Author: Franz Malten Buemann

In the modern era, individuals use chat applications to connect with team members, close ones, family, and friends via their mobile phones. Messaging apps have become a crucial medium of communication these days. There are about six billion smartphone users globally. Hence, the mobile app market is progressively competitive. And this is where many founders and developers put their efforts lately.

It is obvious that application developers are always looking for simpler, quicker, and cheapest methods to build their chat applications. Effective development tools are essential in the application-building process. From programming languages to software development kits (SDK), the tools a coder utilizes define how rapidly they can launch a product in the marketplace. Flutter is a booming framework for developing cross-platform applications and gaining popularity worldwide. The following image explains the acceptance of the Flutter application development framework:

If you are thinking to develop your next mobile application using Flutter and want to learn more about this mobile app development framework, continue reading to find out everything you should know.

Introduction to Flutter

Flutter is a chat application SDK and UI cross-platform framework announced by Google in 2017. This framework allows flutter developer to create Android and iOS applications with a single codebase. The flutter comprises graphics and animation libraries that make creating UI stress-free. Flutter uses Dart i.e. a reactive programming language that assists in developing high-performance, scalable applications with an eye-catching user interface. According to a survey, Flutter has exceeded Reactive native and become the preferable mobile application development framework. Let’s understand this with an image:

Some top Features that Flutter Offers

• Dart programming language — Flutter make use of Dart which is an easy-to-learn programming language and permits flutter developers to build top-quality applications.
• Expressive and Interactive UI — This platform’s elements are created using the same ethics as Google’s material design procedures, giving users a flexible way to develop attractive applications.
• Native performance — Flutter applications are compiled into innate code, giving you the best predictable performance on both Android and iOS devices.
• Hot reload — This feature let the coder quickly and easily makes updates to the application without restarting it.

How does Flutter work?

Flutter is a coated structure including the framework, the engine, and platform-precise embedders. This SDK utilizes Google’s Dart programming language to create chat applications. The Flutter engine is inscribed in C/C++ and Skia library is the spine of this framework’s graphics competencies.

Flutter Layer Structure

Dart is the basis for many of Flutter’s performance benefits. It supports ahead-of-time (AOT) as well as just-in-time (JIT) compilation. AOT compiles code into lower-level innate code, which creates applications that run faster and have good performance. JIT enables the hot reload feature of this framework which diminishes development time. Dart also complies straight with native ARM or Intel x64 code, minimizing performance differences amid Flutter apps, and innate applications that depend on in-between code elucidations at runtime.

Advantages of Flutter

• It is fast — The framework utilizes Dart compiled into innate code. This means JavaScript Bridge is not needed. Resultantly, using Flutter developers can create applications that are fast and responsive.
• Rich set of widgets — Widgets are the building blocks of this mobile application development framework. This feature makes it easy to make striking and custom user interfaces.
• Easy debugging — Flutter uses Dart programming language that has great tools for debugging such as Dart Analyzer and DevTools Suite. These tools make it easy to discover and fix errors in the Flutter application.
• Automated testing — This framework has its set of tools for app testing and also Dart supports automated testing. It is easy to create unit, integration, and widget tests for mobile apps, so the coder can continually optimize and enhance the app quality.
• It creates cross-platform apps — The similar code can be utilized to create chat apps for both Android and iOS devices from a particular codebase instead of swapping between diverse platforms. This can save a lot of time as well as effort when building mobile applications.
• Diverse screen adaptability — The mobile apps developed using Flutter can run on numerous screen sizes and aspect ratios. This makes it easy to develop an application that works well on phones and tablets.

Disadvantages of Flutter

• Lack of third-party libraries.

• Tools available in Flutter are not as good as other platforms.

• Dart is not an extensively used programming language.

• It creates large applications which are not suitable for platforms with limited storage space.

Why Do Businesses Choose Flutter?

Here are some reasons why Tech founders pick Flutter for mobile application development:

• Endow better UI experience — The framework provides a better user interface. Being a businessperson, you want to launch your application quickly to get the marketing benefits. The faster the app is launched, the sooner it will make into the marketplace. Furthermore, your early customers help you comprehend their experiences, from which you can get to know their prospects. The latest release of Flutter has extended the reusable modules to a great extent.
• Reusability of code — Flutter lets coders utilize the same code base for diverse platforms in application development. This feature makes it easier to resolve bugs and helps in fast development. Developers can reuse 90% of the code and this enhances an application’s scalability and diminishes the Flutter app preservation cost.
• Free and open source — Flutter is a free and open-source platform that permits developers to use incalculable third-party libraries and packages for diverse features such as videos, chat, ads, etc.
• Low application building cost: The resource and time allocation for creating an application using Flutter is lesser. Therefore, the development cost would be low spontaneously.
• Community support: As Flutter is an open-source framework, so, it has several active community users about 20.8k. Hence, if you go with this framework for app development, you can get help and guidance from the active users of Flutter.

These are a few reasons why you should elect Flutter for cross-platform and what makes flutter an optimal choice for building MVP.

Different Applications built using Flutter Framework

Let’s take a look at the most exciting developments that have used the Flutter framework:

• Google Ads

• Reflectly

• Groupon

• Pairing

• Alibaba Xianyu App

• Realtor.com

• KlasterMe

• Take Your Seat

• Birch Finance etc.

Final Words

Flutter is gaining popularity with time. Many businesses realize it as a way to reduce costs and save time while developing more user-friendly applications. Hope this blog post has cleared your doubt about the Flutter framework. You can hire a flutter app Development Company if you want to make your next chat application more user-friendly and responsive.

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Are Tech Founders Ready to Build a Chat App In Flutter? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

It is well known that data science teams dedicate significant time and resources to developing and managing training data for AI and machine learning models — and the principal requisite for it is high-quality computer vision datasets. It is common for problems to stem from poor in-house tooling, labeling rework, difficulty locating data, and difficulties in collaborating & iterating on distributed teams’ data.

An organization’s development can be hindered by frequent workflow alterations, large datasets, and an ineffective data training workflow. Growing too rapidly, which is common for startups, regardless of their industry, aggravates these issues.

An example is the highly-competitive autonomous vehicle industry, where scalable training-data strategies are vital. The computer vision market for self-driving vehicles is high in complexity and competition. If your team cannot adapt (including the ability to annotate data), your business can suffer from customer dissatisfaction. Because of the complexity of training data, definitions and scope are constantly changing; if you cannot adapt, you can lose a lot of money.

Identifying the Right Data Annotation Strategy

Several reasons can explain why your training data strategy must adapt quickly. It could be because new product features generate an important volume of raw data that needs to be labeled, or you have decided to develop a solution that requires a significant volume of real-time data to perform well.

Moreover, ML model performance can often disappoint, especially in proofs-of-concept or early versions. Finding the optimal data annotation strategy can come late in the development process when a lot of money and time has already been spent.

Furthermore, some AI projects based on a large volume of data often require a feedback loop. It is often the case when neural networks are used to improve with each new case and tackle edge

cases continuously. ML requires iterative data annotation processes. Data annotation feedback loops and agile methodologies are critical for success.

Regardless of your situation, you can either react by hiring an internal team of annotators, which can be expensive, work with freelance annotators, or rely on a data annotation platform. Let’s see the pros and cons of each approach.

1. Building In-house Teams

Some companies choose to create an in-house data annotation team. A good reason to build an in-house data annotation could be related to security. Perhaps your projects’ nature requires labeled data that cannot be transmitted online.

Building an internal data annotation certainly brings benefits of process control and QA but also carries additional costs and risks:

HR resources

Management of a new team

Software development to support data annotation and workflows,

Risk of constant staff turnover

This method is not scalable. Like all companies involved with AI, your data needs may heavily evolve based on your current and future projects as you invest in hiring, managing, and training employees. Concretely, if you decide to build an in-house data annotation team, you will also require annotation tools. Unfortunately, teams that try to build in-house tech solutions often lose strategic development time rather than outsourcing the data annotation process.

While this method may seem more cost-effective at the start of your project, it’s often not a scalable solution due to operational infrastructure challenges, lack of training data know-how, and skills gaps for internal annotators.

Unless you work for a large tech company, chances are your internal tool will probably never be as advanced as an end-to-end data labeling tool built by many specialized developers and iterated over several years. Third-party data annotation tools are usually more sophisticated and come with experienced annotators and skilled project managers.

2. Choosing an Outsourced Data Processing Company

In this context, outsourcing refers to getting an industry expert on board to perform data processing tasks for AI and machine learning initiatives. The remuneration is often low and based on the volume of work. A prime example of this solution is Amazon Mechanical Turk.

This approach is considered an easy way to collaborate with an on-demand workforce. However, it forces you to define the assignment accurately and identify specific requirements & payment conditions. Conveying your idea clearly to the outsourced data annotation & labeling company behind your ML model is of the utmost significance — the blurry sense of your AI project to the outsourced company may lead to nothing but disaster. So picking the right data processing partner is important. Companies such as Cogito, Anolytics, and a few more offer high-quality custom data to train AI models by the in-house workforce and efficient workflow.

Some companies have built a crowd-as-a-service data platform and license data platforms. These platforms manage the workflow and sourcing of workers. Leveraging such data platforms will enable you to scale quickly with competitive pricing. However, because this approach is often used for small-size and temporary projects, there is no feedback loop and opportunity to train labelers over time.

Another aspect worth mentioning is that outsourced labelers tend to suffer from a lack of expertise, leading to poor training data quality. Give experience and expertise priority when picking your annotation & labeling partner to process your data for your AI model.

Data security is also challenging, as outsourced labelers often work independently on unsecured computers. Depending on your project’s importance, complexity and scope, outsourcing platforms can be an easy and cheap solution to label your data. But the low price comes at the cost of reduced data set quality, consistency, and confidentiality

3. Data Platform + Workforce

Another solution available on the market is related to companies that have built and sold their own data platform. These self-service platforms enable companies to efficiently self-manage their annotation projects with advanced capabilities, robust UI, advanced annotation tools, and, in some cases, ML-assisted annotation features.

ML teams can more easily manage labeling workflows by leveraging these platforms to produce quality computer vision training data while reducing labeling time compared with outsourcing platforms. They can also rely on some on-demand project managers to help structure their projects. Non-advanced transparent quality processes are also part of the offering from these platforms.

These SaaS-based platforms are known for their ability to scale quickly and provide competitive pricing. However, most of them are highly dependent on partners to secure the necessary non-contracted workforce.

This dependency often leads to a lack of expertise from their labelers, uptime issues, and ultimately poor quality labeled datasets (often the case for complex projects).

Another worth mentioning element is that these platforms are often mainly specialized in a specific industry (e.g., data labeling for the autonomous vehicles industry) or AI subfield (e.g., computer vision or NLP).

4. Platform + Fully Managed Workforce

Annotation solutions for data are offered by companies that have developed and sold their own data platforms and have fully managed workforces. The major difference between these platforms and other solutions is that such platforms depend on experienced labelers and subject matter experts for identifying edge cases and suggesting best practices for annotation.

These platforms rely heavily on both human expertise and automated data annotation tools to adapt to new guidelines or computer vision datasets requirements quickly, allowing them to be implemented same-day or next-day. By leveraging human expertise, edge cases will be identified proactively, guidelines will be recommended, and models will be developed faster.

Annotation time can be reduced by leveraging advanced tools used by industry experts. However, fully-managed services cost more than other data annotation solutions in terms of pricing because they cover the entire training data cycle.

5. ML-Assisted Annotation

A growing company tends to have an increasing amount of data to label. When this data is large, manual labeling becomes challenging. ML-assisted annotation can help solve this problem.

The goal of machine learning-assisted annotation is to reduce the time annotators spend annotating by making it possible for them to spend more time correcting complex cases so the machine learning models can be developed further by performing close-to-perfect annotations (covering all important annotation types).

Annotation tools that use machine learning are defined and automated according to different standards. Some allow users to create new neural networks from scratch, while others use pre-trained ones.

Because of this, the model is able to predict classes from an unlabeled image set. This results in annotation tasks turning into evaluation tasks after human annotators have reviewed and corrected them. Additionally, manual annotations are most useful in challenging edge cases, and machine learning-assisted annotation tools have proven to be effective in large datasets as well.

As a result, the annotator can see the suggested labels and only have to review them, while other solutions show only those images with the highest or lowest confidence for label confirmation; data annotation flexibility means you can find errors in your dataset in minutes rather than days.

Annotation tools that use machine learning can integrate a feedback loop, so that after reviewing the images, the user can add the images to a computer vision training data in order to train a more accurate neural network. Reinforcement learning, for instance, can mimic the decision-making process of annotators. the reinforcement agent identifies alarm data based on the annotations made by humans.

An image annotation tool that identifies polygons based on class is available in some data annotation tools. A polygon prediction is provided by the network after the annotator marks the selected object. The user can also use a pre-trained segmentation model to create a rough mask of unlabeled images automatically. A number of other features are available, including the ability to switch between labels & methods and the ability to reach output faster.

6. Promising Quality & Deadlines

The best way to optimize workflows as your company grows is to create in-house teams to analyze data, outsource it, or utilize a machine learning-based data platform. An ideal tool would be one that reprioritizes tasks automatically, provides feedback, and tracks production models.

Developing a fast model requires a deep understanding of the classes represented and the edge cases in a computer vision dataset. Data training strategies need to be scalable as well as reportable. In order to stay in control of your projects and measure the productivity and quality of your annotators, you need a dashboard with real-time analytics and error reports.

Additionally, a good dashboard will let you set labeling rules and integrate raw data easily, perhaps via a Rest API, so you can scale up and down tasks dynamically based on your training data.

Conclusion

This article presents several solutions that will help your company create a scalable data annotation strategy in a jiffy. Companies that need to scale benefit from data annotation platforms that provide complete and cost-effective solutions. You can choose to partner with an outsourced data processing company to develop training data for your machine learning and AI models in order to guarantee success. Originally published at Cogito.

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Annotation Strategies for Computer Vision Training Data was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Der Beitrag Top Digitaler Assistent 2023: Statista und Computerbild prämieren drei BOTfriends-Chatbots erschien zuerst auf BOTfriends.

Artificial Intelligence in the Cloud — Comparing Google Vertex AI vs. Amazon SageMaker

Cloud solutions make it easier for businesses to manage, track, and move their apps, files, and other resources to the cloud without having to deal with many obstacles.

Several benefits exist for moving to the cloud, including increases in scalability, security, and flexibility, as well as decreases in cost and environmental effects.

Artificial Intelligence in the cloud enables businesses to train, test, and deploy deep learning models using cloud infrastructure and services. The leading cloud providers are Amazon AWS, Google GCP, and Microsoft Azure. All three providers provide quality, highly scalable and secure cloud solutions and a huge set of cloud services.

This article focuses on cloud artificial intelligence services, specifically Google Vertex AI, and Amazon Sagemaker. Microsoft Azure also provides AI services through Azure AI, which I would also recommend, but for this article we will focus on Google Vertex AI, and Amazon Sagemaker.

Google Cloud Platform (GCP)

The Google Cloud allows you to host virtual machines (VMs) on a wide variety of hardware and operating systems through their Compute Engine. VMs can be used to host your website, web applications, or other services, and provide you terminal OS access to most Linux based operating systems. You can also enable ssh to allow remote access to your VM from your own computer.

Google Cloud makes it easy to create, start, and stop a VM, and billing is charged by the minute, which makes it easy to run experiments or tests on high end hardware with keeping costs low.

Google provides disk, image, and snapshot resources within its Compute Engine. Files can also be stored in Google Cloud Storage to allow network access and sharing of files.

Google Vertex AI

Google Vertex AI provides a cloud service to make it easier to train, test, and deploy deep learning models in the cloud.

Vertex AI provides AutoML as an easy way for non developers to start training a model. AutoML supports a UI for training models for image, tabular, text, and video. This provides an easy way to get started, but for most projects you will want a lower level of configuration through code.

Python is the overwhelmingly dominant language for deep learning. Most deep learning models are based on Python frameworks such as TensorFlow, PyTorch, or Apache MXNet. Python can be either through a terminal and your favorite code editor, or through Jupyter Notebooks. Jupyter notebooks provide a web based UI for editing and running Python scripts.

Vertex AI provides a Jupyter notebook based environment through Vertex AI Workbench. Vertex AI Workbench makes it easy to create and share Jupyter notebooks with your team.

Vertex AI is mainly geared to training models using TensorFlow Enterprise, but do also support creating VMs configured for PyTorch.

Once you have trained your model, you can deploy it using Vertex AI endpoints. Vertex AI endpoints provide a way to enable access to your model as a cloud service.

Vertex AI allows you to train models using very high end GPU and TPU servers. This is the main advantage of cloud AI, as most development organizations do not have their own high end GPU hardware, and training high models on traditional hardware is not feasible.

Amazon Web Service (AWS)

AWS allows you to host virtual machines (VMs) on a wide variety of hardware and operating systems through their EC2 service. VMs can be used to host your website, web applications, or other services, and provide you terminal OS access to most Linux based operating systems. You can also enable ssh to allow remote access to your VM from your own computer.

AWS makes it easy to create, start, and stop a VM, and billing is charged by the minute, which makes it easy to run experiments or tests on high end hardware with keeping cost low.

AWS provides disk, image, and snapshot resources within its EC2. Files can also be stored in AWS S3 to allow network access and sharing of files.

Amazon SageMaker

Amazon SageMaker provides a cloud service to make it easier to train, test, and deploy deep learning models in the cloud.

Sagemaker provides Jumpstart as an easy way for non developers to start training a model. Jumpstart supports a UI for training a wide variety of different models including image, tabular, text, and video. This provides an easy way to get started, but for most projects you will want a lower level of configuration through code.

Sagemaker provides a Jupyter notebook based environment through Sagemaker Studio. Sagemaker Studio makes it easy to create and share Jupyter notebooks with your team.

Sagemaker is more framework agnostic than Google, and provides Jumpstart models and VM configuration for Apache MXNet, PyTorch, and TensorFlow. Most of their Jumpstart models tend to be based on Apache MXNet.

Once you have trained your model, you can deploy it using Sagemaker Edge Manager. Edge Manager endpoints provide a way to enable access to your model as a cloud service. Sagemaker also provides a service Sagemaker NEO for deploying your model to various hardware and devices.

Sagemaker allows you to train models using very high end GPU servers. This is the main advantage of cloud AI, as most development organizations do not have their own high end GPU hardware, and training high models on traditional hardware is not feasible

Bot Libre and the Cloud

Although cloud providers do their best to make it easy to start a cloud AI project, cloud platforms and services are still very complex environments with a huge amount of different services to understand, and AI in general is a complex subject. Bot Libre and Paphus Solutions have many years of experience in cloud services, cloud AI, and AI and deep learning. If you are considering a cloud AI project, we can help you get started and develop your service through our development services.

The Bot Libre Enterprise Platform provides a cloud agnostic solutions for chatbots, AI, and deep learning services. Bot Libre can be deployed to Google GCP, Amazon AWS, Microsoft Azure, and many other lower cost cloud providers. Bot Libre and Paphus Solutions also provide cloud AI development services either using the Bot Libre platform, Vertex AI, SageMaker, as well as custom Python projects.

For all your development and cloud AI needs, contact Bot Libre at sales@botlibre.com

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Artificial Intelligence in the Cloud — Comparing Google Vertex AI vs. Amazon SageMaker was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Telegram has been growing in popularity ever since it was launched close to a decade ago. The app has nearly 540 million users, which is a staggering number. The app offers a powerful alternative to WhatsApp and Facebook Messenger and claims to be more secure than both of these platforms. Speed and security are Telegram’s USPs.

Without further ado, here are the steps to integrate Telegram with Kommunicate.

The integration presented in this blog post will teach you how to:

• Create a New Bot for Telegram
• Connect a Bot to Kommunicate

How to connect a chatbot to Kommunicate

Open Your Kommunicate Dashboard

Step 1: Click Integrations

Step 2: Click the Telegram card setting link

Step 3: Paste the API key into the Telegram integration card from the Kommunicate Dashboard and click the “Save and Integrate” button.

How to create a new bot for Telegram

Open Telegram messenger, sign in to your account or create a new one.

Step 1: In the search bar, search for @botfather

Note: official Telegram bots have a blue checkmark beside their name

Step 2: Select the BotFather channel and click /start.

Click on the “Send” button.

Step 3: Select /newbot — create a new bot.

Step 4: Add a bot name to call (Kommunicate_Telegrambot) and enter the bot name to display (Kommunicate321_Telegrambot)

Step 5: Copy the API key that is generated under “Use this token to access the HTTP API”

After creating the Telegram bot, follow the steps to trigger the Dialogflow chatbot.

Step 1: Click on the link to open the chatbot you created on Telegram

Step 2: Click on START to initiate a chat, once you click on START, you will send a message to Kommunicate. Next, the Dialogflow chatbot will start answering with the Welcome message you trained on the Dialogflow side.

Make sure you have selected the Dialogflow chatbot on the RULES section of the Kommunicate dashboard. Check here to build and integrate the Dialogflow chatbot if you still need to create one.

This is how your Kommunicate dashboard will look after the integration is complete:

You have now created your new bot for Telegram.

Originally Published on https://www.kommunicate.io/ at 29th October 2022

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How to Integrate a Dialogflow bot with Telegram was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

From voice-enabled interfaces to AI-powered virtual assistants or chatbots, Conversational AI is changing the way we live, work and communicate. It is changing the way businesses support their customers, whether helping them with their queries or purchase decisions. Adding a Conversational AI to your business changes the whole business model.

As per Gartner report, by 2021, 15% of all customer service interactions will be totally taken care of by AI, an increase of 400% from 2017. Yet, customer support is just a small part of the business success story.

Better customer service is directly proportional to business success. When businesses slide their service standards even to a minute level, they face negative consequences with serious repercussions on the overall business. Tragically, small trades like plumbing and heating services, gyms and personal trainers, laundry and many others, fail to meet the customer expectations in terms of response time, or overall customer experience. The factors that result in a negative effect on customer service are insufficient support staff, lack of real time support, or unable to respond in a timely manner to customer needs.

Let’s take an example, usual micro to small trade businesses consist of 1 to 20 people and there are days when all of them have tasks that they must attend to, so what happens when a customer calls in for an emergency service? no one is there to respond.

Result: Angry customer = loss of service

One missed phone call can be a huge blow to your company’s image and reputation. Customers may leave bad reviews on established websites which can be damaging to your business. Unfortunately the world we live in, society concentrates on the negative before the positive therefore you should be doing everything possible to give your business a positive corporate image. Thanks to the internet, customers nowadays are more likely to do some research about your business before availing themselves of your services or products. If they see a negative review, it will most likely drive them away. Aside from online feedback, poor customer service can get around through the word of mouth. Put yourself in your customer’s shoes: why would you continue dealing with a business that can’t help you when you need them?

Web forms vs Phone calls

Computer-mediated forms of communication are important in this digital age. However, it has been found that most people still prefer using their phones to contact businesses because of its personal touch. An unanswered phone call is a missed opportunity. Did you know that even just one unanswered phone call can significantly affect your revenue? Those missed phone calls are likely to be from potential customers.

Studies have found that the callback rate of people whose calls you’ve missed is very slim. If you’re lucky, customers may just complain, but still stick with your business. Unfortunately, though, most customers will simply walk away and turn to your competitors if their calls are left unanswered. With every phone call you miss, you provide your competitors with more leads.

In order to achieve a better customer experience, some businesses will try to do whatever it takes to keep their customers happy. They might hire dedicated customer agents to attend to phone calls or respond to emails to book services and so on, but that will again cost them a lot of money, maybe an office space, or additional IT infrastructure.

Sounds a lot right! What if there was a solution that could help these small tradespeople increase customer experience and increase sales by leveraging Conversational AI? Indeed, there is: Swifter AI, whose mission is to provide AI driven customer service and tools so that businesses that don’t have front-office staff and physical phones can deal with customer intake and payments, while also paying attention to all incoming calls. With powerful Natural Language Processing (NLP) and omnichannel features, Swifter AI helps small trades implement effective customer communications at low costs.

Here’s How we Help

Customer service is the backbone of any business and phone calls still play a very important role in this day and age. When you deliver excellent customer service over the phone, not only do you boost your customer satisfaction and loyalty, but you also ensure the success of your business. This is why Swifter AI can help you with excellent customer service to ensure the success within your business even when you are extremely busy.

Integrating Swifter AI’s assistants in your small trade businesses has massive benefits.

24/7 Customer Service

Think about the time when you had to wait for a long time for a locksmith to come and fix your entrance door. You couldn’t step out of your house the entire day.

Wasn’t it frustrating?

Long waiting times can be annoying for customers.

If you want to provide a customer with a quick appointment for any services you need to ensure that someone or something is there to answer the calls 24/7 irrespective of any time or day. Fortunately, conversational AI technology can be incredibly helpful in this regard.

They can be available to tackle customer queries 24/7, even from different time zones.

To get started, all you need is Swifter AI, that has a range of out of the box AI agents that can work in minutes with no programming knowledge required. The platform comes with a custom merchant profile.

For instance, you can define your own business account with general information like name, contact details, description and create your own list of services and appointment types that customers can book with you.

And Voila! The AI receptionist will do the customer engagement for you.

Wider Engagement

Convenience should be prioritized when it comes to customer experience. What if customers could directly book appointments or make calls without having to switch platforms?

With Swifter AI-driven solutions, it’s possible to do that.

You can reach more audiences by connecting with your customers across multiple channels like on your website or Facebook page, Amazon Alexa, Google Assistant, Facebook Messenger and many other popular platforms and devices.

Scheduling Appointments

Using Swifter AI you can be in control of your appointments. Your virtual AI receptionist will only book appointments for you when you want and you are free and you’ll receive, in real-time notification by email or SMS. You can also synchronize events with all major calendar platforms like Google Calendar, Outlook, Apple Calendar and others.

FAQs

How often were you customers misdirected with a response to their questions? No more with Swifter AI. You can instruct your AI agents how to respond to customers. You can start from a predefined set of questions and answers and customize them as you like. Your receptionist will be trained to your business specificity.

Full control over your budget and customers “sentiment”

One of the most important aspects is the ability to manage the AI agent usage over and view your current expenses. Together with our ‘top up account’ feature it gives you more control over the costs as you go. Acquire the advanced ability to see in real-time all the conversation with your customers over the telephone or any other communication platform. Get a glimpse of how the discussion went on, with our built-in sentiment analysis tool. You’ll know exactly the customers’ feedback instantly.

swifter.ai/ai-gallery

Ready to take a Conversational AI leap?

Conversational AI will stay to help your business support customers, give them a unique experience and ultimately help your small business settle into a profitable business.

If you’re ready to get started or just want to see how it works, you can register for free by following the link below:

TRY SWIFTER AI FOR FREE

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Conversational AI: An opportunity for Small Businesses was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Depending on the skill and experience of the programmers and system designers, you may get served by a soulless robot with an algorithm designed to see if you are committing fraud, a customer service AI which will ask you endless questions to see if you meet their byzantine requirements for a low risk customer, or possibly a well-designed robot system that provides immediate access to a human being in case of trouble.

Which of these three AI systems would you prefer to deal with in your business transactions?

If you choose AI system number 3, you can summon live help when a problem occurs. If you have the misfortune to deal with either of the other two choices, you are dealing with machine logic designed by an inexperienced programmer and when things go wrong you will put your business at risk because the company has decided to save money on salaries and you will not get human help without threats of litigation or public exposure via the internet.

There is a growing awareness of the need for human intervention in AI systems, but you may still encounter a system powered by AIs in the first two categories. If that happens, you should add your voice to the growing number of people writing about customer support problems with AI.

This is what the best AI designers are working toward:
Provide paths forward from failure. The trick isn’t to avoid failure, but to find it and make it just as user-centered as the rest of your product. No matter how hard you work to ensure a well-functioning system, AI is probabilistic by nature, and like all systems, will fail at some point. When this happens, the product needs to provide ways for the user to continue their task and to help the AI improve.

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AI Customer Service Comes In Several Flavors was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

golem: [noun] an artificial human being in Hebrew folklore endowed with life. — Merriam-Webster

According to legend, a golem was animated by instructions applied to its forehead and could be deactivated when those instructions were removed. The golem had no ability to think or decide. It could only carry out orders. This creature is usually brought to life through magical rituals or procedures and is limited to obeying any order of its creator in a literal way.

Like a well-designed chatbot, this modern golem simulates life until you present it with information it cannot handle, and then you encounter the implacable unreasonableness of a system that only mimics life but does not demonstrate it.

When an AI system is presented with data it is programmed to expect, it gives the impression it is capable of making intelligent decisions. When the data you are presenting to the system falls outside the limits of what it is programmed for, the results cannot be predicted and the system response fails its purpose.

Putting such a golem in charge of your life, as in an automobile or financial transactions, is extreme folly, which is why all AI systems interfacing with human beings should provide an override to access human support.

The trap we can fall into is that these AI designs are incredibly efficient when presented with inputs limited to what they are designed for. This lulls the inexperienced designer to assume they have covered all possible cases and not provide a means to override the design in case of failure.

This means that a poorly designed AI system handling human problems acts like a wood chipper which does not distinguish between human hands and the wood it is designed to chip. Fortunately, wood chippers have a manual override. Some AI systems do not.

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With AI Designs, We Can Create Modern Day Golems was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Most small businesses flourish because of the personal touch that the owners present to their customers. Large firms can maintain a good rapport with their customers by staffing the customer interfaces with personable and competent customer support people.

When a company completely replaces people with robots, unrecoverable errors multiply and customer trust vanishes.

If you search the internet for “AI Mistakes”, you will find hundreds of them and the reasons are many and they are not going away yet. The majority of the AI disasters are a result of adopting an AI system with no human backup. Management seeks to reduce payroll to improve the bottom line and automates financial transactions with AI systems scanning for signs of fraud.

Square has done this and the results are not pleasant. Armed with a list of conditions that indicate a high-risk customer, their AI systems acts swiftly to freeze any account that shows these signs of possible fraud: International financial transactions, Larger than normal transactions ($1000 or greater), and data entry errors on credit card transactions. The presence of any of these will trigger a freeze of funds for 90 days or more.

Those of you who operate an online business and have international customers have probably encountered all of these frequently and consider them a normal condition of any online business these days. If you are using any of the money processing services of the Square company, you may encounter their implacable AI system which will freeze your funds with no access to a human supervisor. More than 5000 customer complaints can attest to this situation.

This is a sign that management has bought into the idea that preventing fraud is more important than maintaining good relations with customers and a belief in the infallibility of AI systems. Automated systems rely on predictable data inputs and when this is missing we see self-driving car crashes and money processing systems that freeze your funds arbitrarily.

The internet abounds with solutions for the inflexibility and error prone operation of AI systems. One of the most applicable is to provide paths forward from failure, from the People + AI Guidebook. If you are using AI in your company, this quote should be posted on every wall.

Provide paths forward from failure. The trick isn’t to avoid failure, but to find it and make it just as user-centered as the rest of your product. No matter how hard you work to ensure a well-functioning system, AI is probabilistic by nature, and like all systems, will fail at some point. When this happens, the product needs to provide ways for the user to continue their task and to help the AI improve.

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If You Are A Small Business Owner, Would You Trust A Robot To Service Your Customers? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Phygital — Create More Immersive Experiences

You can be in your small one-bedroom apartment and host a dinner party in the Maldives, you can sell products from your garage but have customers buy from a lavish top-tier storefront on the East Coast, and you can host hundreds of thousands of people from all over the world at a conference from a small office cubicle.

Such is the meaning of “Phygital,” the fusion of the physical and digital world to offer an enhanced experience, and it’s the engine that will drive businesses to the metaverse.

Benefits of a Phygital Experience

Personalized experiences — your customers’ and guests’ experiences can be customized to their personality, industry, preferences, and even previous online habits. Unlike entering a physical space, where it’s a “one size fits all” experience, a Phygital approach brings your product, service, or event alive in new and exciting ways for each person.

Sustainability — Inventory management and waste management are major issues for traditional retail stores. Retailers can significantly reduce this problem. Also, for entertainment and business events, overhead costs would be far less, and there would be less depreciation on the environment.

Invest in Phygital, Invest in the Metaverse

The concept of this extended reality (XR) that is achieved through Phygital, calls for a greater leveraging of AR and VR expertise. This is due to the metaverse’s impending arrival, allowing Phygital to use hybrid virtual places to enhance and augment the physical experience.

The Bot Libre Metaverse Enterprise allows a diverse set of businesses in health, retail, finance, and gaming to engage with the metaverse. By joining the Beta Program, members can work with and alongside a team of AI and metaverse experts from Canada, Asia, and the Caribbean to develop their business solutions that are suitable for the metaverse.

If accepted to the program, individuals will benefit from the following assets in building their metaverse space.

• 3D/VR website
• 3D Android & iOS app
• VR Quest app
• Custom 3D avatar
• Custom 3D space
• PLUS integration with blockchain, cryptocurrencies, and NFTs

For persons interested in participating in this dynamic program, contact sales@botlibre.biz.

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Phygital — Create More Immersive Experiences was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.