Case Studies

Selected delivery examples with defined scope, visible ownership, and concrete outcomes.

These examples show the type of delivery and architecture work Lightheart brings into current engagements: defined workstream ownership, senior technical judgment, and outcomes that survive handover.

What these cases show

Product leadership under deadline pressure

Platform rebuild and operational scalability

Enterprise data pipeline reliability

Defined workstream ownership inside real delivery constraints

Klarna × Shopify integration

Commerce integration delivery for Klarna on Shopify, combining product direction, partner alignment, and launch execution.

Role

Product leadership and delivery execution.

Scope

End-to-end product delivery from scoping through launch.

Environment

Partner-facing commerce integration in Shopify.

Outcome type

Launch-ready product delivery.

! Challenge

Klarna needed to ship a Shopify integration that worked reliably across merchant setups, with tight coordination between product, engineering, and partner requirements, on a deadline that did not leave room for misalignment.

≡ What Lightheart owned

Lightheart led product scoping, implementation prioritization, and launch coordination, making sure engineering, QA, and partner dependencies stayed connected throughout.

→ What was delivered

A production-ready Shopify integration, built and maintained with clear scope boundaries, partner alignment, and structured handover documentation.

✓ Outcome

The integration launched on schedule, with clean merchant onboarding and no post-launch rework from scope ambiguity.

Current Lightheart capability this reflects

Product workstream ownership where scope, partner coordination, release timing, and handover all need to stay aligned.

Eyecons, full platform rebuild for scalability

Full platform rebuild for an AI-powered live sports recording product that needed to scale across venues without operational fragility.

Role

AI Tech Lead and Cloud Architect.

Scope

Application architecture redesign, infrastructure rebuild, and Kubernetes platform.

Environment

AI-powered live sports SaaS on Kubernetes.

Outcome type

Platform scalability and operational reliability.

! Challenge

Eyecons had an AI-powered live sports recording product that needed to scale across multiple venues, but the existing application architecture and infrastructure could not support it. The entire stack needed to be rebuilt for reliability and growth.

≡ What Lightheart owned

Lightheart rebuilt the full application architecture and infrastructure, redesigning the system on Kubernetes with a microservices approach, making it scalable, maintainable, and operationally stable.

→ What was delivered

A completely re-architected platform on Kubernetes: new application structure, new infrastructure, and a scalable foundation for AI-powered live sports recording across venues.

✓ Outcome

Eyecons went from a platform that could not scale to one built for multi-venue deployment, with an architecture their team could extend and operate confidently.

Current Lightheart capability this reflects

Platform rebuilds, cloud architecture, and reliability-focused delivery for products that have outgrown the original stack.

Inter IKEA enterprise ETL pipeline

High-volume data processing and validation work for enterprise supply chain operations where data quality failures could not leak downstream.

Role

Cloud Architect and technical design lead.

Scope

Pipeline architecture, data validation design, and operational reliability.

Environment

Enterprise supply chain data environment.

Outcome type

Operational data reliability.

! Challenge

Inter IKEA needed an ETL pipeline capable of processing high-volume supply chain data with strict validation requirements, where failures in data quality would directly impact enterprise operations.

≡ What Lightheart owned

Lightheart designed the ETL pipeline architecture, defined the validation approach, and made the infrastructure decisions that shaped long-term operational resilience.

→ What was delivered

An ETL architecture handling high-volume data processing with built-in validation stages, designed for maintainability and clear operational ownership.

✓ Outcome

The pipeline supported enterprise-scale processing requirements reliably, with a validation framework that caught data quality issues before they reached downstream systems.

Current Lightheart capability this reflects

Defined data and integration workstreams where architecture, validation, and operational resilience matter as much as implementation speed.

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