What Nativity Mobile
delivers.

Native iOS work, whatever shape the engagement takes. New apps, rescued ones, hybrid migrations, or the backend layer beneath.

New Development

Build your native app from scratch.

Modern native iOS apps built with Swift and SwiftUI. Architecture, implementation, and delivery with a focus on correctness, maintainability, and the kind of polish that only comes from building directly on the platform from its inception.

Whether you're starting from a napkin sketch or a fully specced Figma file, the process starts the same way: understanding what the app actually needs to do and making the architecture decisions that will either support you or haunt you for the next five years. That means choosing the right patterns, the right dependencies (or lack thereof), and building something your team can maintain and extend long after the initial engagement ends.

Rescue & Stabilization

Codebase in trouble? No documentation? No problem.

Your original developers are gone. Nobody knows how the app works. It's still in UIKit, maybe even some Objective-C. You need changes shipped yesterday. We can walk into a codebase cold, read what's there, figure out what it does, and get you back to shipping. Legacy code is not a problem when you've been writing it since before it was legacy.

This is the engagement that starts with a phone call that sounds like a crisis, because it usually is. The app is in production, users depend on it, and the people who built it aren't available anymore. The first step is always the same: read the code, map the architecture, identify what's fragile, and stabilize it before making any changes. Triage first, then ship.

Migration

Your hybrid app is holding you back. Go native.

Your hybrid app stutters, lags behind every iOS release, doesn't look right on either platform, and your users can feel it. They just can't tell you why. We can. Cross-platform was a shortcut, and now it's a ceiling. We'll build the native app you should have built in the first place.

The good news is you've already validated the product. You know what it does, you know who uses it, and you know what's not working. A migration isn't starting over - it's rebuilding on a foundation that won't fight you every time Apple ships a new SDK.

API & Backend

The server-side layer that powers your dynamic app.

Building a good mobile API requires thinking from both sides of the request. What the server wants to send and what the client actually needs are rarely the same thing. That's how you end up with bloated responses, constantly broken contracts, and pagination that makes no sense on a phone. We design mobile-first APIs that serve the client properly, and when the engagement calls for it, build them too. Your backend team gets an engineer who speaks both languages.

Most mobile API problems come from the same place: the API was designed by a backend team that has never built a mobile client. The result is endpoints that make perfect sense from a database perspective and no sense from a phone on a cellular connection. We bridge that gap - either by designing the API your backend team builds, or by building it ourselves.

Software that uses AI as a core part of how it works. Not AI as a feature bolted onto a product. Not prompt engineering against a hosted API and calling it a system. Pipelines, retrieval, and orchestration that turn LLMs into shippable software.

Agentic Pipelines

Agents that do real work inside your product.

Your product needs AI to do more than answer a single prompt. Agents that read, decide, fetch, and act in sequence, working toward outcomes that matter to a user. The mechanics behind software that uses AI as a core part of how it works.

In practice, that means multi-step orchestration of LLM calls and tool invocations, with progressive tool disclosure to keep agent context manageable. An agent given every tool at once fails in surprising ways. Narrowing the tool surface to what the current step needs makes behavior tractable, failures legible. The pattern is closer to a state machine with model-driven transitions than to a chat loop.

Semantic Recommenders & Classification

AI that ranks and categorizes inside your product.

Your product needs to show users the right thing at the right time. Recommendation, classification, and ranking that come from understanding your data, not just patterns in the words. Results that feel obvious in hindsight and would be hard to engineer any other way.

A recommender that knows your catalog beats one that knows the world. The difference is in how the retrieval is structured, how the prompt is constructed, and what the model is asked to do with the data it sees. Done well, the system produces results that look obvious after the fact and would be expensive to engineer any other way.

Retrieval Architectures

AI grounded in your data, not generic guesses.

Your product has data the AI needs to use: catalogs, customer records, internal documents, structured business knowledge. The work is in how that data gets to the model. Structured retrieval that uses what you already know about your own data, not generic similarity search hoping to find the right thing. The difference between AI that answers from your data and AI that makes things up.

Vector search works well when the question shape is fuzzy and the corpus is unstructured. Most real product retrieval is neither. SQL queries, structured filters, and pre-indexed domain knowledge get you further, faster, and at a fraction of the cost. The model is the reasoning layer, not the index.

Cost-Engineered Orchestration

AI features that don't blow up your cloud bill.

Your product can use AI without watching the cost compound on every user action. The engineering is in treating cost as a design dimension: choosing providers and pricing models that fit your workload, controlling when expensive operations get invoked, caching the work that doesn't need to repeat. The difference between AI you keep in production and AI you quietly turn off when the bill arrives.

Most teams pay for every model call and every API hit, then act surprised when the bill compounds. Cost-aware orchestration treats cost as a design dimension. Pricing models matter: a subscription tier can be cheaper than per-token billing for a steady workload. Tool surface matters: an agent that can call web search on every turn will. Caching matters: the same query asked twice should not become a charge twice. The engineering is in deciding what doesn't need to happen.