Consumer Apps

What is consumer app development?

Consumer app development is the design, engineering, and launch of native or cross-platform mobile applications built for individual users — as opposed to internal tools or B2B software. AI-powered consumer apps add intelligent features (recommendations, generation, vision, natural language) that materially change what the product does, not just its marketing.

The bar is unforgiving. Users decide whether your app is worth keeping inside the first session, and they compare you to the best apps on their phone. We build apps that survive that comparison — fast, polished, and with AI that actually earns its keep.

Key terms used on this page:

Native development: Building separate apps in Swift (iOS) and Kotlin (Android) using each platform's first-party SDKs.
Cross-platform: A single codebase that compiles or runs on both iOS and Android — typically React Native, Flutter, or Expo.
On-device AI: Inference that runs on the user's phone using Core ML, TensorFlow Lite, or MLC, with no network round-trip.
ASO (App Store Optimization): The discipline of ranking and converting in the App Store and Play Store search and browse surfaces.
D1 / D7 / D30 retention: Percentage of users who return one, seven, and thirty days after install — the metrics that decide whether the app has a future.

How does the consumer app build process work?

We organize a v1 build into four phases that compound. The phases are sold as one engagement, not separately, because skipping any of them is what produces the apps that look good in TestFlight and die in the App Store.

1. Discovery and AI scoping — We study the target user, the competitive set, and the specific AI capability that creates a real moat. We benchmark the AI on representative data before committing to it. Output: a product spec, an AI evaluation report, and a non-AI fallback for every intelligent feature.

2. Design and prototype — Pixel-level interface design, interactive prototype, usability testing with real target users. We design the AI and the UX together — the worst consumer AI features are the ones that were specced after the screens were drawn.

3. Build and ship — Two platforms (iOS and Android), a real backend, observability, crash reporting, analytics, and a staged App Store and Play Store rollout. Output: a v1 in the stores with the instrumentation needed to learn from launch.

4. Grow and optimize — Cohort analysis, A/B testing, retention loops, AI cost monitoring. The first eight weeks post-launch are where most of the product learning happens; we stay engaged through that window by default.

A typical v1 lands in 12 to 20 weeks. We do not ship "MVPs" that skip Android or skip the backend — the bill comes due immediately and the rebuild costs more than doing it right.

How does AI-powered personalization actually work in a consumer app?

Personalization is the single most overused word in consumer AI. In production, it means one of three things:

Behavioral recommendations — Ranking content (feed posts, products, lessons) using a model trained on user interactions. Foundation: a content embedding model (OpenAI, Cohere, or open-source) plus a vector database (Pinecone, Turbopuffer, or pgvector) and a re-ranker.
Generative personalization — Producing user-specific content on demand (workout plans, study sessions, summaries). Foundation: an LLM call with the user's profile and recent activity in the prompt, with strict latency and cost budgets.
Predictive features — Anticipating the next action (autocomplete, smart defaults, churn-risk nudges). Foundation: a lightweight on-device model or a server-side classifier triggered on session events.

Most consumer apps need exactly one of these to feel intelligent. Apps that try all three at v1 ship none of them well.

When should you build native vs. cross-platform?

This decision is more consequential than founders usually realize. Here is the heuristic we use:

Build native if the app depends on camera pipelines, ARKit / ARCore, HealthKit, on-device ML with Core ML or LiteRT, or platform-specific UI conventions that users will notice. Also build native if the product needs to compete with the best apps in its category on either platform — a Things 3, a Streaks, a Halide.
Build React Native if the product is mostly content, lists, forms, chat, and standard mobile patterns, and you want to share business logic between iOS and Android. With Expo, the developer experience is the best in the cross-platform space, and you can drop down to native modules where you need to.
Build Flutter if your team already writes Dart or you have specific reasons (a strong custom design system rendered identically across platforms, or an existing Flutter codebase). For a JavaScript-fluent team, Flutter offers no real advantage over React Native.
Avoid web wrappers for any AI-first consumer product. Capacitor and similar wrappers can ship a v1 fast, but they hit a ceiling on perceived quality that AI features make worse, not better.

How do you keep AI inference costs under control?

AI inference is a unit-economic problem disguised as an engineering problem. We design every consumer app with a per-user cost model from day one:

Routing: Cheap models (GPT-4o-mini, Claude Haiku, Gemini Flash) for the 80% of requests that don't need a frontier model. Frontier models only on the requests that demonstrably need them.
Caching: Aggressive embedding cache, prompt cache, and response cache for repeated or near-repeated queries. Anthropic and OpenAI both offer prompt caching that we wire in by default.
On-device: Move latency-sensitive or high-volume features (transcription, image classification, light text generation) on-device with Core ML or LiteRT where quality permits.
Usage caps: Free-tier users get a quota. Paid users get a higher quota. Power users that hit the cap are a pricing signal, not a cost crisis.
Observability: Per-feature, per-user, per-day cost metrics in Datadog or PostHog from day one. You cannot fix what you cannot see.

This work is not glamorous, and it is the difference between a 70% gross margin and a 10% gross margin at scale.

Should you build, buy, or partner for your consumer app?

The build / buy / partner question is sharper for consumer apps than for internal software, because polish and platform fluency are the product. Here is how we frame it:

Option	Best for	Speed	Differentiation	Cost (3 yr TCO)	Lock-in
Buy a no-code / SaaS builder (Bubble, Glide, Adalo)	Internal tools, MVP for non-consumer audiences, simple form-driven apps	Days–weeks	None — competitors get the same shell	Low recurring, scales with seats	High — your product lives on the vendor's roadmap
Cross-platform (React Native + Expo, Flutter)	Most AI-first consumer apps that share logic across iOS and Android	12–20 weeks for v1	High — you own the code and the design	Moderate upfront, low recurring	Low
Native (Swift + Kotlin)	Camera, AR, on-device ML, premium-tier apps that must match best-in-class on both platforms	16–28 weeks for v1	Highest — full platform fluency	Highest upfront, low recurring	Lowest
Hire an agency on a fixed scope	Founders without a technical co-founder, single-version handoff	16–24 weeks	Variable — depends on the agency	High upfront	Moderate — you inherit whatever they wrote
Partner with a specialist (our model)	Teams that want a polished v1 plus a product partner through launch and the first growth experiments	12–20 weeks for v1	High — designed and built around your AI advantage	Predictable, post-launch retainer optional	Low — you own the code

The pattern we see most often: founders try Bubble or Glide for the v1, hit the platform's ceiling within six months, and then pay twice — once for the throwaway, once for the real build. If the product is consumer-facing and AI-first, skip the no-code stage.

What does a consumer app engagement look like with us?

Most engagements start with a two-week scoping sprint — a fixed-fee, fixed-scope piece that produces a product spec, an AI feasibility report, and a build estimate. Founders who hire us after the scoping sprint do so with a clear-eyed view of what they're getting; founders who don't continue keep the deliverables and a working prototype.

The full v1 build runs 12 to 20 weeks for two platforms with a real backend, observability, and one or two AI features that have been benchmarked on representative data. We default to React Native with Expo for cross-platform builds, Swift and Kotlin for native, and a TypeScript backend on Vercel, Render, or Fly.io. AI workloads sit on OpenAI, Anthropic, or a self-hosted open-source model behind a routing layer.

We stay engaged through the first 8 weeks post-launch by default — that's when the real product learning happens, and where instrumentation, AI cost tuning, and the first retention experiments live. After that, clients either move to a small monthly retainer or take the codebase fully in-house with a documented hand-off.

What does a consumer app cost?

A v1 consumer app with two platforms, one or two AI features, a real backend, and store-ready polish typically runs USD 80,000 to USD 250,000. The range is driven by:

Platforms (one vs. two; native vs. cross-platform)
Number and complexity of AI features (a single recommendation feed vs. a full conversational assistant)
Backend depth (a thin API vs. multi-tenant infrastructure with payments and content moderation)
Design ambition (a clean utilitarian UI vs. a category-defining design language)

Sub-USD 50,000 builds exist, but they are almost always single-platform, single-feature, and skip the backend or the AI evaluation work. We don't take those engagements because we end up rebuilding them ourselves a year later.

For pricing on retainers and post-launch growth work, see our Pricing page.

Frequently asked questions about consumer apps

Should we build native, React Native, or Flutter for our consumer app?

Native (Swift / Kotlin) when the product depends on platform features — camera pipelines, on-device ML, ARKit, HealthKit — or when it has to compete with the best apps on the platform. React Native is the right default for most AI-first consumer apps that share business logic across iOS and Android. Flutter is excellent if your team already knows Dart, but offers no advantage over React Native for a JavaScript-fluent team.

How long does it take to ship a v1 consumer app?

Twelve to twenty weeks for a focused v1 with a real backend, two platforms, and one or two AI features. Anything faster usually skips the AI evaluation work or treats Android as an afterthought.

Where should the AI run — on-device or in the cloud?

On-device for latency-critical, privacy-sensitive, or offline features (image classification, transcription, light personalization) using Core ML or TensorFlow Lite. Cloud for anything that needs a frontier model or aggregated data. Most production apps use both.

How do you handle App Store and Play Store AI policy reviews?

We design the review submission around Apple's and Google's actual AI guidelines: no surprise data collection, clear consent for content generation, content moderation on user-generated AI outputs, and accurate age ratings. Most rejections we've seen are about consent UX or moderation, not the model itself.

Will users actually pay for AI features in a consumer app?

Only when the AI does something the user couldn't easily do themselves and the value is felt within the first session. Generative chat alone rarely converts; AI that removes a real chore (photo cleanup, scheduling, document parsing, personalized recommendations) converts well.

Can you handle the App Store launch and growth, or just the build?

We handle launch — store listing, screenshots, metadata, ASO, staged rollout, crash and analytics instrumentation. For sustained growth marketing we'll partner with your team or a specialist; we're builders, not a paid-acquisition agency.

What happens to AI cost as the app scales?

It is the single biggest variable. We model per-user inference cost at three usage tiers, design caching and routing to cheap models where quality allows, and put usage caps on free tiers. Apps that skip this step ship a great product and then watch margin disappear at 100K users.