Summarization and analysis are distinct ML tasks with different training data and models: summarization compresses information, while analysis adds context for better comprehension. When converting vast data into accurate reports, summarization overlooks crucial nuances essential for grasping true customer sentiment and can distort data, leading to flawed business decisions. Text like online reviews and support tickets are often rife with ambiguity, sarcasm, and negation, requiring additional context for real comprehension.

Viable has tackled this challenge by fine-tuning OpenAI’s LLMs⁠(opens in a new window) to deliver fast and accurate insights from customer support interactions to recorded transcripts and everything in between, using GPT‑4 to analyze qualitative data on a scale that exceeds current techniques and performance. Viable’s platform provides companies with actionable insights to improve their Net Promoter Score (NPS), reduce support ticket volumes, and better inform their product roadmaps, all while saving on operating costs.

Viable was founded in 2020 with the initial aim of helping businesses achieve product-market fit. They quickly realized that even the most data-driven organizations were unable to make full use of their qualitative data in decision-making.

“We recognized that there was a huge opportunity to use AI to help businesses make sense of the vast amounts of data they generate through customer feedback,” said Dan Erickson, CEO of Viable. “Using GPT‑4's advanced NLP capabilities has been critical in helping us develop our platform, allowing us to deliver more accurate and nuanced insights in a fraction of the time it would take a human to do the same analysis.”