Generative AI integration:
The Key To Grow

To determine if generative AI like ChatGPT is suitable for your business, consider the following straightforward steps suggested by MindTitan’s CEO Kristjan Jansons:

• Pilot Testing: Begin by testing the AI on a specific use case. If it performs satisfactorily and meets your expectations, proceed to integrate it into your operations.
• Identify Workarounds: If the AI nearly meets your requirements but not completely, explore potential solutions with AI team. Sometimes, minor adjustments, such as having a human review and refine the AI’s output, can effectively fill the gaps.
• Assess Risks vs. Rewards: If no workable solution exists and your scenario cannot tolerate occasional errors or irrelevant responses, it might be wise to refrain from using it. It’s important to balance the potential benefits against the risks of incorrect or irrelevant outputs.
• Keep Expectations Realistic: Understand that generative AI, like ChatGPT, generates responses based on patterns in data it has been trained on, without true comprehension. Consider it similar to a new employee who performs well in an interview but may struggle on the job due to a lack of deep understanding.

To begin with generative AI, start by defining a specific business challenge or opportunity where AI could provide a solution or improvement. This involves analyzing your data readiness, including the volume, variety, and veracity of the data you can access and use. Next, partner with AI professionals that have expertise in your industry and technology. This team will help design the initial AI model, ensure data compliance, and set up the necessary infrastructure.

Begin by setting a clear scope for your PoC, specifying what success looks like. For straightforward projects, prepare your data and expect a few weeks for integration and initial testing. For more complex projects, plan for extensive data preparation, algorithm training, and iterative testing, which can take up to a year. Regular check-ins and adjustments will help keep the project on track towards your defined success criteria.

Yes, we fine-tune large language models (LLMs) using your proprietary data to ensure high accuracy and relevance for your unique business tasks, delivering cost-efficient and effective AI solutions.

MindTitan starts by understanding your specific needs and the nuances of your business data. We then tailor large language models by training them on your proprietary data, ensuring they grasp the context relevant to your business. This process involves iterative feedback loops with your team to refine the model’s accuracy and functionality, delivering a solution that integrates seamlessly into your existing workflows and provides tangible benefits.