Large language models can fundamentally transform how your brand interacts with its audiences, ensuring that all communication—both internal and external—is consistently on-brand and aligned with your strategic objectives. Investing in your own model allows for fine-tuning to reflect your brand’s unique voice, terminology, and values. This can help you deliver personalised customer experiences and maintain cohesive communication to strengthen your brand identity and improve engagement across all touchpoints.

Should you use publicly open models like OpenAI’s GPTs?
It’s a crucial decision to make: should you opt for a readily available solution like the trainable GPTs from OpenAI, or invest in building your own language model from the ground up? This choice isn’t just about the immediate ease of integration; it’s about considering the long-term strategic benefits and challenges for your brand. Here are some key factors to consider:

1. Data Control and Privacy
With your own language model, you can ensure that all data handling happens within your control and complies with strict privacy laws like GDPR. This control is essential when dealing with sensitive customer information, which may not be securely managed by public models.

2. Achieving a Custom Model
While public models like OpenAI’s GPTs are general-purpose and adaptable, even extensive training on specific data does not allow these models to alter their core structure, limiting their effectiveness in specialised scenarios. In contrast, a custom model built from the ground up can integrate seamlessly with your existing systems and cater precisely to your brand’s unique requirements.

3. Competitive Edge
Owning a unique model can significantly differentiate your brand in the market. It enables you to offer innovative features that set you apart from competitors relying on generic, public tools.

4. Strategic Independence
Building and owning a language model perfectly aligns with your long-term business strategies, offering your brand the agility to adapt quickly to new market trends without depending on third-party development timelines.

How to go about developing your own LLM?
If you are considering building your own language model, there are several platforms that provide pre-built models and tools that make the process easier. The platforms mentioned below offer robust solutions for fine-tuning, deploying, and maintaining models, with options for both cloud-based and on-premise deployments. Here’s a simplified roadmap to help you get started:

Start with a Pre-Trained Model

• Begin by selecting a pre-trained model from platforms like OpenAI or Hugging Face. OpenAI offers models like GPT, while Hugging Face provides a vast collection of models (such as GPT, BERT, and others) from various developers.
• These models are powerful starting points, saving you from having to build a model from scratch and allowing you to focus on fine-tuning.

Prepare and Organize Your Data

• To tailor the model to your brand, you’ll need high-quality, relevant data. Hugging Face and other platforms offer tools to manage and prepare datasets.
• If you need to annotate or clean your data, tools like Labelbox or Snorkel AI can help ensure the data is ready for training the model.

Customize the Model

• Fine-tune the pre-trained model with your proprietary data to match your specific business needs. Platforms like OpenAI or Hugging Face allow you to easily fine-tune models without deep expertise in machine learning.
• This customization helps the model align with your industry terminology and business context.

Deploy the Model

You have two main options for deployment:

• Cloud Deployment: Use platforms like AWS or Microsoft Azure to deploy your model in a scalable, secure environment.
• On-Premise Deployment: For businesses that require more control over data and infrastructure, deploying the model on your own servers is an option. This is ideal for industries with strict data privacy or security regulations.

Choose the deployment method that fits your business’s scalability and security needs.

Monitor and Maintain

• After deployment, it’s important to continuously monitor your model’s performance. Tools like MLflow or TensorBoard can help track and measure how well the model is performing.
• Regular monitoring ensures that the model remains effective over time and can be updated as needed to keep up with evolving requirements.

If you need advice and guidance about setting up your brand’s own LLM, speak to our experts today.