Description StrideMatch is an AI-powered running-shoe recommendation platform designed to align footwear choice with how runners actually train and run. Rather than relying on generic profiles, static questionnaires, or subjective feel, StrideMatch builds a dynamic understanding of each runner through real training data, biomechanics, and shoe usage insights.
From the first interaction, the platform analyzes key indicators such as pace distribution, weekly volume, training frequency, intensity patterns, and load evolution. These insights are combined with gait dynamics and foot morphology to create a precise runner profile rooted in real-world practice. StrideMatch then translates this profile into clear, actionable shoe recommendations, guiding runners toward the most suitable models via trusted retail partners.
Built for both runners and specialty retailers, StrideMatch acts as an intelligent companion throughout the shoe-selection journey—bringing clarity, confidence, and objectivity to one of the most critical decisions in running.
Innovation StrideMatch redefines shoe recommendation by shifting the decision process from intuition and averages to measurable facts. The core innovation lies in the cross-analysis of real training data, biomechanical signals, and a structured shoe knowledge base. Unlike traditional tools that classify runners into simplified categories, StrideMatch continuously adapts to the runner’s actual usage and training load.
Each recommendation is explainable, data-backed, and limited to a short list of coherent options, reducing choice overload and improving trust. By positioning itself as a neutral decision-support layer between runners, brands, and retailers, StrideMatch enables more relevant advice, fewer mismatches, and better long-term outcomes in performance, comfort, and injury prevention.
Product Detail StrideMatch is composed of several integrated technology layers:
– A data ingestion module analyzing training activity (pace, volume, frequency, intensity).
– A biomechanics layer assessing gait dynamics, stability, and running patterns.
– A foot morphology component capturing shape, volume, and mechanical constraints.
– A multi-brand shoe database structured on objective criteria such as geometry, cushioning, stability, intended use, and durability.
These layers are orchestrated by an AI engine that correlates runner data with shoe characteristics to generate precise, personalized recommendations. The platform is designed for seamless use both online and in-store, supporting professional retail environments as well as direct-to-runner experiences.
Specification StrideMatch is a software-based platform available in B2C and B2B configurations. It delivers real-time shoe recommendations through a digital interface optimized for mobile, tablet, and in-store use.
The system outputs a concise selection of recommended models, each accompanied by clear technical justification linked to the runner’s profile and training data. StrideMatch integrates into existing retail workflows and digital ecosystems, operating as a neutral, brand-agnostic tool focused on decision accuracy, credibility, and scalability.
