AI Wrapper Applications: What They Are and Why Companies Develop Their Own

p>There is so much noise in the AI space right now. It seems like almost every day there is a new model, a new product or some other new breakthrough being announced. The pace of news releases is really unlike anything we've seen before. Now, we're beginning to enter the phase where AI solutions are being rolled into niched areas and industries. In the long run, I feel like AI models are going to be smart enough to do many applications natively. But in the short term, and by short term I mean the next three to five years, what we call AI wrapper applications are going to be the dominant trend.

Now, I am aware that the image of this post is that of a rapper and not a wrapper. I just wanted to see if you were paying attention!

There are quite a few reasons why wrappers will be dominant, which I'll detail in this post, but one primary explanation is that AI is still early and its adaptation is ongoing. With people still getting used to this new technology, wrappers give them an easy way to interact and utilize the capabilities of LLM models without having to learn a new way to interact with machines.

Furthermore, the LLM providers are quite wise in making their systems easily accessible via APIs. Because of this, I expect the next few years to be inundated with AI wrapper applications, even if the long-term prognosis for their necessity may not be so good.

What Is an AI Wrapper Application?

An AI wrapper application is a software layer that sits between the user and an AI model, managing interactions to make the AI more usable and effective for specific tasks. Instead of requiring users to interact directly with an AI model's raw API, wrappers handle the behind-the-scenes work such as:

• Formatting user inputs
• Managing API calls efficiently
• Structuring AI outputs in a useful way
• Enhancing AI capabilities with additional logic or integrations

For example, instead of using a generic AI chatbot, a company might build an internal AI-powered assistant that:

• Uses proprietary data to provide more accurate responses
• Integrates with internal tools like CRMs and knowledge bases
• Follows specific workflows tailored to business needs

This is a simple example of a chatbot, however, wrapper applications can be sophisticated, scaled SaaS applications as well. These types of applications exist to make it very easy for users to leverage the power of AI models without having to change their paradigm when it comes to interacting with machines. As I mentioned in the intro, I don't believe this is a long-term solution because, eventually, the models will be so smart that they'll be able to do all this natively. And for users who cling to a user interface (I spoke about this in our last post, too!), well, the models will be able to just make them on the fly themselves.

But for now, this is where we're at.

Things to Know About AI Wrapper Applications

The Rise of AI APIs

LLM providers such as OpenAI, Anthropic, and Google have deliberately designed their AI models to be accessible via APIs, making it easier than ever for developers to incorporate AI into their applications. These APIs allow companies to build custom interfaces and workflows around powerful AI models without needing to develop their own AI from scratch. AI wrappers are the natural result of this shift, enabling organizations to leverage AI in ways that best suit their needs.

Much of the App Relies on Traditional Web Development

A common misconception is that AI applications require entirely new development skill sets. In reality, most AI wrapper applications still rely on traditional web development practices. This means:

• Front-end development for UI/UX interfaces (React, Vue, or other frameworks)
• Back-end API management to handle user requests and AI interactions
• Data handling to format inputs and store structured outputs

Essentially, AI wrappers are just another form of web application—but one that integrates AI for added efficiency. This makes them far more approachable for companies already familiar with software development, and as we discussed in a previous post, since AI is accelerating the workflows of building custom software, this is now accessible to businesses of all sizes. This results in an amazing outcome for customers who are not used to that reasoning layer being part of the equation.

Many Applications Are Based on Advanced Prompting

Another key point is that many AI wrapper applications don't actually require complex AI engineering or fine-tuning models. Instead, they often rely on sophisticated prompting. Simply put, LLMs are so advanced and capable that many companies that sell a "special sauce" are really just prompting in specific ways to get the desired results. The underlying LLM provides the intelligence, and all that's needed is:

• Well-structured prompts that guide the AI to provide optimal responses
• Context management to maintain relevant interactions
• Minimal additional logic to structure and refine outputs

I often find that businesses believe they need a highly customized AI system when, in reality, carefully engineered prompts can accomplish most of what they need. There's often not much special sauce involved beyond understanding how to guide the AI effectively. Now, this isn't to say that there aren't amazing applications built after a sophisticated process of fine-tuning models, but I think more often than not, they are using the base model with nothing more than a well-thought-out prompt (maybe by AI!).

Wrappers Can Benefit from Integrating Proprietary Data via RAG and Fine-Tuning

For companies looking to leverage their own proprietary data, AI wrappers can integrate retrieval-augmented generation (RAG) or fine-tuning techniques to enhance their applications.

• RAG (Retrieval-Augmented Generation) enables the AI to query external data sources and return relevant information. Instead of requiring the LLM to have pre-learned knowledge, it can pull in company-specific data in real-time, making responses more accurate and up-to-date. This is ideal for companies that have large knowledge bases, documents, or private databases they want the AI to reference dynamically. Think of this as if the LLM has access to internal datasets to refer to quickly - an example can be a customer service bot that has the ability to look up a client's reservation.
• Fine-tuning, on the other hand, involves training an LLM on a company's proprietary data, allowing the AI to make new connections and generate responses that are more aligned with specific business needs. This method is useful when companies need the AI to develop a deeper understanding of internal processes or industry-specific terminology. As opposed to RAG, this is training the LLM to think differently and not just retrieve structured data.

Choosing between RAG and fine-tuning depends on the company's needs—RAG is more dynamic and keeps AI responses grounded in real-time information while fine-tuning is better suited for companies that need AI to generate specialized responses from learned knowledge. Either way, AI providers and other libraries have made these easy to accomplish. However, it's worth noting that fine-tuning with large datasets can incur significant computing costs.

Wrappers Offer Customization and Enhanced Functionality

While general AI models are incredibly powerful, they are often too broad for specialized use cases. AI wrappers allow developers to:

• Implement domain-specific logic that enhances AI responses
• Enforce company-specific rules to ensure consistency
• Integrate AI with other tools, such as databases and automation systems

How this is implemented depends on the particular project. In some cases, this happens through the training of the model or in the software layer. But wrappers definitely make it easier for organizations to install guard rails in the usage of AI.

Wrappers Fix User Experience and Accessibility Challenges

Interacting directly with an AI model can be challenging for non-technical users. Some people may not think about this, but conversational interaction with AI doesn't come easily to some people. Those at a junior level or those who have never managed direct reports sometimes find it challenging to direct via conversational prompts.

Wrappers help by:

• Providing simplified interfaces such as chatbots or dashboards
• Formatting AI outputs for clarity and usability
• Offering ways to refine and control AI interactions for better results

Security and Data Privacy

Many companies hesitate to use public AI models due to privacy concerns. By using an AI wrapper, businesses can:

• Utilize open-source, locally installed models
• Control how data is processed and stored
• Implement security measures to protect sensitive information
• Ensure compliance with industry regulations such as GDPR and HIPAA

One great element about building a wrapper application is that it can be somewhat model-agnostic. This means that if you didn't make a significant investment in training the model, you could always change it for another one as a new one comes available or if one offers better reasoning skills. Developing an app on a platform like Amazon Bedrock makes changing the model as easy as changing a single line of code.

It's also handy if you have privacy concerns. It's quick to prototype an AI wrapper using a service such as OpenAI. But if privacy becomes a major concern after proving concept with your prototype, you can switch to a locally hosted model which ensures you complete data privacy.

If you were to use a licensed solution or rely on a hosted model, security and privacy may not be guaranteed.

Scalability and Efficiency

As AI adoption grows, businesses need solutions that scale efficiently. AI wrappers help optimize:

• API usage costs by managing requests effectively
• Model selection by choosing the right AI for different tasks
• Performance by caching results and handling high traffic

I find that this is a key selling point for companies looking to build internal AI systems. Building a wrapper enables the entire organization to use one unified model and build out a cost-effective approach to utilizing AI. I think in the coming months we're going to see more companies take this approach as opposed to having all their users utilize individual AI accounts such as ChatGPT or Claude.

The Future of AI Wrappers

AI wrappers are not just a trend—they are becoming a fundamental layer in the AI ecosystem. As AI models continue to evolve, wrappers will play a crucial role in making these models accessible, secure, and effective across different industries.

Looking ahead, we can expect AI wrappers to:

• Further streamline AI adoption by offering pre-built solutions tailored to industries
• Enhance multi-model integration, allowing businesses to combine different AI services seamlessly
• Improve automation capabilities, reducing the need for human intervention in routine AI-driven tasks

And then when all that's figured out, as I said, it's not outside the realm of possibilities that wrappers become redundant because LLMs do all the work themselves, and that includes generating a user interface. This is more of a theoretical thought right now, but it seems inevitable.

Wrapping Up

AI wrapper applications are, in my opinion, in the right place at the right time. What's important to understand is that they are not as complex to develop as many assume. Many companies believe they need fully autonomous AI agents, but in many cases, they simply need software leveraging LLMs efficiently.

I frequently talk to companies that overestimate how much AI engineering is required when, in reality, well-crafted prompts and structured workflows are doing most of the heavy lifting. These applications are built using standard web development techniques, with much of their effectiveness coming from understanding how to guide LLMs properly rather than complex AI models.

As AI continues to expand its role in businesses and everyday life, wrapper applications will be at the forefront of making AI accessible, scalable, and truly useful. Understanding and leveraging AI wrappers will be essential for companies looking to stay ahead both for public-facing systems and back-office applications. That is, until the computers do everything for us!