The Challenge
SportFX faced the “R&D-to-Product” bottleneck. While they had core AI concepts, they struggled to bridge the gap between deep-tech computer vision experiments and a stable, scalable mobile application. They needed to harden their backend for global scale, refine pose-estimation accuracy in real-world environments, and accelerate a lagging Flutter development cycle.
The Solution
Valere delivered an end-to-end engineering overhaul, integrating a custom Computer Vision R&D pipeline directly into a robust Flutter mobile foundation. We built the scalable AWS-backed API infrastructure, automated notification workflows, and established a rigorous QA framework that allowed the client to move from experimental code to frequent, stable production releases.
About the Company
Mission
SportFX, an NVIDIA Inception Program partner, is on a mission to democratize elite athletic training by making professional-grade biomechanics accessible via smartphone. Valere acted as the primary engineering partner to bridge the gap between their advanced AI R&D and a stable, consumer-ready mobile platform, ensuring ‘Technology for Good’ reaches aspiring athletes.
Key Leadership:
Ashley Corser (Executive Assistant/Project Lead) acted as the primary visionary during this phase of development. The leadership sought a partner who could move beyond “lab exercises” and treat AI as a core component of a consumer-ready product, requiring a team that could manage the uncertainty of R&D while maintaining strict engineering discipline.
The Challenge: Developing a Custom AI Biomechanics Engine to Solve 3D Analysis Constraints
SportFX was at a critical juncture where their technical vision was outpacing their execution speed. They had high-potential R&D but lacked the “connective tissue” to put it in the hands of users reliably.
- Technical Constraints: “The Lab-to-App Gap” The primary hurdle was integrating deep-learning pose estimation models into a mobile workflow. The existing computer vision pipelines were inconsistent in real-world video conditions, and the backend lacked the “hardening” (APIs, admin tooling) required to support a growing user base.
- Market/Business Impact: Stalled Momentum Without a stable, repeatable release cadence, SportFX was unable to gather user feedback or demonstrate the platform’s full value to stakeholders. The complexity of legacy code and ongoing R&D uncertainty made it difficult to predict delivery dates.
- Compliance/Security: Data Integrity at Scale As a platform handling video data and athletic performance metrics, establishing a secure cloud foundation was essential for future enterprise scaling and data privacy.
Why SportFX Chose Valere for AI Native Solution Development
SportFX selected Valere following an online search, citing “Incredible Customer Service” and a pragmatic approach to complex technical problems. They highlighted 3 differentiators:
Valere’s ability to bridge technical work (Computer Vision + AI) with product-grade engineering (Flutter + Backend) meant SportFX didn’t need multiple vendors.
Valere operated with a strategic partner mindset, focusing on what was actually deployable rather than just theoretical R&D.
The team was transparent from the very beginning and throughout CV experiments, providing clear updates when R&D needed more iteration rather than over-promising on accuracy timelines.
"They operate with a partner mindset: pragmatic, accountable, and genuinely invested in helping us."
The Solution: Building a Computer Vision Pipeline with Flutter & AWS to Automate 3D Movement Feedback
A custom AI-driven engineering workflow was designed to combine a Flutter-based mobile application with Python-powered computer vision models running on AWS. The goal was to deliver real-time movement analysis and performance feedback while ensuring the system could continuously improve as models matured.
How the Solution Was Implemented
Infrastructure and Architecture
A scalable cloud backend was implemented on AWS with a modern API layer that supports mobile application workflows, real-time notifications, and custom administrative tools for managing performance data, model updates, and operational controls.
Integrated AI Development Workflow
Computer vision research and model iteration were embedded directly into the product development lifecycle rather than treated as a standalone module. The CV models evolved alongside the Flutter frontend, allowing each release to improve both user experience and analytical accuracy simultaneously.
Phased Implementation Approach
The engagement followed a structured execution model. Discovery and refinement focused on auditing legacy components and validating the remaining scope. Architecture and stabilization hardened the cloud environment and backend APIs to ensure production reliability. Execution and acceleration combined rapid Flutter feature development with continuous CV model iteration. Validation introduced a repeatable cadence of demos, QA cycles, and production-ready releases, enabling consistent performance improvements over time.
The Results
Valere transitioned SportFX from a collection of experimental components into an integrated, deployable platform ready for the sports tech market.
Key Outcomes:
- Established a repeatable sprint cadence, drastically reducing cross-team handoffs and regaining project momentum.
- Successfully shipped stable Flutter app increments and implemented backend APIs to enable end-to-end video analysis workflows.
- Progressed from Proof of Concept (PoC) to a deployable foundation, positioning the company for its next phase of market entry.
- CV pipelines now run more reliably across real-world, non-laboratory videos.
The Client’s Perspective
"What impressed us most was their ability to bridge deep technical work (computer vision + AI) with product-grade engineering. They didn’t treat CV as a standalone lab exercise—they built it around backend, mobile, and QA so the end user experience kept improving."
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