Xovian Aerospace accelerates satellite-based decision intelligence with Claude Code and Claude Sonnet 4.6

‍Project Overview

Xovian Aerospace is building an AI-native radio-frequency (RF) satellite infrastructure platform designed to deliver actionable, decision-grade intelligence for maritime, aviation, and other high-stakes sectors. The platform transforms geospatial data into real-time insights, empowering customers to make informed decisions in complex environments where 360-degree visibility and operational readiness are critical for mission planning and success.

As the company builds its space-to-cloud intelligence platform, it needs to move quickly without compromising engineering quality, reliability, or developer productivity. With a lean engineering team solving complex problems across data ingestion, signal processing, analytics, platform reliability, and user experience, Xovian required an AI coding partner that could accelerate delivery velocity without introducing risk.

Minfy Technologies engaged with Xovian to deploy Claude Code, powered by Claude Sonnet 4.6, as an AI coding assistant embedded directly within the engineering team’s development environment. The objective was to reduce iteration cycles, improve code quality, and enable the team to move from concept to production-ready functionality faster — across all core engineering workflows

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The challenge

Developing a satellite-based decision intelligence platform requires teams to solve complex engineering problems across data ingestion, signal processing, analytics, platform reliability, application development, and user experience. For Xovian, the mandate is clear: bring Platform to market faster while maintaining the quality required for mission-critical sectors such as maritime and aviation. Key priorities included:

• Accelerating development cycles for new platform capabilities.
• Improving developer productivity across complex codebases and technical workflows.
• Maintaining high code quality, consistency, and maintainability as the platform scales.
• Reducing time spent on repetitive coding, debugging, documentation, and refactoring tasks.
• Supporting faster experimentation and iteration for customer-facing intelligence features.

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The solution

Xovian is using Claude Code with Claude Sonnet 4.6 as an AI coding assistant for the Xovian engineering team. Claude Code works inside the developers’ environment to reason through code, generate implementation options, explain unfamiliar modules, identify improvement opportunities, and accelerate common engineering tasks.

With Sonnet 4.6’s balance of speed, reasoning, and coding capability, the team can apply Claude across day-to-day development workflows — from building features and writing tests to refactoring services and improving documentation — while keeping developers in control of final decisions and code review.

Implementation details

• Xovian’s Skylobe platform is based upon event-driven microservices architecture with specific usage of design patterns such as event sourcing & CQRS (when processing AIS & ADS-B data) and database per service (for different microservices).
• Tech stack includes React.js, Node.js, Amazon DocumentDB, ElastiCache, Redpanda/Kafka/Valkey streams, Amazon MQ for RabbitMQ, and AWS Secrets Manager & KMS. ‍
• Product: Claude Code (CLI / IDE agentic coding tool), current release line v2.x. ‍
• Model: Claude Sonnet 4.6 — API model string claude-sonnet-4-6.

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How Claude supports Xovian’s Development

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Business Benefits

Minfy’s deployment of Claude Code directly addresses Xovian’s engineering and delivery challenges, with quantified impact across speed, quality, and team productivity:

• Faster time to market — 34% reduction in feature build time: Feature build time dropped from ~80 hours to ~53 hours, measured across 40+ features. ‍
• Higher development quality — test coverage up 31 points: Code test coverage rose from 45% to 78% on new services, including Subscription, Notifications, and Portal, where Claude generated test outlines and event schemas from the start. ‍
• Improved developer productivity — 27% more tasks per sprint: Sprint task throughput rose 27% team-wide over a 180-day period.
• ‍Faster experimentation — 38% less time on bugs and docs: Bug-fixing and documentation time fell from 35% to 22% of sprint time. Satellite and vessel controller work dropped from 6–7 days to under 2; ground station sync was live in 45 days, saving ~3 weeks of setup. ‍
• Scalable engineering practices — 28% overall productivity gain: 8 microservices built on shared patterns were 40% faster to set up with zero inconsistencies; consistent Kubernetes/Docker configs cut deployment errors by 45%; API docs are now auto-generated. Combined gain: 28% productivity improvement, against a 25–30% target.