CUSTOM MOBILE
APP DEVELOPMENT SERVICES
Most mobile products don't fail on the code. They fail on the decisions made before it: what should be intelligent, where the model runs, and what a feature will cost on every launch a year from now. Our team has been living with those calls in production since well before anyone had a name for them.
We build mobile products where AI is part of the architecture rather than a layer added after launch. That covers on-device and cloud inference, in-app assistants, computer vision, agentic workflows, and the plain deterministic engineering that still carries most of a good app. We ship on native Android, native iOS, and React Native, with one team from discovery through store deployment and everything that comes after.
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When you need to hire a mobile development partner
You've got an AI capability that works, whether that's a model, an assistant, or a pipeline, and no clear idea what it should look like in a user's hand, offline, at scale.
Someone has asked for "AI in the app" and nobody in the room can say which parts should be a model, which should be rules, and what either one costs per user per month.
Your app already has AI features that demo beautifully and disappoint in production, because they're slow, wrong often enough to notice, or expensive in ways the business case never predicted.
You're extending an existing web product to mobile and want a team that can match what already works, while unlocking the unique features of the mobile world.
Field teams in delivery, service, or facilities need a tool built for the job they actually do, and increasingly that means one that reads, listens, and suggests instead of waiting to be filled in.
Your current app has fallen behind on architecture, performance, or a UI that stopped keeping pace with the web product. Patching it isn't working anymore.
You're planning enterprise mobile app development to replace paper and spreadsheets with something controlled, integrated, and able to take instructions in plain language.
Mobile app development services we provide
We scope every build against the constraints that typically break mobile products: offline mode, device connections, geolocation, battery, bundle size, and handling data that regulators care about. AI features answer to all of those, plus two of their own. A model is right most of the time rather than always, and its cost scales with use instead of with headcount.
AI product and engineering
AI product discovery and feasibility
The first thing we usually do together is decide what deserves a model at all. Predictive search, automated support, document processing, recommendations, forecasting, monitoring automation: each one is worth building when the intelligence has a defined job and a measurable outcome. Plenty of features pitched as AI work better as rules. They're cheaper to run, easier to explain to a user, and they don't need an eval suite. You come out of this phase with a shortlist, a feasibility read, and a cost-per-user estimate. Our business analysis services cover the call.
On-device AI
Where inference happens is the first architecture decision on an AI mobile product, and it belongs per feature rather than per app. Running on-device keeps data off the network, works when connectivity doesn't, and costs nothing per call. You pay for it in model size in your bundle and battery on the user's phone. The platforms have moved sharply in this direction, and what a phone can run locally today isn't what it could run two years ago.
LLM and assistant features
In-app assistants, natural-language search, and summarization over a user's own content, with retrieval grounded in your data rather than the model's memory. The engineering that decides whether any of this works is deeply unglamorous: chunking and retrieval quality, prompt and context management, streaming UI that stays responsive on a bad connection, and a fallback for every path where the model returns nothing usable.
Agentic features and workflow automation
The step past a chat box. These are features that take an instruction and carry out a multi-step task against your own systems: book, reschedule, file, reconcile, escalate. On mobile it comes down mostly to permissions and reversibility. What can the agent do without asking, what must it confirm, what can it undo, and what does the user see while it works? We build these with an explicit action boundary instead of an open-ended tool list.
Computer vision and image processing
Reekolect came to us with an AI photo feature that a previous vendor had already attempted and left broken. Our data science team reconfigured the pre-trained computer vision models and shipped working restoration, colorization, and resolution improvement inside the original 9-month timeline. Inheriting a failed AI integration is a different job from starting a clean one, and we've done it.
AI in regulated mobile products
Once an AI feature touches health or payment data, what leaves the device becomes an architecture question and a compliance question at the same time. We've built a SOC 2-aligned AI document platform with encrypted storage, time-limited access URLs, role-based permissions, and full audit trails. We've also architected AI/ML integration into clinical trial management software running across hundreds of investigative sites.
Mobile platform engineering
Android app development
Native Android app development services in Java and Kotlin. GPS-based delivery tracking, enterprise product configurators, and consumer apps have all shipped on this platform for us, and it's where on-device inference and background processing give you the most room to work.
iOS app development
Native iOS app development services built to the platform's own conventions and performance expectations. Users notice the difference within about ten seconds of opening an app, usually without being able to say why. The same goes for an AI feature that respects the platform's AI conventions instead of importing a web chat interface wholesale.
Cross-platform and hybrid app development
Worth knowing up front: heavy on-device inference is the one workload where the cross-platform case gets weaker, and we'll say so when your feature list crosses that line.
Enterprise application integration
A mobile app earns its keep when it talks to the systems behind the business. Our application integration services connect apps to CRM, ERP, payment platforms, analytics tools, and cloud services, so data moves once and reliably, and so an assistant or agent inside the app has something real to act on.
Legacy app modernization
When a mobile product has fallen behind its web counterpart or its own platform's standards, we rebuild the architecture and interface around whatever still holds up. The technical debt is usually concentrated in two or three places, and finding them costs far less than starting the product over. These days the trigger often isn't age at all. It's that the existing architecture has nowhere clean to put an AI feature.
MVP and POC development
Validate the concept before you commit the budget: test features, gather feedback, refine the idea. For AI-led products this usually means two artifacts rather than one, a thin product prototype plus a separate feasibility spike that answers whether the model is good enough at the actual job before anyone designs UI around it. We use AI-assisted development and low-code/no-code platforms where they genuinely fit.
Mobile app development consulting
Our mobile app development consulting services settle the decisions before code gets written: platform path, core architecture, the build-or-integrate call on every AI capability, and which initiatives are worth funding first. You leave with a roadmap you can execute against.
Not sure what service fits your needs?
Mobile AI engineering, and what changes on a phone
AI on mobile is a different engineering problem from AI on the web. The device has a battery, a bundle size limit, a store review process, and a network connection that comes and goes. All four constrain what a model can do, so our AI integration services for mobile start from those constraints rather than from the model. Here are the six things our team decides on every AI mobile product.
How we build, and where AI sits in our own process
Clients ask about this now, and they're right to. Using AI in the engineering process changes velocity, cost, and risk, and a vendor who's vague about it is telling you something.
Where our team uses it:
Scaffolding and boilerplate, test generation and coverage gaps, migration and refactoring passes across large legacy codebases, documentation of undocumented systems, and first-pass code review.
On app modernization work, where the slowest part has always been reading a codebase nobody has documented in eight years, it's changed the shape of the job more than anywhere else.
Where we don't:
Architecture decisions, security-critical code paths, and anything that ships without a named engineer having read it. Generated code carries the same review bar as written code, and the reviews are done by people who could have written it themselves.
What it means for you:
Faster discovery and modernization phases, plus better test coverage on the same budget. What it doesn't mean is a cheaper hour billed for a worse result. The judgment about what to build and whether it works is the part nobody has automated, and it's the part you're hiring.
Mobile development services beyond the app screen
Assistant and OS surfaces
Widgets, shortcuts, voice entry points, and the system assistant surfaces that let someone reach your product's functions without opening it.
As phone assistants gain the ability to act inside third-party apps, an app that has exposed its actions cleanly gets reached. One that hasn't stays behind its icon.
Wearables and connected devices
Companion apps for wearables and connected hardware, including the Bluetooth device integration, background sync, and battery behavior that decide whether people keep them installed. This is long-standing ground for our company. The Qardio ecosystem has connected mobile apps to blood pressure monitors, pulse oximeters, and wearable sensors since 2012.
Voice, camera, and sensor input
The input methods that make an app usable with one hand, in a warehouse, in a car, or in a treatment room: dictation and transcription, camera capture with on-device recognition, and sensor-driven automation. For field teams, this is usually where the real efficiency gain sits, well ahead of anything in the UI.
Mobile app development process with Aimprosoft
We settle requirements, strategy, and initial visual direction before anyone makes an architecture decision. On AI-led products this includes the honest first pass at which capabilities need a model and which need a rule.
We lock the specification, scope, and estimate. If your app isn't scoped yet, our business analysis services cover that phase as standalone work.
Runs in parallel with business analysis, and only where the product has AI features. Before anyone designs UI around a model, we establish that the model can do the job: a thin evaluation against your real data, a decision on on-device versus cloud, and a cost-per-user estimate. This is the cheapest week in the project to find out that a feature doesn't work, and the answer is sometimes "build it as rules instead."
This is where mobile app development consulting earns its fee. Stack and core architecture get chosen against your requirements, including the native vs. cross-platform call that everything downstream depends on, and, where AI is in scope, the inference topology that constrains it.
Market-informed interface design happens ahead of development, while changing it is still cheap. We design AI features with their failure states rather than just their happy path: what the user sees while waiting, when the model is unsure, and when it's wrong. See our UI/UX design services.
Built in stages, with project management and QA running the whole way through. AI features carry their eval suite alongside the test suite.
We handle store deployment and everything the review process demands, including the AI disclosures and data-use declarations both stores now require.
Maintenance and feature expansion continue after release, and we keep watching the AI features: output quality against the release thresholds, cost per user, and the ability to reconfigure or disable a feature remotely.
Mobile app development success stories across industries
Industries we work with
Every industry runs into different mobile problems, and now a different version of the same AI question: what can be automated, what must stay human, and what a regulator will ask about later. We've built for 27+ business sectors at this point, so compliance requirements, operational workflows, and integration quirks rarely catch our team off guard.
Patient- and provider-facing health apps with real-time device integrations, including wearable monitoring and remote care platforms serving millions of users. It's the sector where "should this run on the device" gets answered by the compliance team as often as by the architect.
Mobile commerce work, both new builds and modernization: loyalty programs, checkout flows, and store-finder tools aimed at repeat purchase rather than first-time browsing. Recommendation and visual search are where the incremental revenue sits.
Booking and lead-generation tools. For one long-term partner alone that covers 500+ cities and 120,000+ listings, which is the kind of catalog where natural-language search beats a filter panel.
Exam-prep, accreditation, and student-management platforms that hold up through peak exam season, serving schools, teachers, and students at once. Also the sector with the sharpest questions about what AI should be allowed to do for a student.
Tools built for delivery, service, and facilities teams: offline-first, GPS-driven, and increasingly voice- and camera-led rather than form-led.
Don't see your niche listed? Let's talk. Chances are we've already solved something close to it.
Why teams choose Aimprosoft for custom mobile app development
Your mobile app is a strategically important asset, so the claims below are the ones we can put numbers behind.
Proven at scale, not just in sandbox
Our team has built and scaled mobile platforms carrying real production loads across healthcare, fintech, and logistics. The health monitoring ecosystem we've maintained for 12 years tracks billions of data points for over 3 million active users. A rewards and monetization platform we scaled now handles 4,000–5,000 concurrent users, up from 1,000–1,500, with monthly revenue growing from roughly $10K to $140–150K over the same period.
AI that survives contact with production
The hard part of an AI feature isn't building one. It's the second month. Reekolect's AI photo restoration had already been attempted by another vendor and left unusable when it reached us, and our data science team reconfigured the models and shipped it working inside the original timeline. That's the job as it arrives: a model that must work on real user inputs, on a phone, inside a fixed date. So we scope for what a feature costs per user, what it does with no signal, and how you turn it off if it goes wrong.
Speed that survives the release
Our Agile, sprint-based delivery model gets products to market on schedule. Splitting validation logic into a dedicated microservice cut partner onboarding for a B2B certification platform by 10x, from weeks to days. A monolith-to-microservices migration cut time-to-market for new products roughly 4x, from a 12-week average down to 3 weeks.
Compliant by design, including AI
We put security and compliance into the architecture at the start. Our clinical trial platform was designed around 21 CFR Part 11, GDPR, and HIPAA from day one, with an OWASP vulnerability checker built into the CI/CD pipeline to catch library risks before every release.
For an AI-powered document platform, we built a SOC 2-aligned architecture with encrypted storage, time-limited access URLs, role-based permissions, and full audit trails. The health monitoring platform holds 80% unit test coverage while processing patient data from connected medical devices around the clock. The same discipline now applies to AI governance: what data trains or prompts a model, what leaves the device, what's logged, and what a user can contest.
Engineering aimed at the business result
The health monitoring ecosystem holds a 97% positive rating across 3 million users. The certification platform's automation cut manual errors by over 80% and improved scalability 5x.
The online rewards platform overhaul drove a 14–15x increase in monthly revenue through better infrastructure, fraud prevention, and continuous feature delivery. For a European pet-supply retailer, we specified loyalty and order-history features around repeat purchase and retention from the outset.
We'll tell you when the answer is no
Not every feature that gets pitched as AI should be one. Rules are cheaper to run, easier to explain, and they don't drift. Honestly, the most useful thing a technology partner can do in the first two weeks is take three of your six AI ideas off the list and build the other three properly. Sometimes the AI assessment says rewrite anyway, or build it without a model at all, and we'll tell you that before you've spent the budget finding out.
What our clients say
Aimprosoft led regular meetings to manage expectations and ensure a transparent workflow. The organized team always took the time to explain everything to us and delivered work on time. They were flexible, receptive to feedback, supportive, and creative. They formed a whole with us and ensured that everything was delivered on time and what was needed was immediately adjusted or added."
Ready to build your app?
Book a free discovery call and talk through your platform choice, scope, and, if AI is on the roadmap, which parts of it really need a model. You'll be talking to our mobile engineering team, before you commit budget.
If you haven't locked down requirements yet, start with our business analysis services. Once your app needs backend or API support, see our backend development services, and our QA and software testing services page covers the pre-launch quality step. For AI work that reaches beyond the app itself, see our AI development services.
FAQ about custom mobile app development services
Cost depends heavily on what you’re building. A straightforward customer-facing app costs far less than a platform handling real-time IoT device data, biometric health monitoring, or deep integration with systems like Salesforce.
What moves the number, based on our own projects:
- Native iOS and Android builds against one cross-platform codebase in React Native, which we’ve used across several projects
- Basic encryption against full compliance-grade security. We’ve built OWASP-integrated CI/CD pipelines and SOC 2-aligned architectures with encrypted storage and audit trails
- Simple API connections against deep integration with enterprise systems like Salesforce, or legacy platforms that need modernization first
- Regulatory requirements such as HIPAA, GDPR, or 21 CFR Part 11. We’ve architected clinical trial and healthcare platforms around these from day one, and the documentation and audit overhead is real
- AI features, where the cost splits in two: the build, and the running cost per user that continues for the life of the product. A feature using a cloud model is an operating expense rather than a one-time line item, and it should be estimated as one
- Infrastructure, meaning cloud-based multi-region setups on AWS with CDN delivery to support scale
- Ongoing maintenance and evolution. Several of our client platforms have been in active development for 7–12+ years
Ranges we’ve seen in practice: a focused MVP, like the AI-powered social platform we delivered in 9 months, lands in the lower band. Enterprise-grade platforms, particularly with healthcare compliance, IoT integrations, or legacy system connections, typically run from $50,000 to $250,000+. AI, IoT, and blockchain features push that higher.
That’s separate from the build, and it’s the number most projects never estimate. A cloud-inference feature bills per request, so its cost scales with engagement, which means the more successful the feature, the larger the invoice. On-device inference costs nothing per call but spends bundle size and battery instead. In practice we design an escalation path: cache the repeatable answers, run a small local model for the common case, and reach for a large cloud model only where the job needs it. We put a cost-per-active-user figure in the estimate before the build starts, and we instrument the feature so you can watch it after launch.
Yes, and it’s usually cheaper than the rebuild people expect. We assess where the model should run, what data would have to leave the phone, what the feature costs per user, and how you’d measure whether the output is good enough to keep. We’ve also picked up AI work that went wrong elsewhere. A photo restoration component for a social media app had been attempted by a previous vendor and left unusable, and our data science team reconfigured the models and shipped it working.
It depends on the feature, and it’s worth deciding per feature rather than once for the whole app. On-device inference keeps user data off the network, works offline, and adds nothing to your running cost, but the model must fit in the app bundle and run on the battery. Cloud inference gives you a bigger model and easier updates, at the cost of latency, per-request billing, and a feature that stops working when signal does. Anything touching health or payment data usually pushes toward on-device, for reasons that are as much about privacy and compliance as engineering.
It will, and the product has to be designed for that rather than surprised by it. Before the build we set an accuracy threshold and the cases it’s measured against, so “good enough to ship” is a number rather than an opinion. In the interface, the user always has a way to see what the model did, disagree with it, and carry on, and no AI output is ever the only path to a task. In the architecture, every AI feature can be reconfigured or switched off remotely without waiting on app stores review. After launch we watch output quality, because the model doesn’t drift but the inputs do.
Yes, if the client’s corporate policy allows, and in defined places: scaffolding, tests, migration passes, and documentation of undocumented legacy systems. Not in others, including architecture decisions and security-critical paths. Everything ships through the same review bar as hand-written code, read by an engineer who could have written it themselves. The benefit shows up as faster discovery and modernization and better test coverage, never as a lower standard.
Native, meaning Swift for iOS and Kotlin or Java for Android, gives you the best performance and the tightest platform fit. It’s worth the cost where device integration, performance, or platform-specific UX carries the product. Cross-platform through React Native development services gets you to market faster and costs less to maintain over time, as long as a shared codebase doesn’t compromise what the app must do. One new factor in the native vs. cross-platform call: heavy on-device inference and tight integration with platform AI frameworks are where the cross-platform case gets weakest, so a feature list with real local model work leans native. We build both, and we’ll make the call against your requirements rather than a house preference.
Timeline tracks complexity. Across our own projects, the pattern holds:
- 3–6 months: basic login, data entry, push notifications, single platform.
- 6–9 months: payment processing, real-time features, system integrations, analytics. This is the band we hit delivering a full AI-powered social platform, web and mobile with custom photo restoration, in 9 months.
- 9–12 months: advanced security, offline mode, AI features, admin dashboards, role controls.
- 12+ months: multi-country deployment, compliance audits, IoT integration, fraud detection. Our clinical platform, architected around HIPAA, GDPR, and 21 CFR Part 11 from day one, and the 12-year health monitoring ecosystem both sit here, with long builds followed by continuous evolution.
What slows things down, from experience: regulatory approval cycles in healthcare and clinical trials, security audits baked into every release, and legacy systems with little or no documentation. We’ve rebuilt several of those, including outdated Liferay portals and monolithic architectures.
Yes. Legacy app modernization is one of our core engineering services. We assess what’s salvageable in the existing architecture and rebuild around it where we can, which keeps both cost and risk below a full rewrite. Sometimes the assessment says rewrite anyway, and we’ll tell you that before you’ve spent the budget finding out.