Changing the Business landscape with Smart and Secure Virtual Assistants Powered by AWS
AI Bots & Virtual Assistants are transforming Businesses
With the advancements in technologies like Artificial Intelligence (AI) and Natural Language Processing (NLP), today, AI Bots, and Virtual Assistants are adding value across business processes. AI-Powered Cognitive Assistants (COGS for short) are being utilized by brands for marketing, to build a solid sales pipeline, drive engagement, enable commerce, induce brand loyalty and deliver delightful customer experiences.
The conversational AI industry has exploded and has seen immense growth after the COVID-19 lockdown. For example, it was recorded that 67% of global customers have communicated with a Bot in the past 12 months. Also, it was noted that more than 40% of people believe that until their queries get answered, they don’t mind whether it’s a live agent or an AI Bot. Further, it is estimated that the Bot industry will reach 454.8 million U.S. dollars by 2027, from 40.9 million dollars in 2018!
In this blog, we have explored the various capabilities and thrown light on how the Chatbot Technology has evolved from rule-based chatbots to advanced Generative AI virtual assistants with the latest advancements in AI services powered by AWS like AWS SageMaker, Amazon BedRock, and Amazon Kendra along with Amazon Lex.
The conversational AI industry has exploded and has seen immense growth after the COVID-19 lockdown. For example, it was recorded that 67% of global customers have communicated with a Bot in the past 12 months. Also, it was noted that more than 40% of people believe that until their queries get answered, they don’t mind whether it’s a live agent or an AI Bot. Further, it is estimated that the Bot industry will reach 454.8 million U.S. dollars by 2027, from 40.9 million dollars in 2018!
In this blog, we have explored the various capabilities and thrown light on how the Chatbot Technology has evolved from rule-based chatbots to advanced Generative AI virtual assistants with the latest advancements in AI services powered by AWS like AWS SageMaker, Amazon BedRock, and Amazon Kendra along with Amazon Lex.
AWS Powered Generative AI Virtual Assistants for Enterprise
The landscape of conversational AI has been profoundly reshaped by the emergence of generative AI technology. This progress has fundamentally changed how chatbots engage with users. By using advanced language models, virtual assistants now engage in more natural, context-sensitive, and human-like conversations, greatly improving user experiences.
Streebo, an Amazon Business Partner has built an array of pre-trained intelligent chatbot solutions. These solutions leverage various AWS services, including Amazon Lex, Amazon Kendra, AWS SageMaker, FMs available on Amazon BedRock and more. Also, customers can now utilize industry-standard models from platforms like HuggingFace through AWS SageMaker. This synergy ensures our chatbots deliver dynamic and context-aware interactions, supported by a robust implementation stack, ultimately providing exceptional generative AI experiences for our valued clients. Let’s discover them in detail.
But before that, it’s important to understand the basics –
Streebo, an Amazon Business Partner has built an array of pre-trained intelligent chatbot solutions. These solutions leverage various AWS services, including Amazon Lex, Amazon Kendra, AWS SageMaker, FMs available on Amazon BedRock and more. Also, customers can now utilize industry-standard models from platforms like HuggingFace through AWS SageMaker. This synergy ensures our chatbots deliver dynamic and context-aware interactions, supported by a robust implementation stack, ultimately providing exceptional generative AI experiences for our valued clients. Let’s discover them in detail.
But before that, it’s important to understand the basics –
What is NLP? A term most frequently and commonly used with an AI-Powered Smart Bot!
NLP is a computer program that can understand human language as it is spoken and written. It helps machines process and understand human language so that they can communicate with the user in their natural language – English, Hindi, Spanish, or any other. It has a variety of real-world applications in a number of fields, including medical research, search engines, and business intelligence. Examples include machine translation, summarization, ticket classification, spell check, and more. Natural Language Processing (NLP) is the key element of an Artificial Intelligence-based Bot
The Power of Amazon Lex: Our Choice for Natural Language Processing
Amazon LEX is an NLP engine provided by Amazon Web Services (AWS). Amazon Lex is a service for building Artificial Intelligence based Conversational Interfaces into any application using Voice and Text. Amazon Lex provides advanced Deep Learning functionalities of automatic Speech Recognition for converting speech to text and Natural Language Understanding (NLU), to recognize the intent of the text. This enables developers to build applications with highly engaging user experiences and life-like conversational interactions. Amazon Lex enables developers to develop Natural Language Conversational Bots or Virtual Assistants.
How to Build a Smart Bot using Amazon Lex?
Actions/Intents
To create a Bot, first the actions performed by the Bot need to be defined. These actions are the intent that needs to be fulfilled by the Bot.
Sample Utterances
For each intent, sample utterances and slots should be added. Utterances are phrases that invoke the intents. Slots are input data that are required to fulfill the intent.
Business Logic
Lastly the Business Logic necessary to execute the actions is provided.
Amazon Lex Bots can be created both via Console and REST APIs.
Amazon Lex Powered Virtual Assistants with other AWS Services:
Amazon Lex is a multi-platform service. You can build your Bot once, and integrate it across multiple platforms, such as Mobile, Web, and various Messaging platforms.
For example, by integrating Amazon Lex with Amazon Connect, you can take advantage of Amazon Lex ASR and NLU capabilities to create great self-service experiences for your customers, quickly. Also, note that creating and connecting your Virtual Assistant to your Amazon Connect instance can be done in minutes.
Moreover, the seamless integration of Amazon Kendra presents a seamless approach to content exploration across a multitude of repositories, facilitated by its cohesive integration with built-in connectors. This integration guarantees swift and efficient access to pertinent information, leading to a marked enhancement in productivity and user contentment throughout information retrieval processes.
Also, by harnessing the capabilities of AWS SageMaker, Amazon BedRock, and a diverse array of open standard Language Models (LLM), our conversational interfaces exhibit a remarkable capacity for engaging in conversations that are both natural and contextually relevant, thereby ushering in an elevated dimension to the overall user experience
For example, by integrating Amazon Lex with Amazon Connect, you can take advantage of Amazon Lex ASR and NLU capabilities to create great self-service experiences for your customers, quickly. Also, note that creating and connecting your Virtual Assistant to your Amazon Connect instance can be done in minutes.
Moreover, the seamless integration of Amazon Kendra presents a seamless approach to content exploration across a multitude of repositories, facilitated by its cohesive integration with built-in connectors. This integration guarantees swift and efficient access to pertinent information, leading to a marked enhancement in productivity and user contentment throughout information retrieval processes.
Also, by harnessing the capabilities of AWS SageMaker, Amazon BedRock, and a diverse array of open standard Language Models (LLM), our conversational interfaces exhibit a remarkable capacity for engaging in conversations that are both natural and contextually relevant, thereby ushering in an elevated dimension to the overall user experience
- First, the current state of the Bot is saved even if it is not ready for prime time.
- Next, the Natural Language Model (NLM) is built, which is what the Bot uses to interact with Customers. This also creates versioning for the intents and slots.
- This is followed by a Test on a Chat Interface on the Console aligning quick iteration and updating.
- inally a new version of the Bot and its alias is created and pushed to the clients.
Deployment to Chat Services:
Amazon Lex allows developers to easily publish the Bot to Chat Services from Amazon Lex Console reducing multiplatform development efforts. Formatting capabilities of the Console provide an intuitive user experience tailored to Chat platforms like Facebook Messenger, WhatsApp, Slack, and SMS.
Amazon Lex can also be integrated into AWS Mobile Hub so it can quickly be deployed on a Mobile App to access the Amazon Lex Bot.
Chat Monitoring
Amazon CloudWatch can be used to track the health of the Amazon Lex Bot. It provides a matrix with individual Amazon Lex Operations and Global Amazon Lex Operations to an individual user. One can also set up CloudWatch Alarms to be notified with one or more matrices exceeding the threshold that one defines.
For example, a Business Owner can monitor the number of requests made to the Bot over a particular time period, view the latency of successful requests, or raise an alarm when errors exceed the threshold.
For example, a Business Owner can monitor the number of requests made to the Bot over a particular time period, view the latency of successful requests, or raise an alarm when errors exceed the threshold.
Top Use Cases of AWS Powered Virtual Assistants:
This will help give you a concrete understanding of how Amazon Lex can be used.
Customer Care Bots at Call Center:
Amazon Lex can be used with other AWS Services to power a Call Center Bot. In this scenario, a customer calls a customer service line to reschedule an appointment. Amazon Connect calls Amazon Lex as soon as the customer asks a scheduling question. This triggers AWS Lambda, an event-driven, serverless computing platform provided by Amazon as part of Amazon Web Services which in turn calls a database to look up customer information by phone, to summon the customer scheduling software. Once a new appointment date is confirmed, Amazon Connect sends a confirmation message via SMS to the customer. You can also use Amazon Lex to build a Bot for everyday customer requests, such as accessing the latest news updates, game scores, weather, and more.
Patient Assistant at Hospital
Amazon Lex can be used with other AWS Services to power a Call Center Bot. In this scenario, a customer calls a customer service line to reschedule an appointment. Amazon Connect calls Amazon Lex as soon as the customer asks a scheduling question. This triggers AWS Lambda, an event-driven, serverless computing platform provided by Amazon as part of Amazon Web Services which in turn calls a database to look up customer information by phone, to summon the customer scheduling software. Once a new appointment date is confirmed, Amazon Connect sends a confirmation message via SMS to the customer. You can also use Amazon Lex to build a Bot for everyday customer requests, such as accessing the latest news updates, game scores, weather, and more.
Patient Assistant at Hospital:
Let’s take an example of a patient requesting an appointment at a medical facility. After a patient requests an appointment, Amazon Lex recognizes the appointment request and asks the patient for his or her preferred appointment date and time. The appointment time is then received, and confirmation is sent to patients via text message or email.
Employee Virtual Assistant:
One final use case that we would like to discuss for Amazon Lex is building an enterprise-grade, smart AI Bot that streamlines common work activities and improves organizational efficiencies. For example, employees checking sales or marketing data from HubSpot using a smart AI Bot. Lex can recognize the request, connect to HubSpot for the data request and respond to the sales reps with the requested data.
In summary, AWS is a reliable platform for building Conversational Interfaces into any application using Voice and Text. AWS democratizes Deep Learning Technologies for Speech Recognition and Natural Language Understanding by putting the power of Amazon Lex within reach of all developers.
Embracing Innovation: Transformative Features Unlocked via AWS Services
In summary, AWS is a reliable platform for building Conversational Interfaces into any application using Voice and Text. AWS democratizes Deep Learning Technologies for Speech Recognition and Natural Language Understanding by putting the power of Amazon Lex within reach of all developers.
Embracing Innovation: Transformative Features Unlocked via AWS Services
Speech Recognition:
Applications often need a Bot to talk to the end user. Amazon Lex can be used to generate speech responses from Virtual Assistants. This conversion from intent, text-based responses to audio is known as audio output, speech synthesis, or text-to-speech. It can also use audio for both input and output when detecting an intent. This use case is common when developing apps that communicate with users via a purely audio interface.
Disambiguation
Standard dialogs are triggered when the Bot recognizes the consumer’s intent, also known as a match or a pattern match. Fallback dialogs are triggered when the Bot doesn’t recognize the consumer’s message at all. But what happens when the Bot recognizes the consumer’s message and matches it to multiple intents? This is where disambiguation is used. Amazon Lex provides a disambiguation process whereby the Bot gets clarification from the user on what is meant by the user’s message.
Digression
A user is in the middle of a flow that is designed to achieve a certain goal, but the user decides to abruptly switch the topic or intent to initiate a different action flow that is designed to address a different goal. Essentially, the user wants to jump midstream from one journey or story to another. Bots usually are not designed to accommodate this, and often, once a user has committed to a journey or topic, the Bot has to see it through. AWS powered chatbot can handle digression as well!
Backend Integration:
A Virtual Assistant can fetch information from backend systems/software which is very useful for businesses usually. This is called Backend Integration. AWS Services & APIs seamlessly integrate with many enterprise-grade softwares used across industries for different domains like SAP, ServiceNow, Workday, Core Banking, eCommerce, ERPs, Operations, HR Management Software, and many more.
Omnichannel
AWS powered virtual assistants can be deployed across different social media platforms like Facebook Messenger, WhatsApp, Instagram, Telegram, WeChat, Signal, Google Chat, Slack, Sametime, Email and MS Teams among others thus providing an option to create omnichannel solutions. The Amazon Lex NLP can be optimized to be integrated with Voice devices also such as Amazon Alexa and Google Home.
Multimodal Capabilities with AWS SageMaker and AWS BedRock:
Our AWS-powered chatbot is a cutting-edge solution that incorporates multimodal capabilities, thanks to the integration of state-of-the-art features from AWS SageMaker and Amazon BedRock. These technologies enable our chatbot to process and generate responses through various modalities, such as text, speech, and images. With SageMaker, we can harness the power of machine learning to enhance conversational experiences, while AWS BedRock offers robust infrastructure support, ensuring our chatbot operates seamlessly and at scale. Together, they form the foundation for a versatile and powerful chatbot.
Accessing Unstructured Data with Amazon Kendra:
Amazon Kendra empowers our chatbot with the ability to access and make sense of unstructured data like never before. Leveraging Kendra’s advanced search and natural language understanding capabilities, our chatbot can efficiently sift through vast amounts of unstructured information to provide users with accurate and relevant answers. Whether it’s searching through documents, FAQs, or databases, Kendra ensures that our chatbot can retrieve the right information quickly, enhancing user satisfaction and productivity. This integration transforms our chatbot into a valuable resource for accessing knowledge within our organization.
Now we know why Amazon Lex from AWS is the go-to choice for many!
To build a Conversational Interface that can handle all the use cases, it becomes vital to choose an appropriate Natural Language Processing (NLP) Engine. Often, the choice comes down to finding the tool which synergizes with the existing business operations in order to gain ROI. Further, an NLP engine should have the capability to develop a smart and powerful Virtual Assistant that fulfills all the business requirements and technical specifications where it needs to be deployed.
After going through all the above essential features to build a Cognitive Assistant/Virtual Assistant you must get an idea of what Amazon Lex and AWS Services have to offer. As per the Gartner review, currently, it is the leading NLP engine that offers greater scalability, seamless integration, easy customization as per business requirements, ease of deployment, administration, and maintenance.
After going through all the above essential features to build a Cognitive Assistant/Virtual Assistant you must get an idea of what Amazon Lex and AWS Services have to offer. As per the Gartner review, currently, it is the leading NLP engine that offers greater scalability, seamless integration, easy customization as per business requirements, ease of deployment, administration, and maintenance.
Amazon Lex & Generative AI Powered Streebo’s SMART® Bots
Streebo a leading Artificial Intelligence (AI) and Digital Transformation Company has created a very powerful, intelligent, and pre-trained AWS-Powered Voice and Chat Smart Bots Store. All the Bots in the store are powered by the Amazon Lex NLP engine.
Our advanced chatbot architecture gives flexibility to enterprises to pick any of the world-class Generative AI or LLM platforms like FMs available on AWS SageMaker, Amazon BedRock, and a diverse array of open standard Language Models (LLM) available on HuggingFace and more. Further, this Bot Store has pre-trained bots for 15 plus industries trained with industry-specific functions that serve different use cases like Customer Service, Marketing, Sales, HR & Operations. The Bot Store has Smart Bots that provide assistance to different users like the prospects, customers, employees, on-field agents, and many more. These Virtual Assistants are ready to be deployed and can be wired to your system as needed on the desired platform.
Our advanced chatbot architecture gives flexibility to enterprises to pick any of the world-class Generative AI or LLM platforms like FMs available on AWS SageMaker, Amazon BedRock, and a diverse array of open standard Language Models (LLM) available on HuggingFace and more. Further, this Bot Store has pre-trained bots for 15 plus industries trained with industry-specific functions that serve different use cases like Customer Service, Marketing, Sales, HR & Operations. The Bot Store has Smart Bots that provide assistance to different users like the prospects, customers, employees, on-field agents, and many more. These Virtual Assistants are ready to be deployed and can be wired to your system as needed on the desired platform.
The plug-n-play architecture allows IT & Non-IT teams to easily integrate the Bot with the required third-party enterprise backend systems SAP, ServiceNow, Workday, Core Banking, eCommerce, ERPs, CRMs Operations, HR Management Software, and many more among others.
By leveraging the Amazon Lex NLP engine, Streebo Smart Bots extend the interaction experience with the brands/businesses to a variety of social media platforms, such as Meta (Facebook) Messenger, WhatsApp, Instagram, Google Chat, Telegram, WeChat, Signal, Slack, Sametime, MS Teams and even SMS. They can even handle Voice channels such as IVR, Amazon Alexa, and Google Home. Bots can also be deployed to existing digital properties such as Websites and Mobile apps.
Streebo’s Bot Store is powered by AWS thus ensuring top encrypted libraries and providing customers with a secured and scalable solution.
Further, the built-in analytics tool allows for tracking the Virtual Assistant’s performance. The integrated analytics tool can read the session including usage patterns, latency issues, as well
as high and low-performing intents. This helps in improving the performance of the Conversational Interface.
By staffing the Business with these 24X7 available Intelligent Virtual Agents with Generative AI experience, businesses can easily assist their prospects, customers, and employees instantly and at the same time reduce their operational costs. That is why these AI-powered, Smart, Virtual Assistants have a powerful return on investment (ROI) as they can both improve sales and significantly reduce costs for organizations. It is projected that by 2025, companies will save over 3 billion USD on IT maintenance via Bots & Virtual Agents.
By leveraging the Amazon Lex NLP engine, Streebo Smart Bots extend the interaction experience with the brands/businesses to a variety of social media platforms, such as Meta (Facebook) Messenger, WhatsApp, Instagram, Google Chat, Telegram, WeChat, Signal, Slack, Sametime, MS Teams and even SMS. They can even handle Voice channels such as IVR, Amazon Alexa, and Google Home. Bots can also be deployed to existing digital properties such as Websites and Mobile apps.
Streebo’s Bot Store is powered by AWS thus ensuring top encrypted libraries and providing customers with a secured and scalable solution.
Further, the built-in analytics tool allows for tracking the Virtual Assistant’s performance. The integrated analytics tool can read the session including usage patterns, latency issues, as well
as high and low-performing intents. This helps in improving the performance of the Conversational Interface.
By staffing the Business with these 24X7 available Intelligent Virtual Agents with Generative AI experience, businesses can easily assist their prospects, customers, and employees instantly and at the same time reduce their operational costs. That is why these AI-powered, Smart, Virtual Assistants have a powerful return on investment (ROI) as they can both improve sales and significantly reduce costs for organizations. It is projected that by 2025, companies will save over 3 billion USD on IT maintenance via Bots & Virtual Agents.
Streebo’s Smart Bots come with the following amazing, break-through features:
- 24×7 Omni-channel Support
- Instant transactional support
- Secured and personalized Conversations
- Support in 38+ Languages
- Support of both Voice & Chat channels
- Inbuilt Live Agent platform
- Advanced Analytics
- Multimodal capabilities
- Access to unstructured data
Frequently Asked Questions
Let’s go over the basic workflow of Amazon Lex in which there are four simple stages.
1. First, the current state of the Bot is saved even if it is not ready for prime time.
2. Next, the Natural Language Model (NLM) is built, which is what the Bot uses to interact with Customers. This also creates versioning for the intents and slots.
3. This is followed by a Test on a Chat Interface on the Console aligning quick iteration and updating.
4. Finally a new version of the Bot and its alias is created and pushed to the clients.
Amazon Lex offers an efficient solution for deploying bots to chat services directly from the Amazon Lex Console. This eliminates the need for complex multiplatform development efforts. The Console is equipped with formatting capabilities that provide a user-friendly experience tailored to popular chat platforms like Facebook Messenger, WhatsApp, Slack, and SMS.
Additionally, Amazon Lex can be seamlessly integrated into AWS Mobile Hub, facilitating swift deployment of the Amazon Lex Bot onto a mobile app, thereby expanding its accessibility and usage.
Amazon CloudWatch offers a comprehensive monitoring solution for the Amazon Lex Bot’s performance. It presents a matrix detailing specific Amazon Lex Operations and Global Amazon Lex Operations on an individual user basis. Additionally, CloudWatch Alarms can be configured to notify users when predefined thresholds are exceeded.
For instance, a Business Owner can effectively track metrics such as request volumes over specific timeframes, latency of successful requests, and even set up alerts for error rates that surpass the defined threshold.
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Zero $ setup fees, zero commitment – just results.
Sign up now and experience the future of customer interactions!
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