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The ultimate guide to AI customer service chatbots

Supercharge your customer journey with custom AI chatbots!

Understanding AI customer service chatbots: The new business imperative you need to know

Have you interacted with a fun yet helpful customer service chatbot lately?

If you’ve visited a large brand’s website, chances are you have —or have seen a customer support chatbot. You may have interacted with AI customer service chatbots to get an answer to a question, to view your order history, or just out of curiosity!

In this blog, we’ll tell you everything you need to know about AI customer service chatbots! We’ll delve into how customer service chatbots are shaping the future of customer interactions, examples of well-executed customer service chatbots, best practices for designing and implementing AI customer service chatbots, and case studies of companies that have found success using AI chatbots for customer service.

First, let's define AI customer service chatbots!

What is an AI customer service chatbot?

An AI customer service chatbot is a computer program or software application powered by artificial intelligence (AI), machine learning (ML), natural language processing (NLP), natural language understanding (NLU), deep learning techniques, or generative pre-trained transformers (GPTs) to mimic and process human conversations when offering customer support. They live on businesses’ websites or mobile apps and get included in service models to answer common questions and automate routine tasks, improving customer experience and support efficiency in a very scalable way. 

Think of OpenAI’s chatbot, ChatGPT integrated and branded into your favorite brand’s app or website.

It’s important to note that today’s AI chatbots are not the same as the scripted chatbots of years past. With the introduction of generative AI into chatbot technology, the experience has vastly improved from early chatbot software limited to pre-written scripts or basic AI.

Today’s AI customer service chatbots use large language models to understand, predict, and respond to customer inquiries in a way that’s both human-like and highly efficient. This integration of AI allows chatbots to handle a wide range of customer service tasks, from answering frequently asked questions to providing personalized product recommendations and processing returns. 

One of the significant benefits of customer service AI chatbots is their real-time availability. AI customer support chatbots can provide round-the-clock service where human support agents are limited to specific working hours. Serving immediate responses to customer inquiries at all hours of the day and night is a win for businesses and their customers. With 74% of customers willing to speak with a bot to get an answer to a simple question and 90% of people wanting businesses to use chatbots, consumers are primed to engage with AI chatbots. Moreover, ecommerce chatbots drive a nearly 70% increase in sales and a whopping 70% increase in conversions!

By managing multiple inquiries simultaneously, these chatbots drastically cut wait times and free up human agents to tackle the trickier problems. This isn't just efficient—it's also financially savvy. Nearly 60% of businesses report a significant return on their investment in chatbot technology. As the demand for online support grows, the smart move is clear: adopting AI customer service chatbots is crucial for keeping up and staying competitive. With continual advancements and increasingly simpler implementations, the future of customer support is leaning heavily toward generative AI integration.

A brief history of AI chatbot technology and types

A brief history of AI chatbots

Chatbots have been around for a long time. Chatbot technology has been characterized by a series of advancements and lulls, and several decades passed until the technology emerged on the consumer market after the introduction of the internet as we know it today. Here is a quick history of AI chatbots:

  • The first widely recognized chatbot was Eliza, created in 1966 by MIT computer scientist Joseph Weizenbaum.

  • In 2010, IBM, Apple, and Amazon introduced their AI assistants: Watson, Siri, and Alexa.

  • In 2021, Jasper introduced the first generative AI chatbots powered by a Large Language Model (LLM) trained on massive data sets and are far more advanced and “intelligent” than previous bots

  • Today, many LLMs are continuously released like ChatGPT, Llama, Claude, Solar, and many more.

Here is an insightful chatbot history illustration from TechTarget:

A timeline of AI chatbots
Figure 1: AI chatbot timeline

The different types AI chatbots

AI chatbots can be broadly categorized into three types:

  • Scripted chatbots: Scripted chatbots, also known as rule-based chatbots, operate based on predefined pathways and scripts. They are programmed to respond to specific inputs with specific outputs using a set of established rules. These chatbots do not possess the ability to understand context or intent beyond their programmed scripts, making them suitable for straightforward tasks where user inputs can be anticipated and are highly structured.

  • AI-powered chatbots: AI chatbots use artificial intelligence technologies, including natural language processing (NLP) and sometimes machine learning (ML), to understand and respond to user queries more flexibly and adaptively. Unlike scripted chatbots, AI can interpret the user's intent and generate pre-defined responses, allowing for a more dynamic and conversational experience. However, Their scope remains narrow, and the experience is still far from being human-like. Popular technology using early AI included Dialogflow, for example.

  • Generative AI chatbots: Generative AI chatbots are a type of AI chatbot that utilizes advanced machine learning models, particularly those based on large language models (LLMs), to generate responses from scratch (e.g., ChatGPT, Llama, Claude, Solar). These generative AI chatbots can handle various conversational topics and construct replies in real time. Generative AI chatbots are known for their ability to maintain human-like interactions, as they generate unique and contextually relevant responses rather than pulling from a fixed set of answers.

The rise of AI in customer service through generative AI chatbots marks a significant shift from transactional customer service chatbot interactions to more engaging experiences. Today’s AI chatbots don’t just respond to customer needs; they anticipate them, leading to a more satisfying and practical customer support experience.

However, the best AI chatbot solutions combine the features of scripted chatbots with generative AI technology. They rely on AI’s creativity for unpredictable questions while controlling the conversation with scripted workflows and custom answers when possible. Let’s explore some key benefits of using AI chatbots for customer service.

Scripted workflows with custom answers created in the Sendbird AI chatbot control center
Figure 2: Scripted workflows with custom answers created in the Sendbird AI chatbot control center

Top 10 advantages of AI customer service chatbots: Why do you need them?

Generative AI chatbots have notably transformed customer service, offering a range of benefits that enhance both customer experience and improve operational efficiency:

  1. 24/7 availability - AI chatbots are available around the clock so customers can get assistance anytime, helping improve overall customer service satisfaction.

  2. Handling large queries: AI customer service chatbots can manage numerous conversations simultaneously, significantly reducing customer wait times and improving efficiency.

  3. Personalization: By accessing customer data with function calling, AI chatbots can deliver personalized experiences, making suggestions tailored to individual preferences and past interactions.

  4. Cost efficiency: Implementing AI chatbots is an effective way to augment your support staff while limiting the hiring of human agents, though human customer support agents will always be necessary to oversee chatbots, handle complex queries, and offer the crucial element of empathy in customer interactions.

  5. Consistent responses: AI customer service chatbots with pre-built answers to select from and quality embedding can offer consistency in responses, ensuring that the information provided to customers is accurate and uniform.

  6. Improved customer engagement: Conversational AI chatbots can engage customers more humanly, enhancing the quality of interaction. still, AI chatbots are imperfect and may require human oversight of their interactions through bot logs and analytics.

  7. Instant response: Speed is crucial in customer service, and AI chatbots immediately respond to queries. This helps brands significantly improve response times and frees up human customer service agents to deal with complex inquiries and situations that require a human touch. It also increases CSAT.

  8. Reduced human error: AI chatbots help minimize human error in customer service interactions. AI customer service chatbots mitigate damage from the inconsistent presentation of information, helping support accurate information delivery (though chatbots may present inaccuracies and require human supervision).

  9. Scalability: Customer service AI chatbots can quickly scale up or down based on demand. AI chatbots for customer service offer more flexibility during peak periods or promotional campaigns, helping supplement your human staff.

  10. Analytics and insights - AI customer service chatbots can gather large volumes of data from customer interactions, providing valuable insights into customer needs and preferences. Brands can use this data to improve service delivery.

Integrating AI customer service chatbots can enhance the entire customer experience when implemented effectively. Sendbird’s AI chatbot technology offers an advanced customer service AI solution for businesses across industries. Let’s look at how it works and how you can quickly get up and running.

How to leverage Sendbird for superior customer service AI

Sendbird’s customer service AI chatbot offers the dependability of proven customer support software combined with cutting-edge AI chatbot technology through our custom AI chatbot solution. This combination delivers a powerful AI-powered customer support solution that redefines the efficiency and effectiveness of customer interactions with features that include:

  • Personalized customer interactions: Custom AI chatbots excel at delivering personalized experiences informed by NLP and ML training, prompt engineering, and data retrieval using function calling. By using customer interaction history and data, they tailor their responses and recommendations to each customer interaction.

  • Efficient query handling: With custom AI chatbots, businesses can efficiently manage high volumes of customer inquiries. The AI chatbot handles routine questions, providing instant responses, while more complex issues are escalated to human agents, optimizing the overall customer service process.

  • 24/7 service availability: Custom AI chatbots ensure that customers have access to support services at any time instead of limiting chat hours to the availability of your human support staff. Offering constant availability can significantly enhance customer satisfaction.

  • Scalability and flexibility: Adding a cloud-powered AI chatbot to your customer support solution allows you to scale according to business needs. This flexible AI customer service solution adapts to varying customer interaction volumes.

  • Actionable insights for continuous improvement: Beyond handling customer interactions, Sendbird’s integrated solution collects valuable data to help your business better understand customer needs and continually improve your service offerings.

  • Seamless integration with business ecosystems: Sendbird’s customer service chatbot API is designed for easy integration with existing business systems so that you can ensure a cohesive and efficient customer service experience across all channels. Our AI-enabled Salesforce Connector allows you to get AI chatbot assistance using Sendbird Chat for a better experience from your support platform.

  • Advanced AI LLMs: At the heart of Sendbird's custom AI chatbots are advanced AI LLM integrations like ChatGPT, LLama 3, Claude Sonnet, and Solar. They allow the AI chatbot to understand and process natural language effectively, ensuring smooth, sophisticated, and human-like customer interactions.

Discover the full potential of AI in customer support with Sendbird’s customer service chatbot.

Case study Support mobile

Virgin Mobile UAE improved their CSAT with Sendbird Desk.

10 guidelines on how to craft an exceptional AI customer service chatbot

AI chatbot

Designing a chatbot that delivers an exceptional customer experience is no easy feat. Many businesses set out to develop a custom AI customer service chatbot internally, only to run into barriers with in-house resourcing, ongoing maintenance, and the continual upgrades required to keep up with industry developments. This is why companies often partner with a customer service chatbot provider, like Sendbird, specializing in this technology.

The goal is to create a chatbot that understands and responds to user needs and engages customers in a way that feels natural and helpful — upholding your brand standards while making your customers feel valued. Here are some guidelines to keep in mind to craft chatbots that excel in customer interactions:

1. Focus on Natural Language Processing (NLP)

The foundation of any effective AI customer service chatbot is its ability to understand and process human language. Robust NLP capabilities allow the AI chatbot to comprehend nuances, slang, and even typos in customer communication, helping keep interactions as smooth and natural as a conversation with a human agent. With advanced NLP, for example, an AI chatbot could likely identify a similarity between the questions “How do I change my billing address?” and “Where do I change my billing address?” and provide relevant information for both questions.

2. Ensure user-friendly chatbot design

The chatbot interface should be intuitive and straightforward. Aim for a clean, simple chatbot design with easy-to-navigate menus and clear prompts. Remember, not all users are tech-savvy, so make the chatbot easy to use for everyone by mimicking a familiar chat experience like SMS or WhatsApp. This involves creating a streamlined chatbot UI and a user-friendly experience as described. Sendbird offers multiple chat UI kits and chat SDKs to integrate chatbots into mobile apps and websites. We provide various widget integrations and a simple chatbot WordPress plugin for websites.

3. Personalize the chatbot interaction

Customize your bot through prompt engineering, function calling, bot logs, analytics, and content ingestion to tailor conversations based on the customer’s previous interactions, preferences, and purchase history.

4. Implement continuous learning and adaptation

A well-developed chatbot improves, ideally, after every interaction. AI chatbot software needs to allow chatbot managers to refine scripted answers for FAQs or improve chatbot workflows to optimize customer guidance. With engineering resources, companies can also post-train or “fine-tune” their AI chatbot LLMs to specialize in their business expertise. A simpler way is to update the AI chatbot knowledge base by refreshing the content ingested and improving prompts.

5. Ensure seamless escalation to human customer support agents

Despite advances in conversational AI, situations will always require human intervention. Your customer service chatbot should be able to seamlessly escalate complex or sensitive issues to human agents to avoid frustrated customers. Both Sendbird Desk and Sendbird’s Salesforce connector are AI-enabled live support agent software that combines custom AI chatbots and live support agents for customer service.

6. Feedback mechanism for improvement

Incorporate a feedback system where customers can rate their chatbot interactions. Then, based on this feedback, regularly update and refine the chatbot. If you deploy your chatbot on an app, consider sending business notifications to your app, or via SMS, or WhatsApp to ask for input or programming the AI chatbot to ask customers directly.

7. Cultural and linguistic adaptability

If your business serves a diverse customer base, ensure your chatbot can handle multiple languages and cultural nuances, ensuring inclusivity and accessibility. Remember that poorly translated bot interactions can be hilarious at best and seriously damage your brand reputation at worst. All generative AI LLM-enabled chatbots have exceptional multilingual capabilities, including AI chatbots that integrate ChatGPT, Llama, Claude, and Solar.

8. Proactive AI chatbot engagement

Instead of being purely reactive, a well-designed chatbot should also be able to initiate conversations, such as offering assistance or recommendations based on specific triggers or customer behaviors at just the right moment. A combination of scripted (workflows, suggested replies, custom answers) and AI-driven chatbot behavior helps get the best result.

9. Transparency, customer trust and safety

Be transparent with users interacting with a chatbot and give them easy access to terms. Demonstrating that the AI customer service chatbot adheres to privacy standards and data protection regulations is crucial for maintaining customer trust and safety. Referencing sources is also essential for users to check response accuracy.

10. Integration with your technology stack

A chatbot should not exist in isolation. Integrating it with your CRM, sales, and marketing systems can provide a more holistic view of the customer and help inform more significant business decisions. OpenAI’s function calling connects your custom AI chatbot with systems and first-party data. A highly abstracted chatbot API is also paramount for seamless integrations.

By focusing on these key aspects, your business can design a chatbot customer experience that meets the functional requirements and enhances the overall customer experience.

But what does a well-implemented customer service chatbot look like? Let’s explore some examples.

Top 10 customer service chatbots you need to try

1. Sendbird

As a custom AI chatbot solution provider, Sendbird launched its own customer service AI chatbot assistant, Clark. Clarks are among the smartest birds you can find in nature. Sendbird's Clark is a trained AI chatbot that can help Sendbird's website visitors learn about its communication platform for user-to-user chat, brand-to-user business messaging, or AI-powered communication. Clark is a knowledge AI chatbot and a lead generation AI chatbot at the same time that is engaging and helpful at the same time.

Sendbird AI chatbot example

2. Zoom

Zoom’s chatbot, present on its homepage and within customer accounts, assists users by immediately offering selectable options or quick replies based on their needs. This approach helps streamline the process of finding help or information and reduces the time to a resolution.

Zoom chat support
Adapted from Zoom

3. Cox

The chat icon on Cox's homepage offers a quick and easy way for consumers to engage with the company and receive fast answers or assistance. Its immediate accessibility and straightforward approach provide instant customer support when visitors land on the website. The chatbot's branding under the name Oliver also offers a customized, branded experience for Cox customers.

Cox chat support
Adapted from Cox

4. Notion

Notion’s chatbot for customers is called Q&A and is still in beta. It sources answers to user questions based on the information it can access, including all of the pages in a user’s space. It can also answer basic product questions while referring customers to the Help Center for more advanced product help. The integration of user content into the chatbot’s response system is a unique feature that offers more personalized assistance to users' workspaces.

Notion chat support
Adapted from Notion

5. T-Mobile

T-Mobile’s chatbot starts by segmenting requests between current and potential customers, streamlining the routing process. This streamlined approach ensures efficiency by quickly directing visitors to the most relevant resources.

T-mobile chat support
Adapted from T-Mobile

6. Amazon

Amazon’s chatbot for logged-in customers asks specific questions to better understand the inquiry and then offers two options for engaging with their support team. By pinpointing the nature of inquiries first, the e-commerce giant can provide more targeted and effective support.

Amazon chat support
Adapted from Amazon

7. Canva

Canva’s AI chatbot sets expectations upfront with a disclaimer about accuracy and terms of use, preparing users for the AI interaction. This preemptive communication helps manage user expectations and reduce potential misunderstandings or complaints.

Canva support chat
Adapted from Canva

8. Target

This big-box retailer offers a virtual assistant as one option for customer support. It begins by asking what the user needs help with, prioritizing assistance with Target orders, and includes a Terms of Use notice, similar to Canva.

Target support chat
Adapted from Target

9. PayPal

PayPal’s chatbot guides users on interacting effectively, stating its limitations and offering common question options. The bot’s upfront guidance and honesty about its learning process help foster a user-friendly and transparent support experience.

PayPal support chat
Adapted from PayPal

10. Etsy

Like several of the other AI chatbot examples we’ve explored, Etsy’s bot clearly states its terms of use and then helps users by asking them to specify their needs based on two options: buying or selling on Etsy. This enables it to efficiently segment user requests, allowing for quicker and more relevant assistance.

Etsy support chat
Adapted from Etsy

Two critical components in these examples are clear guidelines and options for users to select from, helping ensure a more efficient AI chat experience.

4 tips to ensure your AI customer service chatbot success

Introducing AI customer service chatbots requires careful planning and execution to be truly effective. Here are some tips to help with a smooth and successful implementation:

1. Integrate your customer service AI chatbot with existing channels

Make sure to integrate your chatbot software with your existing customer service channels. The chatbot should complement and enhance your support system, not operate in isolation. This integration ensures a cohesive customer experience, whether interacting with a human agent or a chatbot.

2. Familiarize staff with the AI chatbot

Training your staff to work alongside your customer service AI chatbot is equally essential. Employees should understand how the chatbot operates, the types of queries it can handle, and when to take over from the chatbot. Build clear rules for monitoring and supervising the chatbot for customer service, as it should not operate without human oversight.

3. Set clear objectives and expectations for customer service AI

Define what you want to achieve with your customer service AI chatbot. What are your primary goals and KPIs? For example, do you aim to reduce response times, handle common queries, provide 24/7 support, or more? Align clear objectives with chatbot key performance indicators (KPIs) for gauging success, which will help guide the AI chatbot implementation process.

4. Focus on continuous AI chatbot improvement

A customer service chatbot isn’t a set-it-and-forget-it tool. Regularly analyze chatbot interactions to identify areas for improvement through your chatbot analytics. Review customer feedback and chatbot performance data frequently to refine and update the chatbot’s responses and capabilities.

Remember, a chatbot is part of a larger customer service strategy, and its success should be measured by how well it enhances the overall customer experience.

Enhancing customer service AI chatbot efficiency through analytics

Upholding an optimal chatbot customer experience involves analyzing interactions and making data-driven improvements. Start by choosing which main metrics to track.

4 key customer service AI chatbot customer experience metrics to track

  1. User satisfaction rate: A straightforward indicator of how well the customer service AI chatbot meets customer needs, this metric is gathered through direct chatbot user feedback after interacting with the chatbot. A high satisfaction rate means your chatbot is on the right track, while a lower rate signals a need for adjustments.

  2. Resolution rate: This measures the percentage of queries the chatbot resolves without escalating to a human agent. The higher the resolution rate, the more effectively your chatbot handles customer inquiries independently.

  3. Passthrough rate: This measures the percentage of engagement that leads to a live support agent intervention.

  4. Conversation abandonment rate: How often users disengage or drop out of conversations with the chatbot. A high abandonment rate could indicate confusion, frustration, or dissatisfaction with the chatbot’s responses.

  5. Response time: The AI chatbot's response time measures how long it takes to respond to user queries. Aim for fast response times to help ensure a positive user experience. Of course, the response time should be balanced with the quality and relevance of the response.

Remember that the most crucial thing about metrics is how you act on the insights they provide. Follow these 5 steps to leverage your data fully:

  1. Identify patterns: Look for common trends or issues in AI customer service bot interactions. Are there specific points where users tend to drop off? Do certain questions lead to lower satisfaction rates? Adjust accordingly.

  2. Gather user feedback: Your chatbot software should have a simple way to gather direct user feedback, providing insights into issues that metrics alone might not reveal.

  3. Continuously test and optimize: Use A/B testing to try different approaches in the chatbot’s conversation flows. Analyze how these changes impact your chatbot’s key metrics and user satisfaction.

  4. Update content regularly: Keep the chatbot’s content up-to-date and relevant. This includes the information it provides and the style and tone of its communication.

  5. Train the AI: Use the data to train the chatbot’s AI engine, especially if built on machine learning algorithms. The more data it has, the better it can learn and adapt to user needs.

Regularly analyzing these metrics and making informed improvements can significantly enhance your conversational AI's performance and user satisfaction with customer service.

Real-world success stories of customer service AI: How AI chatbots transform the customer experience

Customer service AI is transforming customer support across various industries. Let’s examine case studies of Sendbird customers who have found success with customer service chatbots.


An image of neutral-colored furniture with the Redfin logo in the bottom right corner

Redfin, a leading real estate company, faced challenges in managing the complex information needs of homebuyers. Redfin partnered with Sendbird to develop a custom AI virtual assistant to enhance user engagement and streamline the home search process. This chatbot solution for web and mobile requires in-app chat and leverages Sendbird’s scalable and feature-rich chat experience. Introducing "Ask Redfin" improved communication efficiency, user engagement, and the number of meetings booked with real-estate sales agents. This transformative approach to real estate inquiries reinforced Redfin's commitment to innovation and customer satisfaction and set the bar higher than its competitors.


AllAthlete case study

AllAthlete, a sports recruitment app, struggled to facilitate effective communication between athletes, recruiters, and coaches. The app’s mission to democratize athletic recruitment relied heavily on maintaining active user engagement and efficient communication. Still, its initial AI chat solution wasn’t up to par—the chat solution deleted chats and changed users’ profile names without anyone knowing it.

To overcome these challenges, AllAthlete turned to Sendbird to build specialized ChatGPT-powered chatbots for a more reliable and user-friendly experience. With three different bots designed to guide users on platform usage, assist in athletic development, and bridge communication gaps between athletes and coaches, the technology has added a consistent, personal touch to the user experience.

With Sendbird, AllAthlete saw a 25% increase in user retention and saved 100+ hours with a more reliable, AI-enabled system. Founder Greg Auerbach says, “We’ve been live with Sendbird for a few months, and immediately, the solution's reliability has supported more quality conversations and a more trusted relationship with athletes that you just can’t put a price tag on.” Read AllAthlete’s full story.

5 ways the future of customer service AI will be transformed

The customer service landscape is rapidly evolving, with AI customer service chatbots leading this transformation. Several trends and innovations in AI technology will continue to change how businesses interact with their customers:

1. Advanced AI personalization

In the future, conversational AI for customer service will likely offer even more personalized experiences. By leveraging system integrations, data analytics, advanced data retrieval, prompt engineering, and LLMs’ progress, chatbots will be able to understand customer preferences and behaviors in greater depth to tailor conversations and solutions to each individual’s unique needs and a greater level of specificity.

2. Seamless human-AI collaboration

We expect a more integrated approach where chatbots and human agents work together. As the technology is still in its infancy, with more time to improve the process, AI chatbots will handle routine queries and gather preliminary information before seamlessly handing over more complex issues to human agents. This will take customer service AI to new heights.

3. Enhanced Natural Language Processing (NLP)

Future AI customer service chatbots will possess even more advanced NLP capabilities to understand and process human language with greater accuracy and nuance. This will make interactions with chatbots more natural and human-like, further reducing misunderstandings and improving overall communication. With sentiment analysis and an increasingly more significant understanding of human emotion, AI chatbots will better manage customers who are frustrated or dissatisfied.

4. Predictive customer service

AI customer service chatbots will increasingly use predictive analytics to anticipate customer needs and address issues before they arise, helping enhance customer satisfaction and loyalty.

5. Integration with IoT devices

As the Internet of Things (IoT) expands, AI customer service chatbots could play a pivotal role in interfacing with smart devices, providing customers with an integrated service experience beyond traditional channels.

The future of AI chatbots in customer service will be focused on creating more meaningful, efficient, and personalized customer interactions in an efficient way that balances out the human component. As AI continues to evolve, we can expect conversational AI for customer service to become an even more integral part of the customer support ecosystem.

Build a robust AI customer service chatbot today in a few clicks — no coding required

Given AI chatbot technology's rapid pace and ongoing evolution, the future holds even more potential for companies looking to integrate AI customer service chatbots into their business strategies.

Sendbird offers the #1 chat interface to integrate custom AI chatbots into mobile applications and websites. The easiest way to start for your website is to incorporate a widget using our WordPress Chatbot plugin or our ecommerce integrations. This is achievable within minutes through five simple steps that require no technical skills, and it won’t cost you anything via our free AI chatbot trial.

If you have questions, contact us! Our experts are always happy to help. 

Happy AI chatbot building!

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