See how Coherent Solutions helped a leading North American food delivery platform reduce complex engineering cycle times by 50–80% with AI-native delivery.
A restaurant technology company needed to move its 13-year-old, iOS-only tablet application to Flutter and extend it to Android and Windows along the way. Coherent Solutions created an AI-driven development plan and delivered a working pilot in about six weeks. The pilot proved an AI-native migration approach on real, production-grade legacy code, while keeping how the app looked and behaved the same for end users.
At a glance
Client: A U.S. restaurant technology company with a platform supporting multi-location foodservice operations.
Industry: Restaurant Technology.
Challenge: Migrating a 13-year-old, undocumented, iOS-only application Flutter across three platforms, with no visible change to the end-user experience.
Solution: A scoped, AI-driven migration pilot developed with defined SDLC-mapped skills and deliberate human checkpoints.
Outcome: Coherent’s team of two engineers delivered the pilot scoped in about six weeks, 75% faster than the client’s initial pilot timeline. The early pilot delivery cut the full migration's projected timeline from more than two years to about nine months.
Coherent's role: Long-standing mobile development partner, leading the end-to-end development of an AI-native migration pilot. Coherent also brought digital experience and data & analytics expertise to refine the app's user experience and how the client used the app's data.
Client overview
The client runs a restaurant technology platform and is operating under new ownership following an acquisition. The mobile application at the center of this engagement was built 13 years ago and written natively in Objective-C and Swift for iOS tablets only. Coherent Solutions has served as the client's mobile development team for years and previously delivered a full rewrite of the same application using a traditional development approach.
The challenge
The client’s leadership team wanted to add the application to Android and Windows in addition to iOS, and they wanted it done quickly. Their team, working manually and without Flutter experience, spent six months on the migration with little progress.
The application was a genuinely hard migration target. Its interface was almost entirely dynamic, with screens generated from configuration rather than built individually. As a result, the migration didn’t support screen-by-screen porting; instead, the underlying patterns needed to be rebuilt.
Additionally, there was no technical documentation, automated test coverage, or formal requirements record. The knowledge lived entirely in the heads of the app’s engineers. The app also had to communicate with two parallel server environments, on-prem and cloud, through fixed APIs. And the bar was high for functionality: the rebuilt app had to look and feel identical to users — no visible seams and no rebranding.
Our approach
Coherent’s team built a pilot in six weeks and proved it on the client's own legacy code.
The first attempt used a fully autonomous, agentic AI setup, pushing to minimize human involvement in the migration process. The team hit a wall fast: the app's dynamic UI didn't tolerate loose interpretation, and the context needed to track its complexity burned through the AI's usable window within hours.
So, the team adapted in real time, pivoting mid-engagement. They rebuilt the approach around a defined set of AI-driven skills mapped to each stage of the SDLC — requirements gathering, planning, code generation, testing — with deliberate human checkpoints at every stage. Catching a wrong turn early is far cheaper than untangling it three phases later, and that perspective ensured the team could deliver a working pilot faster than expected.
Additionally, Coherent’s team didn't migrate the entire application at once. They scoped the pilot around the migration of a single, complete user journey and locked that in with the client before writing any new lines of code.
The solution
Coherent’s two engineers delivered the full pilot scope in about six weeks, completing roughly 18%–20% of the application by code. Prior to Coherent joining the project, the client's larger team spent six months on a comparable slice without finishing. Coherent worked with the same legacy codebase and complexity, but completed the pilot in a fraction of the time.
The pilot didn't just produce migrated code. It validated the cost and timeline model for the entire migration, as well as the proposed team structure and roadmap for the project. In other words, the pilot gave the client's leadership team everything it needed to confidently greenlight the full engagement.
The impact
Before the pilot, the client's team estimated that the full migration would take more than two years to complete. After Coherent‘s pilot proved its AI-driven approach, the projected timeline for completing the entire migration came in at six to nine months, with a team of three developers and a part-time architect — a fraction of the original timeline and headcount.
This is what an AI-native migration approach looks like in practice: real speed on real legacy code, with zero impact on the user experience or shortcuts on quality, testing rigor, and fidelity. The client didn't just get a migration plan — Coherent provided a pilot as proof, and a faster path forward.
What’s next
Coherent's Continuous Delivery Loop (CDL) is the operating framework we use to move clients from traditional development toward AI-native delivery through checkpointed steps and focused technology investment. CDL builds human oversight into the process across four activity centers. Those are problem identification, validation and exploration, design and engineering, and observation and scale. This pilot is a live example of CDL in action. The checkpoint-driven model that made this migration faster is the discipline we bring to our AI-native engagements.
