Link to headingAI integration made simple

Advancements in LLMs have made AI integration more accessible than you might expect. Here's why:

• Pretrained mastery: LLMs come with a vast understanding of common knowledge. This means minimal or even zero training with your own data to achieve results

• Adaptability: Transfer learning lets you fine-tune LLMs on your domain-specific needs, even with smaller datasets

• Creative output: LLMs can generate impressive copy, which is ideal for content creation, chatbots, or translation tasks

• Understanding beyond keywords: LLMs grasp user intent, tolerating typos and making interactions feel natural

• Multilingual reach: Pretrained LLMs often support multiple languages, expanding your potential audience significantly

Link to headingPrerequisites for AI features

To get started, you need a frontend framework that allows for API integrations. Then, you can easily connect your app to external AI providers.

Second, ensure your existing infrastructure can handle the computational demands of LLMs—including streaming and caching long responses.

If you don’t have an architecture that can support that, it’s okay. Tapping into AI doesn’t have to mean a massive overhaul. Consider cloud-based, managed providers that specialize in AI workloads.

For instance, you can build a new AI feature entirely on Vercel, which has native AI support and fully managed scaling, streaming, and caching infrastructure. Then, you can easily hook that new AI frontend back into your codebase via an API.

This simplifies the process and minimizes upfront development costs. As you grow, there’s no limit to how many one-off apps or full-fledged frontends you can run on Vercel’s infrastructure. And thanks to serverless infrastructure, you only pay for what you actively use.

Link to headingHow can AI improve your business?

Below, we’ll take a look at some of the coolest advancements happening in the AI space for small and large businesses alike—that aren’t specifically selling AI products. This is by no means an exhaustive list, but hopefully, it’s enough to start thinking outside the box.

Link to heading1. Semantic search

We’ll start with one of the most powerful applications of AI: search.

Websites often suffer from poor search results. Traditional search functionalities—like matching keywords and their stems, handling misspellings and synonyms, and ranking for context and relevance—are typically tedious to implement and optimize.

LLMs can solve this problem, offering accurate search results even for typos or nonoptimal phrasing. They understand language more like humans do—including nuance, context, and synonyms.

Plus, there’s plenty of room to go beyond the basics and offer fully dynamic, faceted search, which intelligently adapts available filters and search results in real time based on the user's query and selections.

LLM-powered search can also be tuned to your site’s specific offerings and can adapt over time to provide better results.

All this drastically speeds up discovery and makes the process of using your site far more intuitive. Users get a more conversational, personalized relationship with your business, which means a lower bounce rate.

Link to heading2. Natural chatbots

Whether they need help with an order or they can’t find a feature your web app offers, customers expect immediacy and convenience. To deliver on this—especially outside of working hours—businesses create chatbots.

But managing chat presents the same natural language challenges as search—plus a whole lot more, such as limited world knowledge, lack of personalization, and no ability to self-improve.

LLMs provide an elegant solution for this and can offer quick, personalized, and even multilingual support out of the box.

Klarna, for instance, recently reported that their OpenAI-powered chatbot is on par with human agents in customer satisfaction but resolves queries 80% faster. The bot accomplishes the work of 700 full-time agents and drives $40M in net revenue.

Link to heading3. Nuanced personalization

Since LLMs can synthesize huge amounts of data, like help articles, product reviews, user behavior, or even external knowledge bases, they glean deep insights into user preferences that go beyond simple click data or past purchases.

The result is shockingly human product and content recommendations.

In SaaS, for instance, LLMs could help surface the perfect help article or feature tutorial based on a user’s conversational query and context in the app, reducing frustration and increasing the platform’s value.

In ecommerce, an LLM might suggest a dress not just because the user liked similar items, but because the product aligns with their taste for comfortable, eco-friendly clothing.

Generative UI can take this a step further, introducing custom, on-the-fly React components tailored to your individual user's needs.

With AI, personalization becomes more nuanced, accurate, and engaging, which leads to improved customer satisfaction and, ultimately, drives conversion and long-term loyalty.

Link to heading4. Instant localization

Another exciting feature of LLMs is their ability to function in multiple languages, which helps businesses better operate in the global marketplace.

Without language barriers, you can tailor product descriptions, marketing campaigns, and customer support interactions on the fly for different regions. This not only improves the customer experience but also reduces the need for costly and time-consuming manual localization.

Multilingual LLMs can also help teams collaborate across borders. For instance, Match Group uses ChatGPT to break down barriers of language and culture and easily communicate with its international teams. Employees can access relevant information no matter where it originated, boosting productivity.

Link to heading5. Automatic content for any page

By now, most people know you can use LLMs to generate all kinds of content for your business: product descriptions, stock images, tooltips, alt text, and more. This is a pretty straightforward win, but it’s in how you work with the models that this gets interesting.

With Vercel, you can quickly spin up—in hours, not days—internal AI tools that reduce time to market by bringing your business-specific data into the context of the LLM. For instance, you could create a tool where all you have to do is upload a product image and name, and the AI then generates content for your product detail page based on the style of your most successful products.

You could then use no-latency A/B testing across your site to determine when content isn’t working well and give that feedback to your LLM to automatically regenerate copy, images, or even videos. If you implement this feedback loop correctly, you could end up with a hands-free system that gradually prefers content that performs better for end users.

Link to heading6. Developer efficiency

While developer tooling isn't a user-facing feature, anything that helps teams use their time better helps you offer more with your product.

Tools like GitHub Copilot provide engineers with inline suggestions while they write code, but AI can also be used to design interfaces, understand code without hunting for context, and even document your business’s repository for better onboarding and code reuse.

At Vercel, we’ve been developing tools like v0, so engineers don’t have to start from scratch when building a new component. v0 helps developers create proofs of concept and even full-fledged interfaces faster than ever.

Link to heading7. Private data analysis

Since LLMs can quickly process vast amounts of unstructured data, you can use them to sift through thousands of internal documents to find better answers faster.

At Vercel, for instance, we’ve been using Glean.ai to quickly surface relevant materials from across multiple mission-critical applications like Notion, Slack, GitHub, and Gong.

By automatically categorizing content based on themes, sentiment, or topics, AI can help your business quickly identify user or employee trends and areas for improvement.

Plus, with a little tweaking, LLMs can present this data in really beautiful ways. Viable, for instance, generates aesthetic visualizations and natural language reports about your company data so that you can enable stakeholders to act upon findings easily.

Link to headingWhere to start

AI isn’t a magic bullet to solve your challenges, but it does greatly augment existing efforts.

And modern LLMs lower the barrier to entry for AI. They provide a powerful starting point instead of requiring you to build and fine-tune models from the ground up. This makes exploring and implementing AI features significantly easier for most businesses.

We recommend starting simple to get a feel for the interfaces available to you and test their limitations with your ideas for your business. Vercel’s AI Playground is a great place to quickly interact and compare the most popular LLMs on the market, without purchasing a bunch of separate subscriptions.

Then, you can use our DX Platform to collaborate with your team and make low-stakes prototypes, getting a sense of what does and doesn’t work in the AI space.

Businesses will continue to innovate and compete with LLM-driven features, whether or not they are AI-first companies. What might you make next?