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Audicin, science-backed audio wellness platform

An evidence-based AI assessment for a science-first product

Audicin’s product is validated by neuroscientists and medical doctors, so any AI added to it had to meet the same bar. Moterra delivered a Rapid AI Assessment that gave leadership a model choice backed by numbers, a scoped path to production, and confidence that AI would enhance the science rather than compromise it.

Client Audicin Sector Health & wellness technology Scale Scientifically-engineered audio platform, global user base Solution AI Consulting - Rapid AI Assessment
What they got
Model benchmarking Tested on their own tasks Claude 4 Sonnet, Nova Pro and Nova Lite compared on recommendation accuracy, therapeutic-outcome correlation, latency and cost per request.
Working sandbox From day one Personalisation use cases prototyped inside Moterra Business AI Suite during the engagement - hypotheses turned into evidence before any build.
Production architecture On AWS, built for scale Real-time recommendation performance, GDPR-grade health data handling, cost predictability, and a phased path with go/no-go gates.
01

The situation

Audicin builds scientifically-engineered music combining binaural beats, spatial sound and neuroscience-backed audio design to help the nervous system relax, focus and recover. Every track is validated by an in-house team of neuroscientists, medical doctors, audio engineers and music composers - a standard any added AI layer would also have to meet.

02

What they needed

As the product and user base grew, Audicin’s team wanted to explore how generative AI could take personalisation from broad categories to something genuinely individual, without compromising the scientific integrity that defines the product. Four questions had to be answered before anything got built.

Which foundation model performs best on real personalisation tasks?Where does AI add measurable value in the user journey?How do we handle health-adjacent data responsibly?What does a production architecture look like at scale?
03

What Moterra delivered

A Rapid AI Assessment scoped to Audicin’s context, combining stakeholder work with hands-on measurement rather than desk research.

Interviews and readiness reviewStakeholder interviews across product, engineering and clinical advisors, plus an AI readiness review of the existing platform and data pipeline.
Use case prioritisationFocused on personalisation, adaptive content sequencing and user progress insight.
Comparative model benchmarkingClaude 4 Sonnet, Nova Pro and Nova Lite run against Audicin’s real recommendation and reasoning tasks - measuring accuracy, therapeutic-outcome correlation, latency and cost per request.
Solution architecture on AWSDesigned for real-time performance, GDPR-grade health data handling and cost predictability.
Phased delivery planClear go/no-go gates at each stage, so investment tracks evidence.

Throughout the engagement, Audicin had access to Moterra Business AI Suite as a working sandbox, used to prototype and validate personalisation use cases against real content and data.

04

The outcome

Audicin’s leadership got what mattered most: a model choice backed by numbers, a scoped path to production, and confidence that AI would enhance the science rather than compromise it.

Why an assessment first

A product built on peer-reviewed method deserves the same rigour in its tooling. Benchmarking three models on Audicin’s own tasks cost a fraction of building on the wrong one and finding out in production.

Next step

Bring the hardest questions to the first call.

Thirty minutes, a live demo, and a quote for your environment.

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