Imagine walking into your favorite store where a friendly employee greets you by name and mentions the specific item you came to buy. Then, as you make your way to that section of the store, a digital display lights up with a personalized offer for the exact product you were looking for. This is the magic of AI-powered personalization powered by multimodal LLM. Research shows that 91% of consumers are likelier to shop with brands that recognize, remember, and provide relevant offers and recommendations in real time. Not only does this personalization improve the overall shopping experience for customers, but it also boosts business revenue. This article will explain how AI-powered personalization works, why it matters, and how to implement AI-powered personalization strategies and tools for your business successfully.
It will also introduce Lamatic’s generative AI tech stack as a valuable solution to help you achieve your objectives. In no time, you’ll be on your way to improving customer engagement, boosting operational efficiency, and driving business growth.
What is AI-Powered Personalization?
AI-powered personalization is a method that leverages artificial intelligence to automate the personalization process. It uses data to determine individual preferences and create relevant, personalized experiences that improve over time.
When a user interacts with an AI system, it learns from that behavior and adjusts its future responses to better meet the users’ needs. Over time, these adjustments help build user trust and improve the overall experience. The more accurate and predictive these responses can be, the more useful and valuable they become to the individual.
Why AI Personalization Matters
Personalized customer experiences have become the basis for competitive advantage. It’s not as simple as just using a first name on an email. Consumers expect organizations to deliver personalized interactions and over three-quarters (76%) get frustrated when this doesn’t happen.
Delivering a personalized experience requires every physical and virtual touch point to be designed to speak directly to an individual. But it can be incredibly challenging to achieve that at scale. That’s why over 9 in 10 (92%) organizations are looking at AI for personalization it connects customers with what they want, which creates endless new paths to purchase, greater profitability, and fast business growth.
AI Personalization Applications
Artificial intelligence is currently involved in several marketing areas:
- Customer segmentation
- Data analysis
- Content personalization
- Real-time customer interactions
With larger data sets, AI tools can predict behaviors, understand preferences, and instantly deliver highly targeted experiences. McKinsey reports that companies that use AI personalization have increased sales by 20% or more. Thus, the role of AI in helping companies communicate with their audiences becomes central.
The Journey to AI Personalization
While the journey to highly personalized marketing experiences has been gradual, it started with the success of loyalty programs and email campaigns. These first ideas segmented audiences by basic data such as purchase history or demographics, which laid the groundwork for the upcoming improvements.
They weren’t providing customers with an individual approach and personalized experience. But by automating the personalization process, AI has completely changed this and allowed businesses to reach the next level regarding relevance in their marketing efforts.
AI Advances Revolutionizing Personalization
Due to the AI’s ability to process and analyze massive amounts of data, machine learning, and predictive analytics have enabled brands to overcome traditional segmentation. With AI, brands don’t categorize customers by general traits such as demographics or age but instead analyze their actions and preferences in real time. In industries like retail and e-commerce, such a personalized approach is crucial. We can see it in the examples of Amazon and Netflix, which are approaching marketing in more scalable and personalized ways, transforming how brands interact with customers.
For example, Amazon’s recommendation system that monitors customer behavior brings in 35% of the company’s total revenue, showing how AI impacts personalized customer engagement. Personalization isn’t limited to product suggestions. It helps facilitate dynamic interactions, from the first visit to a website to subsequent post-purchase communications. With each interaction, AI systems learn from how customers behave and refine their predictions, making future interactions more relevant, strengthening customer relationships, and boosting lifetime value.
The Results of AI Personalization
The results are compelling as AI advances and is increasingly integrated into marketing. Businesses that use AI for personalized marketing have seen a 15% profit increase. AI-driven email campaigns have 41% higher click-through rates and 29% higher conversion rates than non-personalized ones. These figures demonstrate how impactful AI can be on some of the biggest metrics associated with marketing: customer engagement and conversion.
Types of AI-Driven Personalization
On a large scale, AI-driven personalization has changed the game for brands interacting with customers by providing personalized content, recommendations, and personal interaction. Each strategy relies on different AI models, which offer different ways to talk to customers.
Predictive Personalization
Predictive personalization uses predictive analytics to anticipate customer needs, actions, or behaviors before they occur. Using past data to predict future behavior helps brands create highly relevant content, product suggestions, marketing messages, and more. Predictive personalization has already shown its potential to reduce customer churn, improve product recommendations, and boost sales.
Reducing Customer Churn
Predictive models are particularly effective when it is necessary to identify the customers who are about to leave a platform or stop engaging with a brand. For example, Netflix uses predictive analytics to track viewing habits, behavior, and engagement patterns and flag subscribers at risk of canceling their service.
With AI-powered personalized recommendations, Netflix reduced customer churn by 5%, saving $1 billion in annual customer subscription revenue. Companies such as Vodafone and AT&T also use predictive analytics in the telecom sector to detect at-risk customers. The analytics are based on usage trends, payment behaviors, and customer interactions with customer service. Studies show that businesses that apply these strategies can cut churn rates by up to 40%.
Improving Product Recommendations
Predictive analytics in the retail industry allow businesses to analyze customers’ past behavior and use the data to predict what products they are interested in and inclined to buy. Amazon, for example, uses predictive models to analyze its customers' purchase history and browsing behavior and then generates personalized product recommendations.
As a result, it significantly contributes to Amazon’s revenue; these AI-powered suggestions comprise 35 percent of Amazon’s sales. Walmart also incorporates predictive personalization by analyzing customer purchasing and online browsing habits to offer personalized product suggestions and targeted promotions. The quality data on customer preferences helped increase online and in-store purchases.
Boosting Sales Across Industries
Predictive personalization has been shown to increase sales in all sectors significantly. A McKinsey study found that companies that use predictive analytics in their marketing observe a 10 percent to 15 percent rise in sales. Predictive models benefit from such industries as:
- Retail
- E-commerce
- Financial services
- Even healthcare
These tools help businesses offer more personalized services, improving customer engagement and conversions. Predictive personalization is also useful in the travel and financial services sectors. In travel, platforms like Expedia use predictive models to recommend flights, hotels, and activities based on user behavior. In contrast, financial institutions use predictive analytics to offer personalized investment advice or loan products based on a customer’s financial profile.
Dynamic Personalization
Dynamic personalization is real-time personalization that adapts to the user’s actions, preferences, and interactions with an advertisement. This type of personalization is especially effective in digital marketing channels because content, offers, and recommendations can be instantly adjusted to match user behavior and preferences.
Dynamic Personalization in Digital Channels
Dynamic personalization can be effectively used in digital marketing, allowing a brand to edit website content, email campaigns, and advertisements in real time. Spotify is one example of how effective real-time personalization can be. Spotify offers personalized playlists, song recommendations, and a user interface that is updated in real-time based on users' listening habits.
With over 433 million users, Spotify shows how customer-centric AI can help increase revenue, engagement, and user experience with AI-driven personalization. Dynamic personalization can also be done through targeted ads on social media platforms like Facebook and Instagram. They use their AI systems to customize ads to customers’ preferences based on how users interact with it, whether they like, share, or comment. When using dynamic personalization, brands can:
- Optimize their marketing budget
- Increase the quality of targeting
- Improve the overall performance of a marketing campaign
AI-powered Recommendations
Recommendation engines that AI powers are significant for personalized marketing because they provide suggestions of products, content, or services based on users' past behavior and preferences. These systems rely on three recommendation techniques:
- Collaborative filtering
- Content-based filtering
- Hybrid models
Collaborative Filtering
Recommendations by collaborative filtering are popular and provide suggestions based on user-item interactions. It considers the user’s preferences and then predicts what a user would enjoy based on the likes of similar users.
Netflix and YouTube were the first adopters of collaborative filtering. Netflix’s recommendation engine uses this technique, generating 80% of all the content watched on the platform.
Content-Based Filtering
Content-based filtering suggests content similar to what a user has already interacted with or liked. For example, Spotify’s recommendation engine suggests songs based on the characteristics of tracks a user has previously enjoyed.
Spotify provides highly personalized song recommendations by analyzing:
- Genre
- Tempo
- Mood features
It allows the platform to keep users interested for longer and increases engagement with its content.
Hybrid Models
A hybrid recommendation system combines the best collaborative and content-based filtering to improve the accuracy and effectiveness of advertisements. One example of a hybrid model is Amazon’s recommendation engine, which combines user behavior (through collaborative filtering) with product attributes (via content-based filtering) to suggest items.
Part of Amazon’s success is claimed to be due to this approach, as 35% of Amazon’s total revenue came from its recommendation engine. In the same way, YouTube uses a hybrid recommendation system, which combines collaborative filtering to provide users with videos based on user interaction and content-based filtering using video metadata availability. Considering the number of videos on YouTube and the overall content, such an approach is crucial for keeping users on the platform. This also prevents users from being lost on the platform, presenting content that suits what they want to see.
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9 AI-Powered Personalization Strategies for Efficiency
1. Leverage First-Party Data to Gain Insight on Your Customers
First-party data is information collected directly from customers through website interactions, social media, and emails. This data helps businesses understand customer behavior, preferences, and interests. Using first-party data can help businesses save money and work more efficiently. It allows for targeted marketing, reducing waste, and optimizing resources. It also helps identify and fix gaps in the customer journey, improving satisfaction and loyalty.
First-party data is key to engaging customers. Analyzing this data, businesses can create personalized content, offers, and recommendations that resonate with their audience. This boosts engagement, loyalty, and revenue. To use first-party data for personalization, follow these steps:
- Collect Data: Gather data from website analytics, social media, and emails.
- Analyze Data: Identify customer segments, behavior patterns, and preferences.
- Create Campaigns: Develop targeted marketing campaigns and personalized content.
- Monitor and Optimize: Continuously check and improve the personalization strategy for the best results.
2. Dynamic Creative Optimization (DCO) for Efficient Campaigns
DCO uses data from customer profiles, browsing behavior, purchase history, and real-time interactions to create personalized ads. DCO algorithms analyze this data to find the best combination of ad elements for each viewer.
DCO automates the creation and optimization of ads, saving time and resources. This allows advertisers to launch and adjust campaigns quickly, making the most of their ad budget. Personalized ads tailored to individual preferences and contexts increase customer engagement. These ads are more likely to grab attention, generate interest, and lead to conversions. To use DCO effectively, follow these steps:
- Integrate Data Sources: Connect data sources like customer databases, website analytics, and third-party data providers to your DCO platform.
- Define Audience Segments: Identify key audience segments based on demographics, behavior, interests, and other criteria.
- Create Ad Assets: Develop various ad assets, including images, text, and calls-to-action, to be dynamically assembled.
- Set Optimization Rules. Define rules for the DCO algorithms to select and assemble the best ad combinations for each audience segment.
- Monitor and Optimize: Regularly check campaign performance metrics, such as click-through and conversion rates, to identify opportunities for improvement.
3. Integrating Generative AI with Human Curation
Combining generative AI with human curation is a strong strategy for personalization. This mix uses AI to create content and humans to refine it, making the content more engaging. Generative AI looks at large amounts of data to find patterns and trends, helping create content that fits the audience's interests.
Human curation ensures the content is accurate and connects emotionally with the audience. This approach speeds up content creation, allowing businesses to produce high-quality personalized content quickly. It saves time and resources, letting businesses focus on other important tasks. Content made by both AI and humans is more likely to engage customers deeply.
This mix of technology and human touch creates informative and emotionally appealing content, boosting customer loyalty. To use this strategy, follow these steps:
- Develop a Clear Content Strategy: Define the goals of your personalized content campaign and identify the target audience and distribution channels.
- Select a Generative Ai Platform: Choose a platform that can analyze large datasets and generate high-quality content.
- Integrate Human Curation: Pair AI-generated content with human oversight to ensure accuracy and emotional connection.
- Continuously Monitor And Optimize: Analyze campaign performance metrics to find areas for improvement and refine the content creation process.
4. AI-Powered Predictive Analytics
AI-powered predictive analytics uses customer data to predict their needs and preferences. It analyzes large datasets to find patterns and trends, helping businesses create personalized experiences. This approach saves time by automating data analysis, allowing businesses to focus on creating targeted campaigns and improving customer engagement.
Predictive analytics helps deliver personalized experiences that meet customers' needs, leading to higher satisfaction, loyalty, and revenue. To implement AI-powered predictive analytics, follow these steps:
- Collect And Analyze Customer Data: Gather data from various sources, including customer interactions, purchase history, and online behavior.
- Select A Predictive Analytics Platform: Choose a platform that can analyze large datasets and provide accurate predictions.
- Integrate With Existing Systems: Connect the predictive analytics platform with existing systems, such as CRM and marketing tools.
- Continuously Monitor And Refine: Analyze campaign performance metrics to improve the predictive analytics model and enhance customer experiences.
5. Personalized Email Marketing with AI-driven Segmentation
Personalized email marketing is a key part of AI-driven personalization strategies. Businesses can use AI-powered segmentation to create targeted email campaigns connecting with their audience. This approach helps increase engagement, conversion rates, and customer satisfaction. AI-driven segmentation uses customer data to create personalized email campaigns. This data can include:
- Demographics
- Behavioral factors
- Purchase history
- Browsing patterns
Businesses can identify patterns and preferences by analyzing this data, allowing them to tailor their email content to specific audience segments.
AI-powered segmentation saves time by automating the process of creating targeted email campaigns. Businesses can focus on crafting engaging content and optimizing their email strategy rather than manually segmenting their audience. Personalized email marketing with AI-driven segmentation leads to higher customer engagement and satisfaction. By receiving relevant and timely emails, customers feel their needs are being met, increasing the likelihood of conversion and loyalty. To implement AI-powered segmentation for personalized email marketing, follow these steps:
- Collect And Analyze Customer Data: Gather data from various sources, including customer interactions, purchase history, and online behavior.
- Select An AI-powered segmentation Platform: Choose a platform that can analyze large datasets and provide accurate predictions.
- Integrate With Existing Email Tools: Connect the segmentation platform with email tools like email service providers and marketing automation platforms.
- Continuously Monitor And Refine: Analyze campaign performance metrics to improve the segmentation model and enhance customer experiences.
6. Omnichannel Personalization
Omnichannel personalization tailors user experiences across different channels based on their behavior on other platforms. This ensures a smooth and consistent customer journey across devices and channels. Omnichannel personalization uses past data to predict when and where a customer will likely make a purchase. Brands can then send personalized messages through various touchpoints like emails, text messages, push notifications, or chat messages.
Personalized experiences improve customer satisfaction and increase their lifetime value. For example, hotels can use customer data to offer services based on guests' preferences, such as food choices and room arrangements. To implement omnichannel personalization, follow these steps:
- Collect And Analyze Customer Data: Gather data from various sources, including customer interactions, purchase history, and online behavior.
- Select An Omnichannel Personalization Platform: Choose a platform that can analyze large datasets and provide accurate predictions.
- Integrate With Existing Tools: Connect the personalization platform with existing email tools, marketing automation platforms, and other channels.
- Continuously Monitor And Refine: Analyze campaign performance metrics to improve the personalization model and enhance customer experiences.
7. Voice Commerce Personalization
Voice commerce personalization is changing how customers shop online. With voice assistants like Alexa and Google Assistant, customers can use voice commands to search for products, place orders, and get personalized recommendations. AI algorithms interpret customer queries and understand context, making shopping easier and more convenient. Voice commerce personalization uses customer data, such as past purchases, browsing history, and search queries, to offer personalized product recommendations and promotions.
According to a study by Epsilon, 80% of customers are more likely to buy from a brand that provides a personalized experience. Voice commerce personalization improves customer engagement by offering a hands-free shopping experience. Customers can use voice commands to reorder products, track orders, and get personalized recommendations, simplifying online shopping. This approach helps businesses build stronger customer relationships, increasing loyalty and retention. To implement voice commerce personalization, follow these steps:
- Integrate Voice Commerce Technology: Add voice commerce features to your e-commerce platform.
- Collect And Analyze Customer Data: Use customer data to create personalized product recommendations and improve the shopping experience.
- Optimize Product Pages: Ensure product pages are optimized for voice search with relevant keywords and information.
- Provide Customer Support: Customer support through voice assistants to help with queries and concerns.
8. AI-driven Dynamic Pricing
AI-driven dynamic pricing uses artificial intelligence to adjust prices in real-time based on factors like:
- Demand
- Supply
- Competition
- Customer behavior
This helps businesses optimize pricing, maximize revenue, and stay competitive. AI-driven dynamic pricing uses customer data, such as:
- Browsing history
- Search queries
- Purchase behavior
This data helps create personalized price recommendations, ensuring customers get the right offer at the right time. AI-driven dynamic pricing automates the pricing process, reducing the need for manual work and minimizing human error. It allows businesses to quickly respond to market changes, keeping their pricing strategies effective. To implement AI-driven dynamic pricing, follow these steps:
- Integrate Ai Technology: Add AI-powered dynamic pricing to your e-commerce platform.
- Collect And Analyze Customer Data: Use customer data to create personalized price recommendations.
- Set Pricing Rules: Establish rules to ensure prices align with business goals.
- Monitor And Optimize: Regularly check the performance of your pricing strategy and make adjustments as needed.
9. AI-Powered Personalized Customer Support
AI-powered personalized customer support helps businesses improve customer service. Using AI, companies can offer 24/7 support, automate simple tasks, and provide personalized answers to customer questions. AI-powered customer support uses customer data, such as:
- Browsing history
- Search queries
- Purchase behavior
This data helps create personalized responses, ensuring customers get the right support at the right time. AI-powered customer support automates routine tasks, reducing manual work and errors. This allows businesses to respond to customer inquiries, improving overall efficiency quickly. AI-powered customer support enables businesses to engage with customers in a more personalized and efficient way. Companies can build trust, increase customer satisfaction, and drive loyalty by providing tailored responses. To implement AI-powered personalized customer support, follow these steps:
- Integrate Ai Technology: Add AI-powered customer support to your existing support system.
- Collect And Analyze Customer Data: Use customer data to create personalized responses.
- Set Up Chatbots And Automation: Establish rules for chatbots and automation to handle routine tasks.
- Monitor And Optimize: Regularly check the performance of your AI-powered customer support and make adjustments as needed.
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9 Examples of AI-Powered Personalization Tools
1. Lamatic: Managed Generative AI Tech Stack
Lamatic offers a managed Generative AI tech stack that includes:
- 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 managed generative AI tech stack.
2. Dynamic Yield: AI-powered Personalization Across All Touchpoints
Dynamic Yield is an AI-powered personalization tool that helps businesses create personalized experiences across multiple channels, including websites, mobile apps, and email campaigns.
It uses machine learning algorithms to analyze user data and deliver customized content, product recommendations, and targeted offers. Dynamic Yield's robust platform allows you to test and optimize personalization strategies, ensuring maximum effectiveness and engagement.
Pricing: Dynamic Yield offers customized pricing based on your business's specific needs and scale. Contact their sales team for a detailed quote tailored to your requirements.
3. Persado Motivation AI: Generate Personalized Marketing Messages at Scale
Persado Motivation AI is an enterprise Generative AI for text solution that provides brands with efficiency and efficacy gains. While most Generative AI solutions generate output quickly and at scale, Persado Motivation AI also offers better business outcomes while delivering brand messages.
Persado’s specialized large language model is trained to generate messages that motivate customers to act. Built on a knowledge base of 10+ years of language from Fortune 500 marketing campaigns, Persado delivers high campaign performance for businesses across retail, e-commerce, financial services, travel, consumer technology, and other industries.
Key features:
- Integrated with 40+ martech solutions.
- AI-optimized language has proven to increase campaign performance and conversions across industries and digital channels.
- Enterprise security and brand voice compliance.
4. MessageGears: Eliminate Data Silos for Superior Customer Experiences
MessageGears is a warehouse-native customer engagement platform built for enterprise brands. It connects directly to an organization’s data, enabling brands to deliver superior marketing segmentation, cross-channel customer engagement, message personalization, and compelling customer experiences at scale.
MessageGears eliminates disjointed customer experiences caused by siloed tools. Brands can easily access customer information in real time without the added time and costs of data movement. MessageGears serves brands across various industries, including:
- Retail
- Travel and hospitality
- Technology
- Finance
Key features:
- Advanced audience segmentation.
- Bulk message processing.
- Eliminates data lag and data security issues.
- Streamlines 1:1 conversations across a database of millions.
5. Klaviyo: Drive Growth With Customer Data
Klaviyo is a marketing automation platform that enables brands to leverage customer data for faster growth. It allows brands to connect seamlessly with customers across:
- SMS
- Mobile push
- Reviews
The Klaviyo CDP stores consolidate, manage, and analyze customer data to deliver deeper personalization and better online experiences. AI tools are embedded throughout the platform to enable behavior forecasting, personalized product feeds, and sending optimization. Klaviyo works with brands across retail and e-commerce, wellness, and restaurants.
Key features:
- Additional support from in-house Klaviyo experts and agency partners.
- Over 300 pre-built integrations, including with Persado.
- Real-time data for more precise customer segmentation, zero coding, data scientists, or developers needed.
- A unified view of performance and ROI across channels.
6. Marigold: Customizable Personalization Solutions for Relationship Marketing
Marigold is a multi-purpose platform with a unique approach to relationship marketing. Their solutions are designed around an organization’s specific size, industry, goals, and maturity. Brands receive a combination of technology and expertise to enhance customer acquisition, engagement, and loyalty across the customer journey.
Marigold integrates with Persado and third-party e-commerce, CRM, mobile, and display partner systems. It works across various businesses and industries, from SMB to enterprise, encompassing:
- Financial services
- Higher education
- Retail
- Media
- Non-profits
- Travel and hospitality and more
Key features:
- AI and machine learning algorithms deliver real-time content recommendations based on behavioral and contextual data.
- Centralized subscriber data.
- Marigold technology experts offer comprehensive consulting and implementation services.
- Mutually beneficial and engaging zero-party data acquisition capabilities.
7. Bloomreach: Personalization for E-Commerce Experiences
Bloomreach personalizes the online shopping experience using real-time customer and product data. This AI-powered e-commerce personalization solution enables:
- Marketing automation
- Product discovery
- Content management
- Conversational shopping
Bloomreach enables personalized shopping experiences across the digital shopping experience, including retail, travel and hospitality, beauty, and food and beverage.
Key features:
- 130+ integrations, including with Persado.
- Loomis, self-learning AI, fuels connected and personalized customer journeys at scale across Bloomreach’s products, including:
- Marketing Automation
- Product Discovery
- Headless Content
- 33 patents related to e-commerce search
- Simplifies data-driven marketing with a flexible all-in-one platform
8. Personalization with ChatGPT: Create Tailored Experiences for Your Customers
ChatGPT, developed by OpenAI, is a versatile language model that can power personalization efforts across various applications. It can be used to create personalized chatbots, provide tailored customer support, and deliver customized content based on user preferences.
By integrating ChatGPT with your AI-powered personalization tools, you can enhance your ability to understand and cater to individual user needs, creating more engaging and satisfying experiences.
9. Klevu: Optimize E-Commerce Performance with AI
Klevu is a personalization and conversion rate optimization solution for eCommerce businesses. Like DynamicYield, Klevu is mostly focused on website personalization and optimization with a few key AI-powered tools like:
- Site search can deliver the right search results to all website visitors.
- Product recommendations that can personalize each shopper’s experience.
- MOI, is an AI chatbot (powered by ChatGPT) designed to improve the on-site search experience.
Start Building GenAI Apps for Free Today with Our Managed Generative AI Tech Stack
Lamatic offers a managed Generative AI tech stack that includes:
- 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 managed generative AI tech stack.
What Are the Benefits of Lamatic?
Lamatic includes several features that make it easier for companies to adopt generative AI technologies. First, Lamatic's managed middleware solution allows developers to focus on building custom applications without worrying about the underlying infrastructure. Its automated workflows help teams get their applications up and running quickly.
Lamatic's GenOps tools ensure that generative AI applications can be deployed and maintained in production environments. Finally, applications built on Lamatic's tech stack can be deployed on edge devices using Cloudflare workers for fast performance.
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