How KhiladiPro Uses AI to Standardise Youth Sports Assessments

India’s youth sports ecosystem has long faced challenges ranging from lack of trained coaches to inconsistent physical education programmes and limited visibility for emerging talent. KhiladiPro (KPro), an AI-first, SaaS-powered sports tech platform founded in 2023 by Utkarsh Yadav, is attempting to tackle these gaps with technology designed for mass adoption. By combining AI-driven assessments with structured data-backed insights, KPro aims to democratise access to talent evaluation, offering every child, from metro schools to tier-3 and tier-4 towns, a scientifically grounded roadmap to athletic development. 

CiOL recently spoke with David Gladson, Co-Founder & Chief AI Scientist ,KhiladiPro (Kpro)to understand how the platform is addressing India’s long-standing challenges in youth sports assessment. The discussion highlighted the company’s AI-first approach, focus on inclusivity across tier-3 and tier-4 towns, and frameworks ensuring fairness and scientific rigour. Gladson emphasised the importance of scalable AI-driven solutions in a market historically dependent on subjective coaching methods.

The conversation also delved into KhiladiPro’s proprietary Khiladi Ability Index (KAI), its application in schools and academies, and the measures taken to validate AI models across diverse demographics. Key operational strategies for adoption in digitally uneven regions and the platform’s approach to ethical data management were also explored.

Interview Excerpts: 

CiOL’s interaction with KhiladiPro provided detailed insights into how the platform is creating measurable, objective pathways for youth sports development in India. Here, we share the full Q&A with David Gladson: 

How does KPro’s AI-first assessment model address the long-standing structural gaps in India’s youth sports ecosystem, especially in schools and academies lacking trained coaches and standardised evaluation methods?

For decades, India’s grassroots sports ecosystem has struggled with three deep-rooted issues, lack of trained coaches, absence of standardized assessment, and limited early visibility for talent. KPro’s AI-first assessment model is designed to bridge these gaps, and to do so at scale. 

Our technology brings objective, science-backed talent evaluation directly to schools and academies that may not have access to specialised coaching resources. With just a smartphone, KPro can analyse a child’s athletic performance across 20+ parameters, speed, agility, biomechanics, coordination, strength markers, & benchmark them against age-appropriate global standards. This creates a level playing field that India’s youth sports ecosystem has historically lacked. 

The bigger shift is that we are moving talent identification away from subjective human judgement to measurable, machine-led insights. Whether a child is in a metro school or a Tier-3 academy, the quality of assessment remains the same. That uniformity has been missing in India’s sporting pipeline for years.

Ultimately, our AI-first model gives every child access to reliable feedback and developmental pathways early in their sporting journey. It empowers PE teachers with coach-grade insights, helps parents understand their child’s true potential, and enables institutions to systematically nurture talent rather than rely on sporadic, anecdotal evaluations. That’s how we are rebuilding India’s youth sports foundation with data, objectivity, and accessibility at the centre. 

Visual AI systems often struggle with accuracy across diverse demographics. How has KPro tested and validated its 55+ AI models to ensure fairness, reliability, and low bias across different body types, age groups, and regional contexts?

One of the biggest challenges in sports-tech globally is ensuring that visual AI works accurately across diverse human profiles. In a country as varied as India, that becomes even more critical.

At KPro, every one of our 55+ AI models has been trained, stress-tested, and validated on one of the most heterogeneous datasets in Indian youth sports covering different body types, genders, age groups, and regional variations in movement patterns. We’ve captured data from children in metros, small towns, government schools, private academies, and even tribal belts to ensure we’re not building for only the top 5%.

The company follows a three-layer validation approach:

1. Diverse Data Capture: Our models learn from lakhs of annotated videos collected across states like Uttar Pradesh, Tamil Nadu, Karnataka, Odisha, and the Northeast. This ensures the AI recognizes real Indian diversity, different body proportions, playing surfaces, and even local movement styles.

2. Human-in-the-Loop Quality Checks: Every model iteration is benchmarked by certified coaches, sports scientists, and biomechanists. If the AI’s interpretation diverges from expert consensus, it goes back into retraining.

3. Bias & Error-Rate Monitoring: We run bias audits to compare model accuracy across gender, age clusters, and body types. Any skew beyond acceptable limits triggers targeted data collection to fix the imbalance. This is a continuous process, not a one-time exercise.

Because of this rigorous cycle, our models today operate with high reliability even in low-light environments, varied clothing, different terrains, and non-standard camera angles, conditions typical of Indian schools and academies.

The outcome is simple: whether it’s a 7-year-old in Mizoram or a 15-year-old in Mumbai, KPro’s AI assesses them with the same level of precision. Ensuring fairness and low bias isn’t a checkbox for us, it’s the foundation on which our entire product stands.

The Khiladi Ability Index (KAI) is built on FMS and LTAD frameworks. How have you adapted these global sports science benchmarks to India’s constraints such as limited play spaces, uneven nutrition levels, and inconsistent PE programs?

The Khiladi Ability Index (KAI) is rooted in globally accepted frameworks like Fundamental Movement Skills (FMS) and Long-Term Athlete Development (LTAD). But we recognised early on that simply importing these models wouldn’t work for India’s realities. 

So, we re-engineered these benchmarks to reflect the constraints and lived experiences of Indian children, smaller play areas, uneven nutrition levels, inconsistent PE exposure, and varying access to sports infrastructure. 

The three major adaptations:

1. Calibrating for India’s Environment: FMS assessments designed for ideal indoor courts don’t match Indian conditions. We adapted every test to work on real surfaces. Children actually use dusty grounds, corridors, multipurpose fields, & tuned our AI models to normalise variations in space and surface quality.
   
   

2. Contextualising Scores to Nutrition & Growth Patterns: Growth trajectories in India can differ significantly because of dietary diversity and nutritional gaps. So KAI benchmarks are built on India-specific percentiles rather than Western growth models. This ensures a child isn’t unfairly under-scored simply because they come from a different nutritional baseline.
   
   

3. Building a Scalable Framework for Low-PE Exposure: Many children get barely 20–30 minutes of structured PE a week, if at all. That means we can’t assume prior skill training. KAI therefore focuses heavily on raw motor ability, coordination, and movement literacy rather than sport-specific refinement. It’s designed to meet children where they are.

The result is a system that is globally informed but locally relevant. KAI doesn’t compare Indian kids to idealized international standards, it maps them to a realistic developmental pathway informed by India’s social, economic, and environmental context.

Our goal is simple: give every child an accurate, achievable, and scientifically grounded roadmap, irrespective of where they study, how much space they get to play, or what resources their school can provide.

Scaling in tier-3 and tier-4 towns requires digital readiness and ground-level adoption. What operational frictions have you observed in these markets, and how is KPro balancing affordability with consistent assessment quality?

Tier-3 and tier-4 India is where the real sporting talent sits, but it’s also where operational friction is the highest. Scaling in these markets requires far more than a good product, it demands patience, affordability, and on-ground trust-building. 

The biggest challenges we see are uneven digital readiness, limited PE manpower, and a general hesitation to adopt new assessment systems. Many schools don’t have trained coaches, smartphone quality varies, internet connectivity fluctuates, and teachers are overworked. These are structural issues, & not behavioural ones. 

KPro is solving these frictions without compromising quality:

1. Offline-First, Low-Device-Dependency Tech: Our AI can run effectively on entry-level smartphones and works in low-light or outdoor conditions. We’ve ensured the model quality doesn’t drop even when the digital environment isn’t ideal but something critical in smaller towns.

2. Affordable, Volume-Based Pricing: Affordability is non-negotiable. Instead of charging premium per-child fees, we use a volume-based pricing model that keeps costs extremely low for schools and academies, while still giving them access to high-quality, standardized sports assessment.

3. On-Ground Enablement Teams: We deploy field teams to train PE teachers, build awareness, and handhold schools during the first few cycles. Once they see the accuracy and ease, adoption accelerates organically. 

4. Consistent Quality Through AI Standardisation: Whether assessments happen in Lucknow, Lakhimpur or Ladakh, the AI ensures the scoring remains consistent. This removes the dependence on local infrastructure or specialised coaches and a huge barrier in tier-3 and tier-4 regions.
   
   

Our approach is simple: design for Bharat first, metros later. If the product works in the most resource-constrained environments, it will scale everywhere else. That’s how we are democratizing access to quality sports science not as a premium offering, but as a fundamental right for every child.

⁠As KPro positions AI as an objective layer for early talent evaluation, what concerns have emerged from coaches or academies about over-reliance on algorithms or the potential reduction of nuanced skill development to numeric metrics?

Whenever you bring an objective, data-driven layer into a field that has historically relied on instinct and experience, some concerns are bound to emerge. 

Coaches and academies have shared two primary apprehensions: first, that AI might reduce a child’s abilities to just numbers; and second, that institutions may start over-relying on algorithms at the cost of human judgement. These are legitimate concerns, because sport is deeply human and  it cannot be fully captured in metrics alone.

Our approach at KPro has been to position AI clearly as a support system, not a decision-maker. The technology never selects or rejects talent but it only highlights objective insights on movement quality, coordination, agility, and physical literacy. Coaches still decide what that means in the context of a child’s personality, discipline, mindset, and tactical understanding.

We also ensure that every score or KAI metric comes with development-focused recommendations. The aim is not to label children, but to give them a structured roadmap. This is why we work closely with coaches, explaining what the models measure, how they were built, and where their limitations lie. Transparency builds trust.

At the end of the day, AI can neither replace a coach’s intuition nor is it meant to. What it can do is remove subjectivity, save time, and uncover aspects a coach might miss in a large group session. When both work together, the athlete benefits the most. That’s the philosophy behind our entire system.

With funding and rapid expansion, how is KPro building governance, audit systems, and data-privacy safeguards to ensure responsible scaling without compromising scientific rigor or the integrity of athlete data?

As we scale rapidly, the single-most important priority for us is responsible growth both in terms of scientific rigor and data governance. Youth assessment data is extremely sensitive, and it has to be protected with the same seriousness as health or academic records. 

We have built our governance framework on three pillars such as strong data-privacy safeguards, transparent audit systems, and strict scientific oversight.

First, all athlete data is encrypted end-to-end, stored on secure Indian servers, and governed by strict access controls. No raw video or identifiable information goes to any third party without explicit institutional consent. Parents and schools retain ownership of the data, we only process it to generate assessments.

Second, we have put auditability at the core of every workflow. Every model release, every scoring update, and every protocol change undergoes scientific review and internal testing. We maintain logs of model decisions, accuracy reports, bias checks, and field-testing cycles to ensure traceability. This is essential for scaling without losing rigor. 

Third, we have created an internal Sports Science & Ethics Board that includes biomechanists, data scientists, and external advisors. Their role is to continuously monitor model fairness, validate scoring frameworks, and ensure that our rapid growth does not dilute scientific integrity.

Responsible scaling is not optional for us perhaps it’s foundational. When you’re dealing with children’s developmental data, trust is everything. We want KPro to set the benchmark for how sports-technology companies in India treat privacy, ethics, and scientific discipline. If we get this part right, scale automatically becomes sustainable.