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When the way software is built changes, we help companies adopt it. For over a decade, that meant bringing React Native to production across mobile, web, desktop, and TV. Now we are expanding that same mission to AI-native engineering: helping teams ship software with agents, safely and at scale.... show more
24,865 görüntüleme • 5 ay önce •via X (Twitter)
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Our offer now covers four areas: - Engineering: ship products with AI-native teams - Infrastructure: keep agentic delivery reliable - Adoption: turn AI tools into team practice - Governance: scale AI under enterprise constraints Each one solves a different part of the same shift: moving from AI tools to AI-native software delivery.

The first part is Engineering. React and React Native remain our foundation. We still build products, ship features, solve architecture problems, improve performance, and handle migrations. What changes is the delivery model: senior engineers working with AI-native workflows to move faster without lowering the bar.

The second part is Infrastructure. More agent output does not automatically mean faster shipping. The bottleneck moves into testing, review, CI/CD, devices, releases, and observability. We build the systems around that: agentic QA, custom engineering agents, and delivery infrastructure that can keep up.

The third part is Adoption. Most teams already have AI tools. Far fewer have a working AI-native engineering practice. We configure the stack, train engineers, measure usage and ROI, and turn scattered experimentation into a repeatable way of building software.

The fourth part is Governance. Enterprise teams cannot scale AI without security, compliance, data controls, and clear agent boundaries. We help teams deploy private models, audit AI tooling, reduce exposure, and adopt AI under the constraints their business already has.

This is the next chapter for Callstack. React Native is still our foundation. AI-native engineering is where we are taking it next. We are excited for what’s to come. Stay tuned!

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Excited to see you bringing that same decade of expertise to make AI-native engineering safe and scalable.

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AI-native engineering needs AI-native planning too. Teams need scope, cost, timeline, risks, and launch path before implementation starts. We mapped it here:
