AI-Powered Quality Control Platform for Sports Tech
A cloud-native quality control platform enabling human review of AI-generated sports analysis, secure video access, and compliant data handling at scale.
Sports Performance Tracking via Computer Vision
A sports technology company operating across Germany and Brazil, specializing in AI-driven performance analysis and digital talent scouting.Their mobile platform leverages computer vision to analyze user-generated videos of athletic drills, evaluating skills and democratizing access to professional scouting networks. Driven by rapid global growth, the client required a highly secure, enterprise-grade cloud infrastructure to manage an expanding volume of sensitive biometric data and AI-generated performance metrics.
GDPR-Compliant AI Oversight
The client needed a robust internal workflow to audit AI-generated decisions across multiple environments. Existing processes lacked a centralized, secure interface for QA personnel to review AI outputs, manually override inaccuracies, and securely access sensitive user-recorded videos. The required architecture also had to support strict GDPR-compliant data deletion workflows without exposing private cloud resources to the public internet.
Engineering a Secure, Low-Code Auditing Ecosystem
A secure, serverless AWS architecture that empowers data teams with a unified, low-code dashboard for AI validation, media management, and automated GDPR compliance.
Centralized QA Interface
Deployment of Appsmith on Amazon EC2 within a private subnet, governed by AWS Systems Manager. This provides a secure, low-code operational dashboard for data teams to audit AI decisions and manage events stored in Amazon Aurora MySQL.
Asynchronous Data Processing & Overrides
Implementation of AWS Lambda, SQS, and SNS to handle asynchronous data ingestion and enable manual AI-result corrections. QA personnel can seamlessly trigger cross-environment data updates and publish corrections directly from the unified interface.
Secure Media Access & Compliance Automation
Architecture of a privacy-first media workflow using Amazon S3 and Lambda to generate temporary, pre-signed URLs for reviewing exercise videos securely. Additionally, a dedicated, SQS-triggered serverless flow was engineered to execute targeted user data purging in strict accordance with GDPR.
Delivering enterprise-grade security, automated GDPR compliance, and seamless human-in-the-loop validation to scale AI operations safely.
Enhanced AI Accuracy
Provided a critical human-in-the-loop validation layer, allowing internal teams to swiftly identify, correct, and publish refined data across all deployment environments, thereby continuously improving model reliability.
Uncompromised Security & Privacy
Ensured all sensitive user-generated video content and personal data remained strictly protected through private networking (VPC endpoints), pre-signed S3 URLs, and rigorous access controls.
Streamlined Regulatory Adherence
Automated complex GDPR compliance requirements through a targeted data deletion mechanism, significantly reducing manual administrative overhead and mitigating legal risk.
Accelerated Operational Efficiency
Replaced fragmented review processes with a unified, low-code operational hub, accelerating the QA audit lifecycle and empowering data teams with immediate, secure access to the information they need.
