Picture this: a customer reaches out to your business with a problem. Your customer service team jumps right in to assist them. But instead of resolving the customer’s issue, they waste precious time gathering information like the customer’s name, location, and order number—information that a bot could have gathered in seconds. These frustrating situations are all too common. They can happen when customer experience strategies lack organization or team members aren’t equipped with the right tools to assist customers efficiently. The good news is that generative AI can help businesses create a more efficient and effective customer experience. And if you're looking to take control of the process yourself, learning how to build AI for customer service can empower your team with custom solutions designed around your business needs. This blog will explore how generative AI customer experience solutions can help you boost personalization, improve customer satisfaction and loyalty, and enhance operational efficiency.
Lamatic's generative AI tech stack can help you achieve these objectives, so you can seamlessly implement an efficient, AI-driven customer experience that enhances personalization and boosts customer satisfaction, loyalty, and operational efficiency.
What Is Generative AI in Customer Support?
Generative AI creates new content based on input data, such as text, images, or audio. For instance, if a user prompts a generative AI tool with, “Write a poem about winter,” the AI will analyze its training data, which includes numerous poems and winter-related content, to produce an entirely original response. This ability to create novel content sets generative AI apart from traditional AI, which can only recognize patterns in existing data to make predictions.
The hyper-successful launch of ChatGPT has catapulted generative AI into boardrooms and living rooms alike. Generative AI has reignited interest in knowledge management, the number-one solution to improve customer experience, employee experience, and operating performance, per Gartner.
How Generative AI Works
Generative AI uses large language models to predict and create text based on the data and content it has been trained on. The more information these models have, the better they generate human-like responses.
For example, ChatGPT, a type of generative AI, was trained on 570 gigabytes of text data from the Internet, books, and other sources. As a result, it can produce remarkably accurate, coherent, and contextually relevant writing on seemingly endless topics.
Why Generative AI Matters for Customer Experience
Research from McKinsey reveals that 59% of customers expect businesses to personalize their experiences based on the available data. Companies must deliver faster, more personalized, and innovative customer experiences across various channels. Using customer service AI tools to meet customer expectations can positively impact revenue generation.
Here are some general statistics highlighting the benefits of AI customer service for organizations:
Salesforce
According to a study by Salesforce, 51% of service decision-makers reported that AI has increased their revenue.
Gartner
A Gartner report predicts that by 2025, AI technologies will be used in 95% of customer interactions, and companies that invest in AI customer experience solutions will see revenue increase by up to 25%.
PwC
In a survey conducted by PwC, 72% of business leaders believe that AI is a business advantage that will help them outperform competitors and increase revenue.
Accenture
According to a study by Accenture, 73% of customers are willing to pay more for a product or service if they receive a personalized experience. AI tools can enable businesses to provide personalized customer experiences, increasing customer satisfaction and revenue.
Harvard Business Review
A report by Harvard Business Review found that companies that leverage AI in customer service can achieve cost savings of up to 30% and experience revenue growth of up to 10%.
Generative AI for Customer Service
The use of AI for customer service has been introduced previously. It has been a vital ingredient of the broader domain of knowledge management, though some might consider it as two separate areas. As a knowledge management pioneer, we have leveraged AI technologies such as machine learning for intent inference and case-based reasoning for conversational guidance in the customer service context, creating transformational business value for our clients.
Generative AI is another exciting new ingredient that can be added to the knowledge management recipe that will enhance various aspects of knowledge management, elevating the business functions knowledge management serves:
- Customer service
- Sales
- Helpdesk
- HR and more
On the flip side, robust knowledge management is critical to the success of generative AI. Suppose generative AI is not part of your knowledge management strategy and tech stack. In that case, it will create yet another silo of inconsistent information, resulting in even more chaos for the business and the customer.
Speeding Up Knowledge Management With Generative AI
The knowledge management process can be broken down into the following essential steps:
- Create
- Curate
- Deliver
- Optimize
Generative AI can help automate or accelerate these steps to speed up time to knowledge and, thus, time to business value. Here are some early examples of use cases pertinent to the knowledge management process and customer service function: Just like anything else, customer service knowledge is garbage in, garbage out.
Content must be consumable, correct, and compliant with regulations and organizational best practices. Contact center agents and, worse, customers must read lengthy documents, although they may have accurate and compliant content, to find answers. This results in poor customer experience and agent experience. Generative AI can create drafts of more consumable and findable content using compliant and correct long-form documents as reference sources.
How Generative AI Is Reshaping Customer Experiences
Meet Customer Expectations with Generative AI
Today’s customers have higher expectations than ever. No technology has highlighted the need for organizations to stay agile and be proactive other than the rise of generative AI (Gen AI). In a world where customers can easily switch to a competitor—with research from Zendesk showing that 52% of customers will switch to a competitor after a single unsatisfactory customer experience—companies that fail to take action to meet new, higher expectations face losing out.
Gen AI is a valuable tool for meeting these expectations, enabling unique, personalized experiences and customer recommendations.
How Gen AI Is Reshaping Customer Experience
Research by Google Cloud has revealed that 97% of retail decision-makers believe that Gen AI will impact customer experience. As explained by Alex Rutter, managing director of AI GTM, EMEA at Google Cloud, for retailers already utilizing AI, the technology has redefined how they understand and engage with customers. “Synergistic advancements in data analysis and chatbot innovation have seen Gen AI elevate the customer experience in two key areas: personalization and automation,” he explains.
On a customer-facing level, Gen AI can unlock personalized shopping through AI-powered virtual agents. Chatbot agents can take the load off retail contact centers by answering customer queries with data-backed recommendations, ensuring customers receive seamless and efficient service.
Examples of Generative AI Improving Customer Experience
We are already seeing AI take flight in business and create meaningful customer experiences. Google Cloud partners are among those leading the charge, reaping this transformative technology’s benefits. Victoria’s Secret, for example, has launched an AI-powered search feature that leverages Google Cloud’s Vision API Product Search to locate specific products in the Victoria’s Secret catalog. Customers can use this tool by dropping an image into the website’s search bar. According to Jim Rudall, Head of EMEA at Intuit Mailchimp, personalization is key for organizations looking to enhance customer experiences.
“In order to set themselves up for success, businesses must prioritize customer experience through personalization. Understanding an audience is the crucial first step to this—and we know that 73% of customers feel more valued when they receive personalized emails. The next step is to use AI-powered tools to scale up customer communications. Such tools will massively augment the existing capabilities of businesses to engage at scale, raising the bar for what activities can be completed quickly whilst driving down the cost of marketing processes and improving ROI.”
Navigating Challenges and Ethical Considerations
Gen AI solutions and offerings reshape industries’ operational, functional, and strategic landscapes. Still, regarding ethics, Herbert explains that CEOs and leaders struggle to know what to do and where to start. “Before organizations begin, they must develop a trusted, responsible AI framework and put in place an ‘AI Centre of Excellence’ to act as air traffic control for any future experiments.”
This responsible AI framework, Herbert says, must cover things like ensuring that models are fair and unbiased, that they are transparent and can be explained, and that there is adequate corporate governance and accountability over data and its use. “This means thinking through how to safeguard consumers' data against unauthorized access, maintain the privacy of consumers’ personal details, and ensure that AI systems don’t have a negative impact on humans, property and the environment.”
The Future of Customer Experience and Generative AI
Consumers want meaningful and personalized experiences, but Rudall explains that it is important to balance this with privacy. Ultimately, personalization is only possible with data, but companies must ensure it's handled responsibly and used only in ways customers approve. “In a recent study of UK and US-based consumers, we found that nearly 3 in 4 customers are comfortable with companies using their personal data if they are transparent with how they are using it—and ultimately deliver a rewarding experience through impactful, personalized messaging.”
Gen AI to drive the evolution of CX Going forward, Rudall predicts that customers will expect far more personalization in how brands communicate with them. “The future of email marketing and customer experience is evolving to be hyper-personalized. AI is accelerating new methods for marketers to connect to their customers, and it will help them tailor their communications for better engagement and impact. This allows teams to focus human creativity towards the ‘why’ and ‘what’ of communication, whilst AI handles the ‘how.’”
Hyper-Personalization and Generative AI
According to Conway, hyper-personalized journeys created by Gen AI promise to transform how companies connect with customers and employees totally. “Using true 360-degree customer data from across sales, marketing, and service functions, it can tailor experiences and determine the “next best action” that will help a company engage a specific customer.” He says.
“For example, a financial services company could use Gen AI to rapidly analyze their own customer data—as well as data from social sources and partner organizations—to determine which customers are most likely to take various actions, from opening a new checking account to investing assets to applying for a loan. Gen AI can then help bankers achieve true one-to-one marketing with a personalized strategy and automated, point-in-time customized offers, translated into the customer’s preferred language.”
Generative AI is the Future of Customer Experience
Research shows that 73% of customers worldwide expect brands to understand their unique needs and expectations. Rutter also advocates a 360-degree approach to customer service, starting with a strong customer insight foundation. “A data-driven approach to retail management helps brands better understand trend forecasts and custom journeys, ensuring that the shopping experience is catered to each customer and their unique needs,” he says. Gen AI is the first technology to integrate front-line service with internal data processing, resulting in retail systems engineered for memorable customer engagement.
Soon, AI-powered retail will become the industry standard, meaning businesses relying on outdated customer service models risk being left behind. Gen AI is adding a new dimension to customer-brand relationships by matching customer expectations with innovative retail solutions.”
Lamatic’s All-in-One Generative AI Tech Stack for Seamless Integration and Deployment
Lamatic offers a comprehensive Generative AI Tech Stack. Our solution provides:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge deployment via Cloudflare workers
- Integrated Vector Database (Weaviate)
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities.
Start building GenAI apps for free today with our generative AI tech stack.
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21 Use Cases for Generative AI in Customer Service
1. Automated Email Responses: Speedy, Personalized, and Efficient
Generative AI can analyze incoming customer emails and generate personalized responses, reducing response times and improving efficiency. Example: A retail company uses Generative AI to handle customer inquiries received via email.
When a customer sends an email asking about product availability, the AI system analyzes the query and generates a response, providing real-time information on the product’s availability.
2. Chatbots for Instant Support
Intelligent chatbots powered by Generative AI engage with customers in real time, providing instant assistance and resolving queries efficiently. For example, a telecommunications company implements a chatbot on its website to assist customers with service-related inquiries.
When a customer initiates a chat, the chatbot uses Generative AI to understand the query and provide relevant information, such as troubleshooting steps for connectivity issues.
3. Virtual Assistants
Generative AI-driven virtual assistants offer personalized support, guiding customers through various processes and providing recommendations. For example, an airline company introduces a virtual assistant on its mobile app to assist travelers with flight bookings. The virtual assistant utilizes Generative AI to interact with users, helping them find suitable flight options based on their preferences and travel dates.
4. Dynamic FAQs: The FAQ of the Future
Generative AI dynamically generates and updates FAQ sections on websites or apps, ensuring customers can access relevant information. For example, a banking institution employs Generative AI to maintain its FAQ page. As customers ask questions or search for information, the AI system analyzes the queries and generates concise and accurate answers, updating the FAQ section in real-time.
5. Social Media Engagement
Generative AI automates responses to customer inquiries on social media platforms, enhancing engagement and maintaining an active presence.
Example: A hospitality chain uses Generative AI to manage customer interactions on its social media pages. When a customer posts a question or complaint on Facebook, the AI system generates a personalized response, addressing the inquiry and providing assistance.
6. Voice-based Customer Support
Generative AI customer support models integrated into interactive voice response (IVR) systems provide natural and human-like responses to customer queries over the phone.
Example: An insurance company implements a voice-based customer support system powered by Generative AI. When a customer calls to inquire about policy details, the AI system engages in a conversation, understanding the query and providing relevant information in a natural-sounding voice.
7. Product Recommendations
Generative AI analyzes customer preferences and browsing history to generate personalized product recommendations, enhancing cross-selling and upselling opportunities. Example: An e-commerce platform utilizes Generative AI to recommend products to customers based on their past purchases and browsing behavior.
When a customer visits the website, the AI system generates personalized recommendations, increasing the likelihood of successful conversions.
8. Sentiment Analysis
Generative AI algorithms analyze customer feedback and sentiment in real time, enabling businesses to proactively address concerns and improve satisfaction.
Example: A hospitality company monitors customer reviews using Generative AI-powered sentiment analysis tools. When negative reviews are detected, the AI system generates alerts, prompting the customer service team to respond promptly and resolve the issue.
9. Multi-language Support
Generative AI-powered translation services facilitate seamless communication with customers in different languages, breaking down language barriers.
Example: A global software company implements a multi-language support system powered by Generative AI. When a customer submits a query in a language other than English, the AI system translates the message and generates a response in the customer's preferred language.
10. Automated Transcription
Generative AI can accurately transcribe customer calls or messages, enabling businesses to review and analyze interactions for quality assurance purposes.
Example: A financial institution uses Generative AI to transcribe customer calls to its support center. The AI system converts audio recordings into text, allowing supervisors to review conversations and identify areas for improvement in customer service delivery.
11. Complaint Resolution
Generative AI assists customer service agents by suggesting relevant solutions or responses to common complaints, speeding up resolution times.
Example: a telecommunication company equips its support agents with AI-powered tools that analyze customer complaints and generate suggested responses. When a customer calls to report a network outage, the AI system suggests troubleshooting steps for the agent to follow, expediting the resolution process.
12. Personalized Marketing Campaigns
Generative AI generates tailored marketing content based on customer data, enabling businesses to deliver targeted and engaging campaigns.
Example: a fashion retailer leverages Generative AI to create personalized email marketing campaigns. The AI system analyzes customer purchase history and preferences to generate customized promotional offers, increasing engagement and sales conversion rates.
13. Content Creation
Generative AI assists in creating blog posts, articles, or social media content, saving time and resources while maintaining quality and relevance.
Example: a technology company uses Generative AI to generate blog posts for its website.
The AI system analyzes industry trends and news articles to create informative and engaging blog content, attracting readers and driving website traffic.
14. Knowledge Base Generation
Generative AI algorithms automatically analyze internal documents and customer interactions to generate comprehensive knowledge bases for support agents.
Example: a software company utilizes Generative AI to build a knowledge base for its support team. The AI system analyzes product documentation and customer queries to generate a repository of troubleshooting guides and FAQs, empowering agents to provide accurate and timely assistance to customers.
15. Training Simulations
Generative AI-powered simulations replicate real-life customer scenarios, providing immersive training experiences for customer service representatives.
Example: A retail company uses Generative AI to develop training simulations for its support staff. The AI system generates simulated customer interactions based on everyday scenarios, allowing agents to practice communication and problem-solving skills in a controlled environment.
16. Automated Surveys
Generative AI designs and deploys surveys to gather customer feedback, enabling businesses to make data-driven decisions and improvements.
Example: a hospitality chain uses Generative AI to create customer satisfaction surveys. The AI system generates survey questions based on predefined criteria and analyzes responses to identify areas for improvement in service quality and guest experience.
17. Predictive Analytics
Generative AI models analyze historical data to predict customer behavior and preferences, empowering businesses to anticipate needs and tailor their services accordingly.
Example: An online retail platform employs Generative AI to predict customer purchase patterns. The AI system analyzes past shopping behavior and market trends to generate forecasts of future product demand, helping the company optimize inventory management and marketing strategies.
18. Fraud Detection
Generative AI algorithms detect patterns indicative of fraudulent activities, helping businesses mitigate risks and protect customers from security threats.
Example: a financial services provider implements Generative AI-powered fraud detection systems.
The AI algorithms analyze transaction data and user behavior to identify suspicious patterns and flag potentially fraudulent activities, preventing unauthorized access and financial losses.
19. Appointment Scheduling
Generative AI-powered chatbots assist customers in scheduling appointments or bookings, simplifying the process and reducing administrative overhead.
Example: a healthcare clinic integrates a chatbot on its website to handle appointment scheduling.
The chatbot uses Generative AI to interact with patients, guiding them through the booking process and offering available appointment slots based on their preferences and the clinic's availability.
20. Automated Billing Support
Generative AI handles billing inquiries and assists with payment processes, ensuring a smooth and hassle-free customer experience.
Example: A utility company employs Generative AI to manage billing inquiries from customers. When a customer calls to inquire about a billing discrepancy, the AI system analyzes the account information and generates a response explaining the charges and resolution steps.
21. Continuous Improvement
Generative AI enables businesses to iteratively improve their customer service processes and offerings by analyzing customer interactions and feedback.
Example: An e-commerce platform uses Generative AI to analyze customer feedback and identify areas for improvement in its support services. The AI system aggregates input from various channels, such as surveys and social media, and generates insights to guide strategic decision-making and service enhancements.
7 Steps to Implement Generative AI in Customer Service
1. Evaluate Your Customer Service Needs
Before you can implement generative AI for customer service, it’s essential to understand the specific challenges your customer service team faces. This initial step involves:
Analyze Current Challenges
Identify the pain points in your existing customer service processes.
- Are customers frequently experiencing long wait times?
- Are there common inquiries that could be handled more efficiently?
Recognizing these issues will help you pinpoint where AI-driven customer support can be most beneficial.
Identify AI Opportunities
Focus on areas where generative AI can significantly improve the customer experience, such as improving queue management, reducing response times, and personalizing interactions. For instance, deploying an AI-powered chatbot could effectively manage high volumes of common queries, freeing human agents to handle more complex issues.
Gather Input from Staff
Engage with customer service agents and managers to understand their perspectives on the current system. Their insights can reveal areas where generative AI might improve efficiency or resolve specific pain points. This collaborative approach ensures that the AI solution aligns with the team’s needs and complements their workflows.
2. Choose the Right Generative AI Solution
After assessing your needs, the next step is selecting the most suitable generative AI platform. With various options available, it’s essential to evaluate them based on several criteria:
Integration Capabilities
The AI solution should seamlessly integrate with your existing customer service systems, whether CRM software, chat platforms, or email systems. Look for solutions that offer easy integration to avoid disruptions during implementation.
Scalability and Customization
Choose a platform that can scale as your business grows and offer customization options to tailor the AI’s responses and functionalities to your specific needs. This flexibility ensures that the AI can adapt to the evolving requirements of your customer service operations.
Language Understanding and Response Generation
Assess the AI’s ability to comprehend customer queries and generate accurate, contextually appropriate responses. The effectiveness of AI-driven customer support heavily depends on the quality of its language processing capabilities.
Data Privacy and Security Compliance
Ensure the AI solution complies with your company’s data privacy policies and relevant regulations. Generative AI customer service systems will handle sensitive customer information, prioritizing security.
3. Train and Fine-Tune the AI Model
Training and fine-tuning the AI model are crucial to ensuring your generative AI performs effectively in customer service. This process involves:
Gather Relevant Data
Collect customer service data such as chat logs, email exchanges, and call transcripts. This data will serve as the foundation for training the AI model, helping it learn the specific language and nuances of your customer interactions.
Data Cleaning and Categorization
Cleaning and categorizing data to provide context is essential before feeding it into the AI model. This step ensures that the AI understands the different types of customer inquiries and can respond appropriately.
Continuous Fine-Tuning
Once the initial training is complete, fine-tune the model by incorporating new data and feedback from customer interactions. This ongoing process helps improve the AI’s performance and accuracy over time.
Testing AI Responses
Before full deployment, thoroughly test the AI model’s responses for accuracy, relevance, and tone. Ensuring that the AI communicates in a way that aligns with your brand’s voice and meets customer expectations is critical.
4. Integrate Generative AI into Your Workflows
Integrating generative AI into your existing customer service workflows requires careful planning and execution to ensure a smooth transition:
Easy Integration
Work closely with your IT team to integrate the AI solution into your current systems, whether your CRM, live chat, or email platforms. The goal is to boost, not disrupt, your current processes.
Guidelines and Protocols
Develop clear guidelines for when and how AI-powered assistance should be used. This includes determining which types of inquiries the AI should handle and when human agents should take over. A well-defined AI-based customer flow system helps maintain consistency and quality in customer interactions.
Agent Training
Provide comprehensive training to your customer service agents on the capabilities and limitations of the generative AI system. Agents should understand how to work alongside the AI, when to intervene, and how to provide feedback to improve the system.
Encouraging Feedback
Encourage agents to provide feedback on the AI’s performance to further a culture of continuous improvement. Their insights help identify areas where the AI might need further fine-tuning or adjustments.
5. Monitor and Optimize Performance
Once the generative AI system is up and running, ongoing monitoring and optimization are necessary to ensure it continues to deliver value:
Establishing KPIs
Set key performance indicators (KPIs) to measure the impact of AI-powered customer service. Common KPIs include customer satisfaction scores, first contact resolution rates, and agent productivity. These metrics will help you gauge the effectiveness of the AI system.
Continuous Monitoring
Monitor the customer service experience regularly to identify issues or areas for improvement. This includes gathering customer and agent feedback about their interactions with the AI.
Performance Analysis
Analyze the AI’s performance data to pinpoint areas where it might be underperforming or adjustments are needed. This could involve tweaking the AI’s algorithms, updating its knowledge base, or refining its response strategies.
Knowledge Base Updates
Review and update the AI’s knowledge base regularly to ensure it remains current and relevant. An up-to-date knowledge base is crucial for maintaining the accuracy and usefulness of AI-driven customer support.
6. Communicate the Benefits to Customers
To maximize the effectiveness of your generative AI system, it’s essential to communicate its benefits to your customers clearly:
Educating Customers
Inform customers about the availability and advantages of AI-powered customer service. Explain how it can help them get quicker, more accurate responses and what types of inquiries the AI can handle.
Guidelines for Interaction
Provide clear instructions on interacting with AI, including when and how to escalate to a human agent. This transparency helps set customer expectations and ensures a smoother experience.
Gathering Customer Feedback
Collect customer feedback about their experience with AI-powered support continuously. Address any concerns or issues promptly to maintain trust and satisfaction.
7. Ensure Ethical and Responsible AI Deployment
As with any AI technology, it’s essential to implement generative AI in a way that is ethical and responsible.
Developing Ethical Guidelines
Create and enforce ethical guidelines for using generative AI in customer service. These guidelines should address potential issues such as bias, transparency, and the ethical implications of AI decision-making.
Addressing Bias and Transparency
Ensure that the AI model’s responses are free from bias and that the system operates transparently. Customers should understand how their data is being used when interacting with AI.
Compliance with Data Protection Regulations
Ensure your generative AI solution complies with all relevant data protection regulations, such as GDPR or CCPA. This includes implementing robust security measures to protect customer data and maintaining transparency about data usage.
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8 Best Low Code GenAI App Builder
1. Lamatic
Lamatic provides a robust generative AI tech stack, including managed GenAI middleware, custom GenAI APIs (GraphQL), low-code agent builders, automated GenAI workflows, and integrated vector databases. Their platform automates workflows and ensures production-grade deployment on the edge, enabling rapid, efficient GenAI integration for products needing swift AI capabilities.
2. Softr
Softr tops Zapier's list of no-code app builders because it's easy to use without sacrificing functionality. It’s the fastest way to go from the prompt window to an app close to ready to use. Start on the Softr AI page by selecting a category and entering what you’d like your app to do. After you send in your prompt, Softr captures the gist of what you want, assigns a name to your app, and lets you choose a starting color and design look before it begins generating.
3. Microsoft PowerApps
Microsoft is rolling out Copilot features across its product lineup, and PowerApps is no exception. Featured in Zapier's list of the best internal tool builders, PowerApps is already a powerful app builder offering deep functionality without requiring coding knowledge. It uses a function-based language for app logic, close to the one available in Excel. In the PowerApps dashboard, you can now start a new app from a prompt.
I suggest beginning with a small description of what you’re looking for. After submitting the prompt, you’ll see the generated database schema. A chat window on the right lets you add, remove, or change the fields with prompts. Once you save this, PowerApps will assemble the main page to display and interact with this database. Starting small is better because you can only generate the starting point, not the entire app.
4. Google AppSheet
Spreadsheets are the birthplace of valuable tools, from simple data storage to productivity systems. AppSheet, created by Google and featured in Zapier's list of the best database-powered app builders, takes your spreadsheet and generates an app based on it, complete with a data display and a few forms to help you create new entries. There’s no prompt window anywhere. Most of the AI magic happens in the background as the engine reads your rows and columns to identify what your app is about.
The structure of your sheet dictates the elements you’ll see on the screen. Customization also has very little AI; there’s no sidebar to enter prompts. The control panels are a bit more complex to understand initially, but there are many possibilities. You can leverage smartphone features, such as QR-code reading, which is excellent for creating site-specific tools. You can also train an AI model on your spreadsheet, helping you add prediction features to your apps, or use other tools such as character recognition and intelligent document processing.
5. Quickbase
Quickbase's AI Smart Builder creates internal tools adapted to your prompt with accuracy. Still, beta accepts a more detailed prompt than other options. You’ll start by entering your industry, describing your app's goals, and listing things you want to track, report on, or manage.
Are you having trouble coming up with the perfect prompt? You can click the link on the generation pop-up to access instructions and examples. Once you lock in on the prompt and click create, grab a cup of coffee. Quickbase can take up to 10 minutes to build the tables and user interface.
6. Pico
We're now moving on to the new kids on the block. Pico is the first one, offering an experience that feels like magic. When you add your starting prompt, a line appears on the screen showing the AI generating the code in real-time. It flows between HTML, CSS, and JavaScript with enviable ease, and on the side, your app comes to life bit by bit. With this starting point, you can write more prompts to change anything about the app.
You can change button labels, the background, and the input fields showing on the screen. A few settings help you change the style, visual properties, and connected data—and if you know how to code, you can jump in at any point and edit it yourself. If you don't, don't worry: English is its main programming language.
7. Construct
Construct leans on simple, minimalistic apps. When you start with your prompt, it prepares a product requirement document, a data model, and the interface components. After a minute, your one-page app opens with buttons and lists to display data and input forms.
Visually, the results are always the same: black outlines for all elements, be they lists or buttons. If you try to write a prompt to change any app's visual aspect, it'll say it can't do that. At first, this felt frustrating, but it may be for the best: the baseline design is clean and straightforward, and the focus on functionality makes sense.
8. Create
Where other apps in this list use a combination of a starter prompt and additional ones to continue building, with Create, you'll use a single prompt to generate the whole thing. This is its main advantage and, sometimes, its most significant drawback. You'll notice the input field on the right, inviting you to write your prompt.
Instead of compiling your app's first draft here, I suggest you write it in a separate document. Use ChatGPT to describe features: you can ask to "describe the user interface, including visual and interactive elements with extreme detail" to help Create do the job well.
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Generative AI App Builders vs. Building AI Features Into Apps
Generative AI app builders help you build apps, like chatbots or apps that summarize text faster. They let you use AI to automate parts of the app-building process so you can go from idea to launch more quickly. Once you deploy your app, you can get user feedback and improve it over time with the help of AI.
OpenAI’s ChatGPT, for instance, can help you write better prompts and even code to enhance your application. In contrast, integrating AI into existing applications can help improve the software’s performance and user experience. It also can make your app smarter, as it learns from real-world data over time. The two options have a clear difference, so it’s essential to understand them before diving into app development.
Pros and Cons of Using Generative AI App Builders
Building apps with dedicated generative AI app builders can significantly speed up development. These app builders let you implement AI to automate parts of the app-building process. For example, you can use AI to help you write code or create content for your application.
Using a Dedicated Generative AI App Builder
Doing so can help you get to the finish line faster or at least improve the quality of your app by allowing you to focus on more important features. You can deploy your app sooner and use AI to help you improve it once it’s live. Users can provide feedback that helps you fine-tune your generative AI app; the AI can even help you implement the changes.
Considerations When Using a Dedicated Generative AI App Builder
The main downside of using a dedicated generative AI app builder is that it may create a separate application that doesn’t integrate well with your existing software. Apps built with AI help can also feel different to users since they often rely on AI to function.
Start Building GenAI Apps for Free Today with Our Generative AI Tech Stack
Lamatic provides an extensive generative AI tech stack. The platform's features include:
- Managed GenAI middleware
- Custom GenAI API
- Low-code agent builder
- Automated GenAI workflow
- GenOps
- Edge deployment via Cloudflare Workers
- Integrated vector database (Weaviate).
Managed GenAI Middleware
Managed GenAI middleware acts as a bridge between users' applications and the generative AI model. It handles communication and optimizes performance to ensure smooth operation. Lamatic's managed middleware eliminates the tedious setup and configuration process typically associated with GenAI integration.
Custom GenAI API (GraphQL)
Lamatic's custom GraphQL API allows users to customize the data requests sent to their GenAI model during application runtime. This feature optimizes performance and enables faster response times. The custom API also supports streamlined integration with existing applications and frameworks.
Low-Code Agent Builder
Lamatic's low-code agent builder allows users to create and customize GenAI applications without extensive programming knowledge. Users can build unique agents through an intuitive user interface that easily configures the agent's behavior and personality. This feature accelerates the development process and allows teams to focus on building applications that meet their specific business needs.
Automated GenAI Workflow (CI/CD)
Lamatic's automated GenAI workflow helps users manage GenAI application development and deployment through continuous integration and continuous delivery (CI/CD). This feature streamlines updating applications with new model iterations and performance optimizations. With Lamatic, users can automate workflows to ensure stability and production-grade performance for their GenAI applications.
GenOps (DevOps for GenAI)
GenOps, or DevOps for generative AI, focuses on improving the development and deployment of GenAI applications. Like DevOps, GenOps combines development and operations to improve efficiency and reduce time to market. Lamatic's GenOps features provide the tools to help teams implement GenAI solutions quickly and effectively.
Edge Deployment via Cloudflare Workers
Lamatic's generative AI tech stack is built for performance. The platform's edge deployment capabilities via Cloudflare Workers enable users to deploy their GenAI applications closer to their end users, reducing latency and improving performance. Deploying GenAI applications at the edge ensures fast load times and optimal application performance for users regardless of their location.
Integrated Vector Database (Weaviate)
Lamatic's generative AI tech stack includes Weaviate, an open-source vector database designed to store unstructured data and make it easily searchable. GenAI models create unstructured data, and as applications run, they can retrieve relevant information from a vector database to optimize performance and improve response accuracy. Weaviate can be integrated with other applications and frameworks, making it a powerful tool for enhancing GenAI applications.
