We build iOS, Android, and cross-platform apps that combine mobile product engineering with AI system design. As a result, our Clients’ mobile apps support copilots, LLM-based workflows, and on-device intelligence.
We build mobile apps that meet your business goals with precision and care:
We define a clear scope, prioritize features that drive value, and build a tailored roadmap, exploring advanced technologies like AI or IoT to match your vision. You’ll get a transparent plan that keeps your project on track and ready to compete.
We focus on accessibility, smooth navigation, and performance, testing designs with real users to perfect the experience. Whether it’s a sleek e-commerce app or a dynamic fitness platform, your app will feel effortless to use, keeping users engaged and loyal.
Our process adapts to your evolving needs, delivering regular updates for transparency. With expertise in modern frameworks, we’ve powered apps handling over 100,000 active users without a hitch. Your app will scale seamlessly as your business grows.
We seamlessly connect your app to AI tools, payment gateways, social media, cloud storage, analytics, or custom enterprise systems. Our integrations boost features and streamline operations without slowing performance.
We simulate real-world conditions to catch issues early, ensuring a flawless experience. A recent retail app we tested achieved 99.9% uptime post-launch. Your app will deliver consistent, reliable performance that keeps users coming back.
We manage App Store and Google Play submissions, navigating strict guidelines for a smooth rollout. Post-launch, our team provides ongoing updates, monitors performance, and resolves issues quickly.
We handle both layers. Our software development lifecycle covers the mobile foundation: app architecture, platform fit, release stability, and interface performance. Our agentic development lifecycle covers the intelligence layer: model selection, prompt and tool design, retrieval flows, guardrails, evaluation, and post-launch monitoring.
We turn ideas into high-performance iOS, Android, and cross-platform apps users love.
We established comprehensive guidelines during the past years, combining the best industry practices and our internal guidelines to deliver high-quality, user-focused mobile applications.
We immediately consider where the model will run, how the app will behave on the device, and how AI features will impact speed, privacy, and UX.
We bridge standard mobile development for the app’s stability and ADLC for the quality of model responses, security, evaluations, monitoring, and updates to the AI logic.
We use AI tools where they speed up implementation, but all code, tests, and architectural decisions are reviewed by the team.
We design AI features with battery life, memory, unstable network conditions, background limitations on iOS and Android, and App Store and Google Play requirements in mind.
We test the model’s compliance with product rules, its ability to work with the required data, and its handling of user flow.
After the release, we update the app and the AI layer separately to improve models, prompts, retrieval, and response logic.
The choice depends on where the application requires speed, where privacy is important, and how much AI logic should be processed in the mobile product.
| Criteria | On-device AI | Cloud AI |
|---|---|---|
Latency |
Minimal latency for local tasks. The application doesn’t wait for network requests. |
Dependent on connection quality and the response time of the external AI service. |
Privacy |
Suitable for scenarios where data is best processed on-device without transferring it to the cloud. |
Requires server-side protection: isolation, redaction of sensitive data, storage rules, and access control. |
Capabilities |
Better suited for classification, scanning, text extraction, short summaries, and other local tasks. |
Better suited for complex reasoning, large retrieval indexes, long context, and heavier generation. |
Offline operation |
Can work offline if the model and pipeline are hosted on the device. |
Typically limited or unavailable without a network connection. |
AI logic updates |
Requires consideration of the device and model size, and whether updates are delivered via a mobile release or a model package. |
The model, prompts, and retrieval logic are easier to update centrally on the server side. |
Latency
Privacy
Capabilities
Offline operation
AI logic updates
Minimal latency for local tasks. The application doesn’t wait for network requests.
Suitable for scenarios where data is best processed on-device without transferring it to the cloud.
Better suited for classification, scanning, text extraction, short summaries, and other local tasks.
Can work offline if the model and pipeline are hosted on the device.
Requires consideration of the device and model size, and whether updates are delivered via a mobile release or a model package.
Dependent on connection quality and the response time of the external AI service.
Requires server-side protection: isolation, redaction of sensitive data, storage rules, and access control.
Better suited for complex reasoning, large retrieval indexes, long context, and heavier generation.
Typically limited or unavailable without a network connection.
The model, prompts, and retrieval logic are easier to update centrally on the server side.
We established comprehensive guidelines during the past years, combining the best industry practices and our internal guidelines to deliver high-quality, user-focused mobile applications.
We design mobile AI solutions to meet platform requirements for moderation, user data processing, access controls, and app behavior in sensitive use cases.
If an app requires a camera, microphone, geolocation, or photos, we build clear permission flows so the user can see in advance why access is needed and what exactly will happen after consent.
If you already have an app written in Swift, Kotlin, or React Native, we can integrate AI functions without a complete rewrite, clean up vulnerable code, and integrate a new AI layer via an API, SDK, and server-side orchestration.
Our work doesn’t end after launch: we monitor response quality, update rules, test new model versions, and modify AI logic separately from the mobile client to ensure the product remains manageable and predictable.
We build these capabilities into the product architecture, with the mobile client, backend services, and AI layer working as one system.
We integrate GPS, mapping, geofencing, and route logic for location-aware behavior. In AI products, location can also shape recommendations, field workflows, task routing, and context-sensitive assistance.
Push systems work better when they respond to product events, user state, and timing rules. We build notification flows tied to backend logic, segmentation, and mobile behavior, with AI used to improve message relevance, prioritization, and response handling.
We integrate payment gateways, subscriptions, and transactional flows that match the product’s security and compliance requirements. When needed, AI can support purchase assistance, payment support flows, and anomaly detection around transaction behavior, while the payment logic itself stays deterministic and tightly controlled.
We build voice interfaces that let users navigate the app, query data, retrieve answers, and complete tasks through natural language. Depending on the use case, the stack can combine speech recognition, LLM-based reasoning, tool calling, and latency controls designed for mobile use.
We build in-app copilots that connect to your app’s knowledge sources, business logic, and system actions via retrieval, governed APIs, and permission-aware access. These copilots can answer product questions, support users, resolve ticket flows, and perform multi-step actions within the app.
We use on-device AI when a product needs offline support and faster response times, including quantized small language models, Core ML pipelines, ML Kit features, and hybrid on-device or cloud execution.
We cover the full cycle – from wireframes and code to launch and long-term support.
We build mobile apps with AI features for industries where the product must meet specific requirements for data handling, system access, performance, and offline behavior. The architecture depends on the use case.
Educational apps must maintain the course flow, help maintain the pace, and provide support when needed. We create mobile EdTech products where AI enhances the learning system: they can explain a complex passage, analyze an answer, help with practice, or guide through the material, all within a predetermined structure.
We develop eCommerce apps to shorten the path from inquiry to order. AI can take on some of this work: understanding more complex wording, clarifying intent, selecting relevant products, and assisting with selection. For the user, this means a shorter and more understandable purchasing process. For businesses, this means more accurate catalog management, inventory, pricing rules, and service processes within a single app.
We build fintech mobile apps for banking, payments, investing, and personal finance. AI features can include voice input, grounded account queries, spending summaries, and guided actions tied to user permissions and transaction data. This helps users get answers faster and complete routine tasks inside the app while the product stays within strict security and compliance limits.
We build healthcare mobile apps for telemedicine, patient portals, clinical workflows, and remote monitoring. When sensitive data should stay off the cloud, we can run selected AI features on the device so patient inputs are processed locally. When cloud AI is needed, we route it through controlled services with role-based access, logging, and review points. This helps reduce data exposure and provides users with faster support within the app.
In logistics, a mobile app must withstand connection failures, high load, and constant context changes. We design such apps so that key actions are immediately available on the device, and synchronization occurs without unnecessary user interaction. AI is useful here not as an operational layer: it helps recalculate routes, prompt the next step, and reduce manual decisions in operational work.
We build mobile tools for campaign monitoring, customer engagement, analytics, reporting, and assisted content workflows. AI can help teams analyze signals, surface insights, and support routine actions inside the app, with access tied to approved data sources and business rules. This helps marketing teams work faster while keeping output tied to governed inputs.
Get strategic insights and technical guidance from developers with real-world success.
We use advanced technologies in mobile products when they improve response time, device-side processing, system access, or connected workflows. The point is to support a product requirement that standard mobile patterns do not cover.
We connect mobile apps to sensors, gateways, machines, wearables, and smart devices so users can monitor status, receive alerts, send commands, and review device data from one interface. This is useful in smart home products, industrial systems, healthcare devices, and logistics operations where the phone acts as the control point between the user and the connected environment.
We build AR and VR features for guided training, product visualization, remote assistance, and immersive learning. On mobile, that can mean overlaying instructions on a camera view, placing products into a physical space before purchase, or using 3D environments for simulation and onboarding. This is a strong fit for retail, field service, healthcare, and training apps.
We design mobile flows that take advantage of 5G when low latency and higher throughput affect the product experience. That includes live video, remote inspection, media-heavy collaboration, and field workflows that depend on fast sync. The benefit is speed and better support for time-sensitive features when the network can provide it.
We use edge computing when selected workloads should run closer to the user or directly on the device, rather than waiting for repeated cloud round-trips. In mobile apps, this can support faster response times, lower network dependence, and tighter control over sensitive data. It’s often a good fit for offline support, camera-based processing, inspection tools, and device-assisted AI features.
We integrate face and fingerprint authentication to help users sign in faster and confirm sensitive actions, while the app still relies on secure storage and server-side controls.
We connect mobile apps with Apple Watch, Wear OS devices, health monitors, fitness trackers, and other companion hardware when users need glanceable data, short interactions, background sync, or quick actions away from the phone.
We build both layers of an AI-powered mobile product: the app itself and the intelligence behind it. Our teams handle mobile architecture, backend integration, and release quality, then apply ADLC to model selection, retrieval flows, evaluation, guardrails, and post-release monitoring.
Since 2012, we bring years of delivery experience across startups and enterprise projects. Teams are staffed with senior engineers who can work through complex product requirements, legacy constraints, and platform-specific issues without slowing the roadmap.
Our developers use AI coding tools that help speed up implementation, refactoring, and test coverage. The output still goes through code review, QA, security checks, and release validation before it reaches production.
We work under ISO 27001 and ISO 9001 standards and build mobile products with structured QA, controlled delivery, and secure engineering practices. For regulated products, we also account for requirements such as HIPAA and role-based access controls.
We choose native, cross-platform, on-device AI, or cloud AI based on product scope, operating cost, compliance needs, and long-term maintenance.
We support mobile apps after release with updates, monitoring, issue resolution, model iteration, and ongoing improvements to both the product and AI layers.
XtremeProgrammers has been recognized by the leading analytics agencies from all over the world. We deliver value, not just software.
Receive a clear breakdown of cost, timeline, and tech stack tailored to your app concept.
We reduce unnecessary model calls, compress payloads, cache results, and move selected tasks on device when it makes sense.
Yes. We route RAG through a secured backend, not directly from the phone, and control access through your existing auth and data rules.
We design for unstable networks with streaming responses, fallback states, caching, and request control. The goal is to keep the app responsive while the model works in the background.
We do not store LLM keys in the mobile client. Model calls go through a backend layer where access, limits, and request filtering are controlled.
We account for store requirements, including content moderation, data handling, permission logic, and sensitive use cases.
If you have any questions, email us
Transform Ideas into Mobile Applications
