational data is unavailable, you’ll need to use human authors or natural language generating tools.

4. Language Technology.

When dealing with voice interfaces, you’ll almost certainly need to employ speech-to-text transcription to generate text from a user’s input and text-to-speech to convert your responses back to audio. Language understanding techniques such as sentiment analysis, question classification, intent identification, and entity and subject extraction are likely to be relevant for both speech and text interfaces to grasp what the user is saying.

5. Other capabilities of machine learning.

You might wish to apply machine learning models in addition to language technology to help set the stage for a successful encounter and give value to the user.

6. Loops of feedback.

Each discussion should increase your ability to design a successful conversation while also updating your understanding of the user. You might directly ask the user for feedback after the chat, or you could look at downstream behaviour (such as if they re-engage or if the conversation leads to conversion) and utilise that information to optimise the next conversation.

7. Confidentiality and safety.

As conversational contact between bot and customer can be casual and natural, and the data can often contain sensitive information, so careful technical and policy treatment is necessary. At the same time, you’ll want to make sure you can use the data you’re gathering in the future to improve the user experience.


To understand what differentiates a chatbot from a conventional artificial intelligence solution let’s explore its components.
In this sense, some of the most important elements of conversational AI are:

  • System of instruction

Using supervised and semi-supervised learning methods, your customer service professionals can assess NLU findings and provide comments. Over time, this trains the AI to recognize and respond to your company’s unique preferences.

  • User-friendly interfaces

As the AI employs a modern, graphical interface, users don’t need to know how to code in order to comprehend or update it.

  • Extensions 

Extensions are ready-to-use conversational modules that can provide rapid assistance for common needs without forcing you to mold the AI. Extensive, automated regression testing ensures that you’re still accomplishing business goals after making changes to your AI.

  • Multi-language compatibility

Conversational AI systems can operate in multiple languages at the same time while using the same underlying logic and integrations.

These are only a few of the advantages that conversational AI may offer businesses. Different businesses have different AI requirements, demonstrating the technology’s adaptability. For example, some businesses don’t need to communicate with clients in many languages; thus, that feature can be turned off.

Features of a chatbot include:

Most conversational chatbots include the following basic features:

  • The vast majority of conversational chatbots are unable to understand sentences. Instead, they look for specific terms written by clients and answer with a pre-programmed response.
  • Conversational bots are quite simple to integrate into your system. This helps a lot when you need something to run quickly. Conversational AI is intrinsically more powerful and capable than chatbots, yet shaping an AI’s responses with machine learning takes time.
  • Almost many conversational chatbots are capable of handling between 100 and 200 customer intents. Customer intent is something that a client is seeking to communicate to the chatbot, and it usually involves a specific set of terms.

Even though there are plenty of advantages regarding the use of conversational artificial intelligence, not only in the hospitality industry but across all industries, we can highlight three main benefits:

1. Recognises the human factor.

Conversational AI can recognise human characteristics such as pauses, repetition, tone, and even sarcasm. These are important tools of human communication that conversational AI can quickly pick up on, making encounters more engaged and helpful for customers and enterprises.

2. Emotional Intelligence.

Customer psychology is an important aspect of modern customer service. As a result, an advanced conversational AI evaluates and analyses client feelings using conversational AI NLP (Natural Language Processing), categorising them as positive, negative, or neutral. This enables the conversational bot to respond appropriately to the customer.

3. Boosts employee efficiency.

Customer service representatives are frequently overworked, and as a result, they are mostly exhausted. As a result, conversational AI for customer service assists in prioritising calls and taking some responsibilities. If the conversational bot is unable to assist the consumer, then customer service representatives can obtain access to the conversation and solely deal with complex questions or problems.


Banking virtual assistant conversational bots may monitor customer balances and handle transactions across all bank accounts and are good conversational AI examples:

  • Conversational AI is now making waves in the field of finance and banking, and hospitality.
  • It recognizes any phrases or keywords that could suggest fraudulent activity and uses automatic speech recognition to avoid fraud. Anomalies in normal conduct that could imply fraud can also be detected by it. This makes banking and hospitality good conversational AI examples.
  • Finance bots can handle all of your transactions and provide you with a complete financial picture. Conversational AI can access and evaluate data like spending trends or bank accounts to assist you in making financial decisions. 

The use of different types of conversational AI in the hospitality and banking industries includes chatbots, voice assistants, mobile assistants, and interactive voice assistants. These are some of the most advanced conversational AI.


There is a wide range of applications of Conversational AI for hotels. It enables streamlining many processes and making things easier for both the hotel staff and the guests.

What are the key benefits of using Conversational AI in travel and tourism?

  • Quick response

Conversational AI for hotels can give speedy service to customers by instantaneously addressing their questions in a variety of languages, 24 hours a day, seven days a week. All thanks to AI-powered chatbot

Instant support not only results in satisfied customers, but it also means less time spent handling difficulties like reservations, which leads to shorter sales cycles and more bookings. In fact, all the credit goes to conversational AI for hotels.

  • Saves time and cost

With AI-powered hotel chatbots, all of the above issues may now be resolved at the same time. You don’t need a large team of human agents to answer the same questions over and over again. This is the era of conversational AI technology in the hospitality business, which allows you to decrease the time, money, and effort required for a high-quality online visitor experience.

  •  Increasing customer engagement

When a chatbot is driven by AI and integrated across all of your online visitor touchpoints, it produces exceptional outcomes. Then, to engage with present and future guests, an AI hotel bot extends beyond all time-based constraints to initiate conversations, settle inquiries, complete all transactions, and provide travel help. This is one major benefit of conversational AI in hospitality. 

To integrate the guest experience across your website, WhatsApp, Facebook, Instagram, Google, and other touchpoints, you can utilize an omnichannel conversational AI for customer service.

  • Data Treasure Capture

Conversational AI chatbots may acquire essential data such as your guests’ contact information, names, preferences, and more, in addition to interacting with them online. This data is used by AI to qualify and filter visitor leads in real-time, allowing human agents to focus on how to convert leads who appear uninterested to potential customers.

Hence, the hospitality industry is a great example of conversational AI applications.


Some of the biggest challenges Conversational AI technologies face include:

  • Lack of AI Experts

In September 2019, IDC forecasted that 97.9 billion dollars would be spent on AI technology by 2023. AI continues to grow at a steady rate as more people accept the concept of AI and recognise its significance in today’s digital world.

This rising demand for AI also means that there is a need for more AI tech developers. However, professionals who are trained to deploy AI at the required levels and can construct fully working systems are still in short supply.

  • IT Systems support

Information Technology makes life easier by creating systems that let us store, retrieve, and process data. IT ensures that the gadgets and technology we use are secure, reliable, and efficient.

Many IT systems still have room for improvement. AI-assisted IT solutions can improve it in a variety of ways. For example, it can aid in the development of layered security systems, the detection of security risks and breaches, and the assistance of programmers in writing better code, ensuring quality, and optimising servers.

  • Unstructured data processing

Unstructured data is extremely useful to a company, but many firms are unable to get significant insights from it since it cannot be evaluated using traditional techniques. They can’t be stored in a Relational Database Management System (RDBMS); therefore, processing and analysing them is difficult. Audio and video files, photos, documents, and site material are examples of unstructured data.

It will be easier for organisations to make sounder and more rational judgments if unstructured data can be broken down, processed, and stored as easier-to-understand analytical data utilising AI and Machine Learning.


Conversational apps are the next step in the evolution of the traditional NLP or rule-based chatbots as they free the traditional booking assistants from the restrictions of text-based interactions.

 HiJiffy enriches interactions with visual UI elements (e.g., buttons; calendars; maps; carousels; images; and more), helping with interactive elements when the conversation isn’t the most effective choice. Everything is done without giving up on providing a one-on-one experience. 

 Integrating HiJiffy’s interactive conversational app with PMS, Booking Engines, CRM and/or Maintenance/Housekeeping software, makes it the perfect addition to an automated workflow. For this reason, turning a chatbot into a conversational app can improve user experience and significantly impact the customer journey, including the direct bookings conversion rates. 

 Another critical aspect of HiJiffy’s conversational app is its omnichannel flexibility. The solution can perform on any messaging channel: in the hotel’s website, inside an app, or a third-party messaging platform like WhatsApp, Facebook Messenger, Instagram, Google My Business, and many, many others.

 Using the combination of text-based conversation and rich graphic elements, HiJiffy is reshaping how hotels – chains or independents – communicate with their guests.


The widespread growth of Emotional Intelligence (known as Emotional Quotient) will be the focus of conversational artificial intelligence in the future. Certain conversational artificial intelligence apps are assisting people in coping with the increasing pressures of a post-COVID society by automating routine jobs. For higher-order jobs and imaginative thinking, EQ will become a more important skill set.

It will be a major differentiator for businesses, resulting in more corporations actively cultivating EQ in their workforce. This emotional campaign will increase company culture, productivity, and innovation.

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