From ChatGPT to your own AI: this is how to build an AI application that works

AI is everywhere. Tools like ChatGPT, Claude, Gemini and Perplexity are often the starting point, easy to use. But you won't discover the real power of AI until you define the architecture yourself. Only then will you determine how secure, fast, sustainable, affordable and scalable your AI is, and how to seamlessly integrate it into your existing processes and systems.

In this blog, you'll learn how to go beyond individual tools and build a smart AI application that fits your business exactly. From model selection to data storage and hosting: this is how to stay in control, avoid surprises and get the most out of AI.

You read:

• How AI really works and why your choices make the difference
• The benefits of building your own AI solution
• When you're also fine with a cloud solution
• Concrete examples from healthcare, legal, finance and HR that show what is possible

Get ready for a deep dive that will help you really deploy AI on your terms. Without fuss, without surprises.

The AI stack: not one model, but five layers of choices

AI is not simply one "thing. It is a stack of technologies, in which each layer determines how your application performs, how secure it is, and your impact on data and costs. Understand your full AI stack, and you'll stay in control.

1. Application layer: the interface with your user.

The first layer defines how your team or customers interact with AI. Often this is through off-the-shelf apps like ChatGPT, where you simply log in and ask questions. But you can also build your own interface that links AI directly to the tools you already use, such as an internal chatbot, a search function in your systems or an application that automatically analyzes documents.

This layer determines who receives what information, what data is processed and how users experience the AI. With a proprietary interface, you maintain full control over access and privacy, and can fine-tune exactly what data is shared.

While tools like ChatGPT increasingly offer integrations with platforms like Google Drive and Microsoft OneDrive via connectors, this access is limited to those specific ecosystems. Want to seamlessly connect your AI to ALL your systems, both inside and outside your organization, and maintain full control over all data and system links? Then building your own interface is the best choice.

2. LLM layer: the AI language models themselves.

This layer determines which model performs the AI tasks, and thus how powerful, fast and scalable your solution is. Each model has its own strengths and limitations. Here the most important ones:

• Size and speed: Large models such as GPT-5 and Gemini 2.5 are powerful and provide detailed answers. This is due not only to the size of their training data, but also to their ability to better understand logic and relationships. Smaller models, on the other hand, are faster, cheaper and suitable for simple tasks such as FAQs and chatbots. Consider OpenAI's GPT-mini models, as well as open source variants such as Mistral and LLaMA 2 (Meta).
• Context window: This determines how much text a model can handle at a time. Some models, such as Google Gemini 2.5, can effortlessly handle long documents and complex conversations, while others are better for short queries. In many standard apps, the context window is deliberately limited to keep performance and response time smooth for all users. Through APIs, you often have the ability to use larger context windows. Context windows that are too small often lead to incomplete or inaccurate answers, so it is important to take this into account.
• Open source vs. closed: Open source models like LLaMA 2 and Mistral you can customize and host locally, on-premises or on your own servers. That way you keep maximum control over privacy and security. Closed models are easier to integrate and often immediately usable, but give you less control over what happens to your data. Getting started quickly without technical hassle? Then closed source is fine. Those who want complete control often choose open source.

3. Model type: smartness vs. speed

Not every AI model is the same. Depending on what you want to achieve, choose a model that can reason logically or, on the contrary, generate texts quickly and efficiently.

Reasoning models, such as GPT-5 Thinking or Magistral, are designed for complex thinking and in-depth analysis. They are ideal for legal documents, financial calculations and medical diagnoses.

Non-reasoning models, such as GPT-5 Instant, are faster and more suitable for simple tasks such as answering FAQs or generating basic content.

But you don't have to choose between one or the other. Instead, in a smart AI solution, you combine them. For example, you use reasoning models for complex tasks and fast models for standard work. That reduces errors and keeps costs manageable.

4. Data storage: where does your knowledge live?

AI is only really valuable when it works with your data. A vector database helps you make that knowledge available smartly. You don't store raw documents, but "embeddings" - mathematical translations of your content. This allows your AI to find the right information at lightning speed and process it contextually.

If you use your own vector database, you decide what knowledge is available, how it is linked to your models and who has access to it. Perfect for internal documents, customer information or specialized knowledge that is not on the public web.

You can deploy such a vector database in two ways:

• As a cloud service via Pinecone or OpenAI, for example. Nice and easy, but you sacrifice control and transparency.
• Hosting it yourself with tools such as Weaviate or Chroma - on-premise or in your own cloud. That gives you maximum control over data, structure and performance.

Do you mainly work with public data such as product information, manuals or FAQs? Then a cloud-based database is often easier and faster. Do you use complex or confidential data? Then an in-house database with a specifically trained, smaller model will provide more reliable answers and fewer hallucinations.

In short: with both your language model (LLM) and your vector database, you have the choice of hosting it yourself - on-premises, in a private cloud or via a cloud service.... The more control you want, the more you have to control yourself.

5. Hosting: the technical environment

Where and how does your AI run? OpenAI's cloud is fast and scalable, but does not always comply with strict regulations such as the GDPR in Europe - depending on the type of data you process. Especially in highly regulated industries, this can be an important consideration.

In addition, latency plays a big role. The closer your hosting is to your users or data centers, the faster the AI responds. European hosting reduces latency for European users and helps with compliance.

You have several hosting options:

• Local, air-gapped: maximum privacy and data sovereignty, but less scalable and requires a lot of manual management.
• Proprietary servers or cluster on-premises: good control and scalability, but you remain responsible for network and security management.
• Own server in external data center: scalable and professionally managed, but part of the control lies with the provider. Strict security agreements are essential here.
• Third-party cloud hosting such as OpenAI: easily scalable and maintenance-free, but less control over data and dependent on the provider.

Self-hosting also allows you to optimize hardware and network for better performance, crucial in real-time applications. This does require the right infrastructure and technical knowledge, but fortunately you can also outsource this to a technical partner (such as Sterc).

Tip: When hosting and data storage, pay attention not only to privacy and compliance, but also to latency and performance. Choose a location and infrastructure that is close to your users and ensures AI runs quickly and smoothly.

Why build your own AI application?

So building AI is about making choices. Choose convenience with a SaaS solution? Fine for getting started. But do you want real growth, security and complete control? Then take control and build your own AI stack. Here are the most important advantages in detail:

Privacy & data ownership

Your data is worth gold. With your own AI stack, you keep maximum control over where your data is, who can access it and how it is processed - whether you work on-premise or in a private cloud. This way you protect sensitive information and comply more easily with privacy regulations such as the AVG.

Public cloud providers such as OpenAI often work with processing agreements and (in some cases) do not store data permanently. But the control is never completely in your hands. If you build your own, you control what happens or does not happen to your data - from storage to processing, from logging to access.

More importantly, you don't have to chase everything through an AI model or vector database. By designing your own environment intelligently, you can decide which data you make available and which you deliberately leave out. Think in data categories: public info, customer data, strategic knowledge or personal data. Different requirements apply to each type, and therefore a different approach.

In short: conscious handling of data is always important - with or without AI. But with your own stack, you set the rules of the game.

Security & compliance

Privacy is the basis, but security goes further. Building your own stack means that you decide who can use which data, what is stored and how the AI output is controlled. This is essential to meet internal compliance requirements and external audits.

In addition, you have control over fine-tuning AI models to minimize unwanted output and bias. No surprises or limitations of a public API, but full control over the quality and reliability of your AI.

Cost control

Pay-per-use of public AI clouds sounds appealing. Thousands of API calls per day usually don't immediately create huge costs, but the cost per token can quickly add up - depending on your use case and the model you use.

An in-house AI stack requires an initial investment in hardware (such as server rental) and expertise, but after that, variable costs are often much lower and more predictable. No sky-high prices for a large token consumption, but decide for yourself how to set up capacity. That's how you make AI affordable and scalable in the long run.

Independence

By building your own, you are no longer dependent on one supplier. No more hassle with unexpected price increases, changing conditions or suddenly dropped services.

You decide when and how to update your models, which is essential to keep up with new versions and innovations. You choose which technology you use and can easily switch or expand without migration problems. This provides peace of mind and flexibility, crucial in the rapidly changing AI landscape.

Smart combining

Because you build your own AI stack, you also get the freedom to really strategize AI. Because one model that does everything? There's no such thing.

The real power lies in cleverly deploying different AI models and techniques, tailored to your organization's specific tasks and goals. That way you get better performance, lower costs and more flexibility. How

1. Reasoning models for complex analysis
When it comes to complex problems that require logical thinking and multiple steps, reasoning models are your best choice. Consider GPT-o3 or Claude Opus 4: they fathom legal documents, perform financial risk assessments and support medical diagnoses.

These models provide power and precision, but also require more computing power and therefore cost more. So use them purposefully for tasks that really matter.

2. Fast, compact models for standard queries
For simple, recurring tasks such as answering FAQs, short chats or generating basic content, lightweight models such as Mistral 7B or similar open source variants are perfect. They are fast, affordable and efficient.

Note that these models have a higher chance of hallucinations - they may occasionally give incorrect or fabricated answers. Therefore, it is best to use them for simple tasks where these risks have less impact.

The result? Smart combining means lower costs, better performance and a sustainable AI that grows with your organization. This way you get the most out of your technology and your budget.

Inspiring examples from practice

AI is no longer a pipe dream. More and more organizations are now deploying self-hosted or hybrid AI solutions. They are making their processes smarter, safer and more efficient, and are already reaping the benefits.

Here are four examples that show how that works:

Healthcare organizations

Hospitals and healthcare networks run their own LLMs, trained on medical directives, patient records and research data. Because all data stays within their own IT environment, these systems are fully GDPR-proof. Physicians use the AI to quickly find the right info, support diagnoses and create treatment plans, without risk of privacy risks or data breaches. That speeds care and improves patient safety.

Law firms

Legal service providers work with highly confidential data. That's why they use in-house AI that searches case law and contracts without running the data through public clouds. This AI is trained on their in-house legal library and helps lawyers more quickly analyze documents, find precedents and assess risks. Thus, professional secrecy remains inviolate.

Financial organizations

Banks and insurers combine reasoning and search models to perform risk analysis and compliance checks. Self-hosted vector databases for documents and reports keep sensitive customer data within the organization stored in secure systems such as CRMs or data warehouses. AI uses both sources to detect fraud, assess creditworthiness and automate reporting. All within strict regulations.

HR departments

HR uses proprietary AI chatbots that quickly answer internal questions. With access to policy documents, collective bargaining agreements and procedures - securely within the corporate network. This unburdens HR, speeds communication and ensures confidentiality. Employees get immediate answers to questions about leave, salary and more, without personal data being circulated externally.

When do you choose ChatGPT or another cloud model?

Building your own has many advantages, but sometimes an off-the-shelf solution like those from OpenAI, Anthropic or Google is just the smartest choice. Here's when to go for such cloud AI without worry:

Quickly build a proof-of-concept (PoC) or MVP

ChatGPT is a great starting point to quickly build a basic MVP or proof-of-concept. You can test prompts and upload files, or work with your own data via connectors such as Google Drive and OneDrive. This makes it approachable and fast without complex setup.

Low volume or occasional use

Do you use AI sporadically or with a small group? Then a subscription or pay-per-use with a cloud provider is often easiest and cheapest. This way you avoid expensive investments in infrastructure that you hardly ever use.

No in-house IT or AI expertise available

Don't have the resources or knowledge to manage AI yourself? Cloud solutions are then a smart, pragmatic choice. The provider handles maintenance, updates and scalability.

Summary: ChatGPT and other SaaS AI are perfect for speed and convenience. But once you grow, work with sensitive data or have high security requirements, building your own is the logical next step.

Deploy AI on your terms

AI can transform your business - but only if you stay in control. With Sterc ONE, you build an AI solution that fits your organization exactly: secure, scalable and fully integrated into your own processes.

• No separate tools, but one smart AI layer on top of your data, systems and workflows
• Full control over models, storage and integrations
• From strategy to realization - we build your AI stack

Ready to deploy AI really smart?

Check out Sterc ONE