About Feeld
Feeld is a dating app for the curious. The platform creates inclusive online and real-world spaces where people can safely express and explore relationships in new ways.
As its user base grows, Feeld actively builds trust by maintaining safety policies and practices that evolve alongside its community.
The Challenge: Scaling Infrastructure to Protect the Community
To support their highly engaged user base, Feeld invests heavily in building advanced systems that maintain a safe and authentic community while continuously improving the digital experience for its members.
As Feeld expanded, one of its most critical operational hurdles was the detection and prevention of fake accounts, spam, and fraudulent behaviors. Protecting the community required robust artificial intelligence, but their underlying infrastructure could not support the required growth.
Their initial machine learning setup relied heavily on a scheduled notebook workflow, which quickly became difficult to manage at scale. Specifically, the engineering team struggled with:
- Manual Bottlenecks: Ad hoc processes made it increasingly difficult to retrain, update, and deploy models efficiently.
- Resource Inefficiency: The team lacked flexible ways to run inference, meaning they had to rely on rigid, always-on infrastructure that couldn’t scale dynamically to meet different use cases.
- Development Delays: Engineers needed to build infrastructure from scratch every time they wanted to deploy a new model, pulling their focus away from core product innovation.
Feeld was also seeking to replace its third-party fraud detection vendor to achieve significant cost saving without sacrificing security. The startup required a partner who deeply understood machine learning and possessed the technical expertise to architect highly scalable production systems on AWS.
The Solution: Automated MLOps on AWS
Feeld selected Automat-it due to our AWS AI Services Competency and proven ability to design robust ML infrastructure. By leveraging their proprietary Generative AI & MLOps Production Accelerator, Automat-it helped Feeld transition into a highly structured, automated machine learning framework.
Automat-it’s hands-on collaboration ensured the new system bypassed common architectural pitfalls and adhered strictly to AWS best practices from day one. Key implementations included:
- End-to-End Lifecycle Management: Replaced the legacy notebook setup with standardized, automated workflows, making it frictionless to manage the full lifecycle of models.
- Dynamic, Serverless Inference Scaling: Introduced flexible inference deployment options using AWS Lambda, allowing the infrastructure to scale up or down efficiently based on specific workload demands without requiring always-on compute resources.
- Unified ML Foundation: Established a centralized platform that decoupled infrastructure management from data science, allowing developers to deploy new capabilities without worrying about the underlying servers.
Automat-it’s engineering team stepped in to overhaul the fragmented setup, designing a fully Terraform-managed, multi-account AWS environment (production, development, and website) tailored for complex AI workloads. Adaptive learning best practices were also implemented in the form of daily automated retraining cycles to stay ahead of evolving spam tactics and fraud patterns.
The Results
Feeld successfully secured its platform and accelerated its development pipeline.
Key Outcomes Included:
- Enhanced Platform Safety: Successfully established a robust fraud detection process, significantly reducing fake profiles and making the user experience much safer and more secure.
- Accelerated Time-to-Market: Standardized pipelines dramatically increased the speed at which the team can iterate and experiment. New models—including core architecture and recommendation systems—now reach production far faster than before.
- Increased System Reliability: Moving to a structured ML framework significantly improved the overall stability and observability of the backend systems.
- Reduced Maintenance Overhead: Once models are deployed through the new pipeline, they require far less upkeep. This operational efficiency frees up the internal team to focus entirely on delivering real product value and launching new initiatives.
Start Your Journey with Automat-it
Scale securely with an AI infrastructure built on best practices . Start your journey with Automat-it and join the ranks of high-growth startups like Feeld.