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Digital & Intelligence Engineering · Intelligence in the hand

AI-Native Mobile Engineering

Mobile experiences designed around intelligence, context, and the constraints of the device.

AI-native mobile product connecting on-device intelligence, sensors, secure cloud services, and user context
Intelligence in the hand

The work

Engineering that holds up beyond the demo.

We create mobile products where AI is part of the interaction model, not a bolted-on feature. Cloud services, on-device intelligence, and the mobile experience are engineered together.

What we help you achieve

  • More useful, contextual mobile interactions
  • Responsive experiences across connectivity conditions
  • Private and low-latency on-device intelligence
  • A consistent path across iOS and Android
  • Telemetry and evaluation that improve the product over time

Detailed capabilities

The work behind a dependable platform.

Explore the engineering areas that take this capability from a first technical decision to a stable, production-ready system.

01

AI-Native Mobile Product Design

Mobile intelligence should make the interaction more useful, not add a model call to an existing screen.

  • Map user intent, context, gesture, voice, camera, sensor, and notification moments where AI can reduce friction
  • Design proactive, conversational, visual, personalized, and assistive flows with clear user control
  • Define when the experience uses local inference, cloud services, deterministic logic, or human escalation
  • Set quality, latency, battery, privacy, accessibility, offline, and adoption targets before implementation
02

On-Device & Hybrid Intelligence

The intelligent mobile edge balances immediacy and privacy against device compute, memory, battery, and connectivity.

  • Deploy vision, speech, language, recommendation, anomaly, biometric, or intent models on supported device hardware
  • Use quantization, pruning, distillation, batching, caching, model compilation, and hardware acceleration
  • Route work between device, gateway, and cloud based on latency, privacy, model size, network, cost, and battery
  • Support offline inference, synchronization, conflict handling, deferred actions, and graceful degradation
03

Mobile AI Experiences

AI features are shaped around the strengths of the phone: camera, microphone, sensors, location, and personal context.

  • Build voice and language interfaces, image understanding, visual search, OCR, AR or camera workflows, and smart capture
  • Create personalization, recommendations, intent prediction, anomaly detection, and adaptive journeys
  • Design streaming, progressive results, confidence, citations, correction, feedback, and safe refusal states
  • Integrate Swift, iOS, Android, Kotlin, Flutter, React Native, and shared service patterns as appropriate
04

Cloud Backend & Enterprise Integration

The app experience depends on secure services for knowledge, identity, synchronization, and ongoing model improvement.

  • Build mobile APIs, identity, profiles, preferences, content, vector search, model gateways, notifications, and sync
  • Connect to enterprise systems for retail, health, finance, logistics, field service, and customer operations
  • Apply tenant boundaries, consent, encryption, PII handling, access control, audit, retention, and data minimization
  • Support prompt and model versioning, feedback, feature flags, remote configuration, and staged releases
05

Evaluation, Performance & Lifecycle

Mobile AI must work across devices, environments, user behaviors, and network conditions—not only on a developer phone.

  • Evaluate task success, accuracy, groundedness, latency, battery, memory, thermal behavior, accessibility, and crash impact
  • Test device classes, OS versions, permissions, cameras, languages, connectivity, offline transitions, and adversarial inputs
  • Monitor model quality, user feedback, retention, inference cost, app performance, privacy events, and drift
  • Use staged rollout, remote kill switches, model updates, rollback, incident response, and clear user communication

Technical scope

Engineering detail from reset to runtime.

The work is designed around observable system behavior: what executes first, how resources are controlled, how failures are recovered, and how the platform proves it is ready.

01

Mobile AI experience

  • Voice, language, vision, OCR, visual search, camera, AR, sensors, personalization, recommendation, intent, and anomaly flows
  • Conversational, proactive, assistive, progressive, streaming, confidence, citation, correction, feedback, and refusal states
  • Swift, iOS, Android, Kotlin, Flutter, React Native, native bridges, accessibility, permissions, and release constraints
02

Edge & hybrid inference

  • On-device model packaging, quantization, pruning, distillation, compilation, hardware acceleration, memory, battery, and thermal budgets
  • Device, gateway, and cloud routing based on latency, privacy, model size, connectivity, cost, and availability
  • Offline inference, local storage, synchronization, conflicts, deferred actions, caching, fallback, and graceful degradation
03

Cloud & enterprise services

  • Mobile APIs, identity, profiles, preferences, content, vector search, model gateways, notifications, and synchronization
  • Enterprise integrations for health, retail, finance, logistics, field service, and customer operations
  • Consent, tenant isolation, encryption, PII handling, access, audit, retention, data minimization, prompt/model versioning, and flags
04

Quality & lifecycle

  • Task success, accuracy, groundedness, latency, battery, memory, thermal, crash, accessibility, and device/OS compatibility
  • Golden tasks, device matrices, permissions, languages, network transitions, adversarial inputs, staged rollout, and regression
  • Quality, feedback, retention, cost, privacy, drift, remote configuration, kill switches, rollback, and incident operations

Services and customer value

Technical work with a business reason.

Every engineering activity is tied to a decision your team needs to make: bring up faster, reduce risk, improve determinism, or prove production readiness.

01

AI-native mobile product strategy

Technical work

Find high-value mobile moments for voice, vision, intent, personalization, and assistance, then define local/cloud boundaries, UX states, targets, privacy, and rollout.

Customer benefit

The app gains intelligence where it improves the user’s context and task, rather than adding AI as a novelty feature.

02

On-device and hybrid inference

Technical work

Package and optimize models for mobile hardware, then route work between device, gateway, and cloud with offline behavior, caching, synchronization, and fallback.

Customer benefit

Users get responsive, private experiences that remain useful when network quality or device resources vary.

03

Vision, voice, and multimodal experiences

Technical work

Build camera, OCR, visual search, AR, speech, language, sensor, recommendation, and intent workflows with progressive results, confidence, and correction.

Customer benefit

The phone’s native inputs become simpler, more natural ways to complete work and understand the world around the user.

04

Mobile AI backend and enterprise integration

Technical work

Implement APIs, identity, profiles, sync, model gateways, retrieval, notifications, enterprise connectors, consent, audit, and remote configuration.

Customer benefit

The mobile experience can use trusted organizational knowledge and services without compromising user data or operational control.

05

Mobile AI evaluation and operations

Technical work

Test model quality, device/OS behavior, latency, battery, memory, privacy, accessibility, network transitions, staged release, feedback, and rollback.

Customer benefit

Teams can improve intelligence over time while protecting app quality, user trust, and the economics of mobile delivery.

Why this matters for AI

Intelligence needs a dependable execution layer.

Mobile is where AI meets personal context and hard constraints: limited battery, memory, connectivity, privacy, and many device variants. We treat the app, on-device model, cloud backend, and evaluation loop as one product so intelligence feels immediate, useful, and trustworthy in every pocket.

What you take forward

Clear outputs your team can build on.

  • AI-native mobile opportunity map, user journeys, interaction model, local/cloud decision matrix, targets, privacy posture, and rollout plan
  • Mobile AI experience package with voice, vision, language, camera, sensor, personalization, progressive results, confidence, and feedback states
  • On-device/hybrid runtime with optimized models, device matrix, routing, caching, offline behavior, sync, fallback, and update strategy
  • Cloud and enterprise integration package with mobile APIs, identity, profiles, retrieval, model gateway, notifications, consent, audit, and connectors
  • Evaluation and performance package with golden tasks, device/OS/network coverage, quality metrics, battery/memory/thermal data, and accessibility checks
  • Production handoff with telemetry, feature flags, staged rollout, remote configuration, kill switch, rollback, incident runbooks, and ownership

Capability map

Built across the layers.

The right answer is usually a connected system of decisions, not a single isolated skill.

01

AI-enabled mobile UX and conversational interaction

02

On-device, cloud, and hybrid inference

03

Camera, voice, sensor, and multimodal experiences

04

Prompt, RAG, evaluation, and guardrail integration

05

Mobile cloud backends, identity, and synchronization

06

Performance, privacy, telemetry, and release engineering

Keep exploring

See how the pieces connect.