Sela. | Cloud Better.


Aiberry is a cutting-edge artificial intelligence company, working in the clinical psychology space. Based on research from Oxford University and the University of Paris, their research team has developed a patented methodology for detecting depression based on expression, voice, and word context analysis. Through machine learning, the company aspires to predict your level of depression at different points, and even tackle other mental health issues such as anxiety. 

The challenge

Aiberry needed to build out a software platform to provide quantitative analysis, mental health insights, and risk scores to health care providers in real time during short interviews from captured media.

The system needed to be able to process imagery, audio, speech, and other media in real time and leverage Large Language Models (LLMs) for analysis and risk scoring. The system needed to be globally available, accessible, and redundant with low latency. Onboarding of new providers needed to be quick and seamless. Modifications and enhancements to the underlying AI/ML cloud infrastructure needed to be fast, simple, and reliable. Large Language Models (LLM) needed to be flexible and adapt to new media, data, and usage scenarios over time. Costs needed to be managed, observed, and scalable.


The solution

Sela’s solution relied heavily on machine learning (ML) services on AWS. Media streams were converted to raw transcripts, with ML inference used to predict patient suicidality using:

  • Lambda + Step functions
  • ECS
  • SQS
  • DynamoDB
  • Sagemaker
  • AWS Comprehend

A simple user interface was built for clinicians and patients and deployed to a secure, multi-account AWS environment using containers running on ECS. AWS SSO provided secure roles and permissions for users. Infrastructure builds across tenants and regions were automated with AWS CDK, Gitlab, and CodePipeline.



The results

Aiberry is now able to quickly adapt their system to changing requirements, new research, and new technology. Documentation and training on the simplified end-user application make it easier to user and understand. This translated into greater subscriber growth via faster onboarding and a better user experience with fewer support issues and performance problems.

The system performs faster and costs less per user to operate. The product is easier to innovate and deploy.