Start Owning You AI, Stop Renting Your AI. Own It. Control It.

Building a Fully Secure, Local Open AI Model – No Cloud. No Dependencies. Just Architecture.

We use AI and data from multiple LLM sources to enrich content with meaningful, structured metadata. This enables companies and retailers to use their content in more precise, relevant, and compliant ways across all channels.

AI Labs principles and approach

1. API-driven and system agnostic
   Gorilla AI Labs is available to any company or system. Our platform is API-driven, so if you have content, you can send it to the Content Assistant API and we can enrich it for you.

2. Your data and IP stay protected
   Gorilla AI Labs uses our own AI models, and your data stays within our environment. We believe your content and intellectual property are valuable assets that should not need to pass through external AI systems. By hosting and operating our own models, we give companies greater control over their data and IP.

3. Multimodal by design
   Gorilla AI Labs can work with multiple types of content, including text, images, video, and audio. By combining information from different sources and modalities, our models can generate richer, more accurate, and more holistic content enrichment.

4. Ethical and energy conscious AI
   We believe AI should be developed and used responsibly. Rather than relying on large, general-purpose models for every task, Gorilla AI Labs uses smaller, specialised models designed for specific purposes. These models require fewer parameters and can therefore reduce the computational resources and energy required for individual tasks.

5. Multiple specialised models, one intelligent platform
   Instead of using a single model to perform every task, Gorilla AI Labs combines multiple smaller, specialised models through API and orchestration layers. Each model is optimised for a specific task, allowing us to select the right model for the right job while maintaining a consistent experience through a single A

Five Steps to Building an LLM App

Gorilla AI LAb

1) Start with one clear problemFocus on a specific problem that is small enough to solve and test quickly, but valuable enough to make a real difference for users. For example, GitHub Copilot initially focused on helping developers write code inside their IDE rather than trying to solve every developer problem with AI.

2) Choose the right LLMYou do not always need to build an LLM from scratch. Using a pre-trained model can save significant time and cost. When choosing a model, consider:

Licensing. Make sure the model can be used for your intended purpose, especially if you plan to sell your application. Model size. Larger models can be more capable, but smaller models can be faster, cheaper, and easier to run.

Performance. Test how accurate, fast, consistent, and relevant the model is for your specific use case. Offline evaluations can help you compare models before putting them in front of users. These tests can measure accuracy, latency, relevance, and consistency.

3) Customize the LLMA pretrained LLM may not be perfect for your specific application. You can adapt it to your needs using different techniques.

Incontext learning. Give the model clear instructions, examples, and context in the prompt.

Finetuning. Train the model on examples that represent the task you want it to perform. This can make the model much better at specific tasks, but requires high-quality training data.

Reinforcement learning from human feedback, or RLHF. Use human feedback to help the model learn which outputs are more useful or acceptable. The right approach depends on your use case, data, cost, and desired level of customization.

4) Build the application architectureAn LLM is only one part of the application. A complete LLM app usually includes several components.

User experience. The interface where users interact with the application.

Data and context. Your data sources, embedding models, vector databases, and other tools that provide relevant information to the LLM. Prompt and orchestration. Tools that build, optimize, and manage prompts and decide what information should be sent to the model.

AI operations. Caching, content filtering, monitoring, telemetry, and other tools that help make the application faster, safer, and more reliable.

5) Evaluate the application with real users
Testing should continue after the application is launched.

Online evaluations measure how the LLM performs during real user interactions. You can track metrics such as accuracy, acceptance rate, user feedback, retention, latency, and how often users modify the generated output.

The goal is to continuously learn from real usage and improve the application. The simple formula is: Problem → Model → Customize → Architecture → Evaluate → Improve