CASE STUDY
exchange that mimics human tone, emotion and sentiment often comes down to technical details like latency and response times. Verbal cues, interruption models, sentiment matching and even a bit of background noise all help the interaction feel less robotic.
How did you decide what to build versus what to adopt?
If you think about raising a child, you start with the simple things. Don’ t touch that. You’ re teaching the right behaviours, like no sweets before dinner. It’ s similar in the AI world where, from a prompting perspective, you’ ve got to lay the basic ground rules of what is acceptable( or not). As the process matures, what can and can’ t be done becomes more nuanced. It requires a little bit more discretion. We spent a lot of time engaging with our bots and listening to calls to refine the prompting process. It’ s not a quick path. Yes, the technology is awe-inspiring, but you still need to build a core value system before getting into specific skills. There’ s also the fact that the use of AI has to fulfil any regulatory requirements as well as any value or reputational based policies that you might have in your organisation. A well-architected system means providing the right data at the right time to a bot, and no more than no less. Satisfying those data flow requirements is an incredible learning in how to build a system that satisfies reputational, regulatory and legal requirements while still giving the machine all the information it needs to have a great conversation, just like a human would.
What’ s next for Nutun?
What we’ re talking about, a year in, is the balance of quality versus cost. If you look at the various components of what it takes to build a bot, speech to text, text to speech, modelling, LLMs, orchestration, etc., there’ s a lot of choice out there. For us, the focus is quality because ultimately, it’ s just like a good meal – if you if you have good quality ingredients, you’ re going to have a better experience. Our journey has been about two parallel streams. While we’ ve spoken a lot about voice agents, we’ ve also invested in agent assist. When humans and AI work together in the same interaction, that’ s where the real value lies. The AI handles repetitive tasks while people focus on empathy and judgement. It’ s not easy to perfect, but it delivers the biggest benefit. We’ re also looking beyond customer-facing use cases to improve our own operations. The fear of AI taking jobs is fading as people and bots work together to deliver better results for clients, better experiences for customers and better opportunities for our people. x
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