What Nativity Labs
delivers.
The work all runs on one foundation.
AI products and native apps are different surfaces. Underneath both is the same thing: systems work, the layer everything else depends on.
Strip away the surface and most modern software is a client talking to a server. A mobile app is a sophisticated network client. So is an AI product. What separates the ones that work from the ones that don't is the quality of that conversation: how the data is structured, what gets sent and what gets withheld, how the client and server stay in agreement as both change. Get that interaction right and everything on top of it gets easier. Get it wrong and no amount of polish on the surface will save it.
With an AI product, the system is the work. Designing it and building it is the job. Getting the right context to the model, constructing the request that gets the most out of it, forcing the response back into a structure the rest of the system can actually use, and measuring whether any of it is good instead of just plausible. That is retrieval, orchestration, prompt design, structured output, and evaluation.
Getting a system working is real engineering. Keeping it working is harder, and it is a different skill. A system that holds up under real traffic, and still behaves a year later, is systems engineering. It is the difference between a system that stays in production and one that has to be torn out and rebuilt.
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.
Adding a single LLM call to your product is trivial. An agentic pipeline is a different kind of work: a system that reads, decides, fetches, and acts 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.
That means multi-step orchestration of LLM calls and tool invocations, often with custom tools built for the specific product. It also means progressive disclosure: controlling which tools the model can reach on each turn instead of handing it everything at once. An agent given every tool up front fails in surprising ways. Narrowing the available tools to what the current step needs keeps behavior tractable and 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 engineered, 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 over embeddings works well when the question shape is fuzzy and the corpus is unstructured. Most real product retrieval is neither. This is where naive RAG falls down: 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 can afford to run and AI that becomes too expensive to keep.
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.
Native mobile apps, built right. New builds, migrations off hybrid, codebase rescues, and the API layer underneath. Fully native, never cross-platform. Never a compromise.
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.
Mobile-Friendly APIs
APIs designed for the app that consumes them.
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 is how you end up with bloated responses, broken contracts, and pagination that makes no sense on cellular. We design mobile-first APIs that serve the client properly, and build them too when the engagement calls for it. 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 mobile perspective. We bridge that gap, either by designing the API your backend team builds, or by building it ourselves.