Data Blog by Lizeo
By integrating LLMs, such as Open AI’s GPT, Claude, Llama, MistralAI, DeepSeek, and others, Lizeo has overcome the limitations of traditional methods. These models are constantly tested and trained on large amounts of data, enabling them to learn from patterns and relationships that would be impossible for humans to discern.
One of the key applications of LLMs at Lizeo is automated topic extraction and sentiment analysis. By feeding large volumes of text data into these models, Lizeo can extract relevant topics, sentiment (positive, negative, or neutral), and emotions (anger, joy, disgust, etc.) from customer reviews, social media postings, and other sources of text data.
This approach enhances the accuracy and speed of the analysis, providing a more detailed market perspective.
Lizeo takes the power of LLMs a step further by testing and fine-tuning these models internally (on our own machine with powerful GPUs), allowing the company to develop our own models specialized in specific industries and topics. This expertise enables Lizeo to deliver highly accurate and relevant insights that are tailored to the unique needs of its clients.
Lizeo also leverages open-source LLMs and enhances them with custom-built components, such as RAG (Retrieval Augmented Generation) or fine-tuning process, to create highly specialized models integrating:
So, how does Lizeo’s AI-powered applications benefit businesses?
Case study: Image-based brand detection
Lizeo has developed significant expertise in prompt engineering, optimizing brand detection in images and enhancing various other AI-driven capabilities. For example, through computer vision and machine learning algorithms, Lizeo can extract brand information from images, allowing clients to verify their presence on specific websites or platforms. This technology has proven to be highly effective, with one notable example being the detection of a brand that was previously missed by OCR (Optical Character Recognition) technology.
Our expertise in prompt engineering also strengthens our AI models in text analysis, improving accuracy in automated topic extraction, sentiment analysis, and contextual understanding. This enables us to deliver deeper and more relevant insights across various business applications.
Lizeo continues to push the boundaries of AI text analytics and is poised to transform the way businesses interact with their customers. The potential applications of this technology are vast and Lizeo is well positioned to capitalize on this trend.
However, as with any emerging technology, there are also challenges to address. Ensuring the accuracy and fairness of AI-driven models is crucial in the development of this technology while also mitigating the risks of bias and misinformation.
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