Overview
Qubika is helping Throne, a privacy-first wishlist and gifting platform for content creators, accelerate its product development capabilities.
Throne enables fans to purchase gifts from creators’ public wishlists without exposing sensitive personal information such as home addresses. The platform spans web, iOS, and Android and also supports brand partnerships and charitable initiatives.
Qubika has been using Claude to drive AI-accelerated engineering within our team, using Claude Code, Claude Chat, and the Qubika Agentic Framework (QAF) to increase development velocity.
The Challenge
Throne operates a multi-platform product serving creators and fans, creating a broad engineering surface across backend services, web, iOS, Android, product development, and compliance, and customer support.
As the platform grows, the team needs to deliver new functionality quickly while maintaining quality, privacy, and operational efficiency. In our work, Qubika identified an opportunity to use Claude to significantly accelerate development.
Development Acceleration
Beginning in April 2026, Qubika incorporated Claude Chat, Claude Code, and Qubika’s Agentic Framework (QAF) into our engineering workflows.
The Qubika teams use Claude and QAF for both bug fixing and feature development, as well as for generating and refining product and engineering artifacts.
The measured impact has been significant:
- Estimated effort without AI during measured period: 1,376 hours
- Effort with Claude + QAF: 657 hours
- Effort reduction: 52%
- Average delivery acceleration: 2.1× faster
Rather than treating AI as a separate development workflow, Qubika integrates Claude and QAF into our day-to-day engineering practices, allowing our teams to apply agentic development techniques to real production work.
Model Optimization & Scale
As Claude-powered capabilities expand, Qubika’s focus extends to optimizing model selection and production economics.
Different workloads have different requirements for reasoning quality, speed, and cost. Qubika can therefore right-size Claude models-including Opus, Sonnet, and Haiku-according to the needs of each use case.
Optimization focuses on model selection by workload, prompt and context optimization, response quality, latency, token consumption, cost, and reliability at production scale.
This creates a pathway for increasing Claude Code consumption sustainably as adoption grows.
Sustained Adoption & Governance
Qubika continuously evaluates quality, performance, and cost across AI workloads, identifying where Claude delivers the strongest results and where additional use cases can generate measurable business value.
For example, this creates an opportunity to consolidate workloads toward Claude where it demonstrates stronger performance, economics, or developer experience. There are also opportunities to start using the Claude API for in-product integrations, and add the Claude intelligence layer to power customer-facing and operational capabilities.
Results & Why It Matters
Qubika’s work illustrates a practical model for adopting Claude in engineering. The use of Claude Code, Claude Chat and the Qubika Agentic Framework have translated into materially faster engineering delivery including a 52% reduction in estimated engineering effort, and a 2.1× average acceleration in delivery.
