Many direct-to-client digital services rely on interactions with real people to make decisions, provide support, and improve their platforms over time. But real people produce ‘messy’ data, which makes it hard – at scale – to accurately draw a line between what they need, and the official services and systems that can support them.
Last year, Jacaranda sat down and developed an automated approach to standardise conversational data to address this challenge.
The result was a simple 10 step toolkit, designed to support other implementers augment diverse data more quickly and cheaply at scale and more seamlessly train AI-based models.
We are keen to hear from other implementers how this toolkit has supported data augmentation in their services and systems. Please reach out to [email protected] to share feedback, learnings, and areas for improvement.
