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Visist.ai is an AI-powered sports analytics platform for young athletes. It generates match assessments and performance reports, and serves players, coaches, academies, and parents, each with a different view into the same athlete's development. Our engagement involved a full suite of services, including Design Strategy, User Research, and Visual UI Design, to transform how these athletes interact with their own data.
Despite growing use of video in athletic training, most sports analytics platforms lacked intuitive design, engaging visuals, and personalised insights. Visist.ai aimed to change that but their existing product failed to reflect its own core value proposition: an AI-driven, performance-enhancing experience for athletes. That brought them to UniKwan the first time. The second time, the performance reports being generated for younger players were falling flat.
The primary objective of our long-term partnership with Visist.ai was to build a comprehensive, end-to-end sports analytics ecosystem from the ground up. Initially, the goal was to digitize the athlete's journey by creating a unified space for video uploads and structured coach feedback. As our collaboration evolved, the focus shifted toward optimizing engagement through a complete overhaul of their Student Match Assessments and Overall Performance Reports.
We designed the comprehensive architecture for both the Player and Coach Portals, including video management systems ("My Journal") and grouped goal-tracking ("Threads") . Our work included developing task flows, low-fidelity wireframes, and a streamlined video review queue for coaches. Most recently, we completely overhauled the platform's reporting engine, collaborating with the client’s developers to ensure our high-energy, gamified interfaces were technically feasible and easy to implement.
The onboarding experience sets the tone for everything that follows. For a platform targeting young athletes, the entry flow needed to feel clear and low friction. We designed a minimal, step-by-step onboarding that eases players in — collecting only what's necessary upfront and surfacing the platform's value before asking for anything in return. This was backed by a 10-week design thinking process — qualitative interviews, a survey of 60+ families, and weekly Figma sprints ensuring every decision was rooted in real user needs.
We structured the player's profile to surface what matters to an athlete — their sport, their progress, their history — in an order that makes sense to them first. The information hierarchy was built directly from our persona research, with Advaith — a 20-year-old casual player who needed affordable, structured feedback — as the reference point throughout.
My Journal is where the player's upload training videos, tag them by skill or session type, and submit them for coach review. Timestamped feedback is attached to the exact moment in the video it references, not a generic end-of-session summary. Threads allow players to group related videos around a specific goal, tracking development across multiple sessions in one place. The design goal here was to ensure every piece of input is traceable; every improvement is visible.
Players move through the platform and encounter its value before a subscription prompt appears. The upgrade flow communicates what specifically unlocks at each tier rather than abstract plan names. When the subscription screen arrives, it reads as the natural next step in a journey the player is already invested in.
The coach onboarding experience is a streamlined setup flow that introduces key dashboard features, team management capabilities, and real-time performance analytics, ensuring coaches can hit the ground running and focus on what matters.
A coach's working session on Visist.ai begins with the video queue — a prioritised list of submitted player videos waiting for review. We designed this screen around speed of triage: which videos are pending, which have been reviewed, and where attention is needed first. For coaches managing students across varying skill levels and submission frequencies, clarity at the first screen determines whether the platform fits into their workflow at all.
Coaches can place feedback at any timestamp — written, voice, or AI-generated — and layer multiple notes across a single session. The AI feedback tool generates a starting point from the video content; the coach refines and publishes it. This keeps the coach's expertise in the foreground while reducing the time cost of producing detailed, structured feedback. The result directly addresses what the research flagged: players receiving surface-level notes that didn't account for their age, fitness, or current development stage.
Threads give coaches a structured view of a player's development across a specific skill over time. A thread tracking backhand technique might span several weeks of submissions — the coach can follow the progression, generate statistics from the video content, and share a documented summary directly with the player. This was the feature that moved the platform from a feedback tool to a genuine development record, giving both coaches and players a shared, evidence-based view of where the work is going.
The results confirmed that creative, user-centric storytelling drives engagement. The redesigned screens performed significantly better than previous versions, directly increasing athlete interaction with their performance reports. Due to the measurable success of this gamified approach, Visist.ai has indicated their intent to continue working with UniKwan for the design of all future reporting templates and platform expansions.
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