Yorker Application for Cricket training using AWS AI

About the Client

Yorker Limited is a New Zealand-based sports-technology company founded to solve one of cricket’s most persistent problems — the inability of grassroots and amateur players to properly track and manage their bowling workloads. Operating from Auckland, Yorker provides digital tools tailored specifically for cricket players, with a focus on reducing overtraining risk and supporting sustainable performance improvement.

Key business drivers included:

• Providing bowlers with accessible, AI-driven training guidance regardless of access to professional coaching
• Enabling coaches and team managers to monitor cumulative bowling loads and prevent overuse injuries
• Delivering personalised speed improvement recommendations based on individual player data
• Building a scalable, cloud-native platform capable of growing with the user base across New Zealand and beyond

Project Background

Cricket bowlers — particularly at grassroots, club, and academy levels — lack access to the sophisticated workload management tools available to professional teams. Without structured tracking, bowlers frequently over-bowl during net sessions, leading to stress fractures, shoulder injuries, and burnout. Coaches rely on manual scorebooks and memory rather than data. Yorker was conceived to democratise performance science for cricket.

Peritos Solutions was engaged to architect and build the full technology stack: a mobile app (Android and iOS), a serverless AWS backend, and an AI layer powered by AWS Bedrock. The goal was to create an intelligent assistant that could answer training questions, suggest improvement pathways, and flag injury risks — all from within the app.

Requirements

• A mobile application for Android and iOS allowing bowlers to log net session bowling loads by delivery type and intensity
• AI-powered training suggestions using AWS Bedrock, providing personalised speed improvement drills based on player history
• Injury prevention intelligence — detecting when cumulative loads approach dangerous thresholds and alerting the player or coach
• Natural language Q&A interface powered by a foundation model, enabling users to ask questions like “How can I bowl faster?” or “Am I at risk of injury?”
• Secure user authentication via Amazon Cognito with individual player profiles and coaching team access
• Serverless, cost-efficient infrastructure using AWS Lambda and API Gateway to scale with demand
• Push notification capability via Amazon SNS for load reminders and AI-generated insights
• Full observability and alerting via Amazon CloudWatch

Solution Overview

The Yorker platform is built on a fully serverless AWS architecture, with AWS Bedrock at the heart of its AI capabilities. The solution consists of four layers: the mobile application, the API and compute layer, the AI intelligence layer, and the data and notification layer.

Technology & Architecture

Scope & Feature List

AWS Bedrock Integration Detail

AWS Bedrock is the intelligence layer of Yorker. Lambda functions invoke Bedrock’s foundation model API, passing structured player data and a custom-engineered prompt that contextualises the AI response to cricket-specific training science. Peritos Solutions developed a prompt library covering speed improvement, load management, injury prevention, and performance analytics — each prompt template retrieves the relevant player context from DynamoDB before calling Bedrock.

The Bedrock integration handles four primary AI use cases:

• Speed Improvement: Player enters current pace figures; Bedrock returns a structured 4-week training plan with specific drills, strength exercises, and technique cues personalised to the bowler’s style.
• Injury Risk Q&A: Player asks “Am I bowling too much?” and Bedrock analyses their current acute-to-chronic ratio and provides a risk rating (green/amber/red) with an explanation and recommended action.
• Training Load Questions: Open-ended natural language questions about periodisation, recovery, rest days, and session structure are handled by Bedrock with cricket-specific context injected via prompt engineering.
• Performance Insights: At the end of each week, Bedrock generates a narrative performance summary comparing the player to their own historical averages and recommending adjustments for the following week.

Implementation Approach

Peritos Solutions followed an iterative, sprint-based delivery model across 12 weeks:

• Weeks 1–2: Requirements workshops, AWS architecture design, Bedrock model selection and prompt strategy, Cognito user pool setup, DynamoDB schema design
• Weeks 3–4: API Gateway and Lambda scaffolding, Cognito authentication flows, mobile app shell (Android & iOS), core bowling load data model
• Weeks 5–6: Bowling load tracker UI, DynamoDB integration, session logging, acute-to-chronic workload calculation engine
• Weeks 7–8: AWS Bedrock integration — speed improvement prompt, injury risk engine, natural language Q&A endpoint, Lambda prompt orchestration layer
• Weeks 9–10: Coach dashboard, SNS push notification integration, CloudWatch observability setup, performance insights Bedrock module
• Weeks 11–12: End-to-end testing, UAT with Yorker team, Google Play submission, production go-live, hypercare support

Challenges & Solutions

Benefits to the Client

• AI-powered training suggestions now accessible to grassroots bowlers — democratising elite-level performance science
• Real-time injury risk monitoring reduces the likelihood of overuse injuries through proactive, data-driven alerts
• Coaches gain team-wide visibility of bowling loads without manual tracking, saving time and improving player welfare
• 100% serverless architecture means Yorker scales automatically with the user base at minimal infrastructure cost
• AWS Bedrock eliminates third-party AI subscription costs — all AI compute is metered and cost-effective on AWS
• Secure, privacy-compliant user data management via Amazon Cognito — no player data shared with third parties
• Push notifications via SNS keep players engaged between sessions and reinforce consistent training habits
• Full observability via CloudWatch ensures the team can monitor usage, debug issues, and maintain SLA targets

Support & Next Steps

Peritos Solutions provided two weeks of hypercare post-launch, monitoring Lambda function performance, Bedrock API response quality, and DynamoDB throughput. The Yorker app is now live on the Google Play Store (com.yorker) and is actively being used by cricket players and coaches across New Zealand.

Planned next phases include:

• iOS App Store release (in progress)
• Bedrock fine-tuning with real Yorker user data to further improve suggestion quality
• Video analysis integration — allowing Bedrock to analyse bowling action video clips for technique feedback
• Team/club management features — enabling clubs to manage multiple players and compare squad load data
• Integration with wearable devices for automated session detection and delivery counting