AI app development company · EonTech Poland

AI app development,
shipped to the phone.

EonTech is a Poland-based AI app development company. We build AI-powered mobile apps on iOS, Android, React Native, and Flutter — with on-device inference, cloud LLM integration, computer vision, voice, and AR/VR/AI features. Mobile engineers and ML engineers work inside the same squad, so the intelligence and the interface are designed together.

Start your project Mobile app development
  • Mobile + AI · one accountable team
  • On-device · cloud · hybrid inference
  • GDPR · ISO 27001 · Responsible AI defaults
Mobile + AI
One team ships both the app and the intelligence inside it
Senior only
Engineers who understand both mobile constraints and model behaviour
Your IP
App code, prompts, model weights, and eval sets stay yours

Why this page exists

A working guide to AI-powered mobile app development

The gap between a demo AI feature on a laptop and one that ships to millions of phones is much larger than a wrapper around a model API. Mobile is where AI runs into background limits, battery budgets, offline states, cost controls per user, App Store and Play Store review policies, and the awkward fact that a large language model will confidently produce the wrong answer if you let it. An AI mobile app development company earns its keep by treating the model as a system with real behaviour — not a magic API — and designing the mobile UX around that behaviour.

EonTech is a full-cycle Poland-based engineering agency covering custom software development, web and mobile, cloud and DevOps, QA, and custom AI/ML model development. Our AI-mobile practice sits inside that broader team, so as a custom AI app development company we can hand you both the app and the intelligence inside it under one contract — with the same engineers designing prompts, tool calls, on-device models, and native UI.

The rest of this page is a practical brief for CTOs, product leads, non-technical founders, and mobile leads comparing an AI-powered app development company against a generic mobile shop, an ML-only vendor, or DIY hiring. Everything below is drawn from actual delivery — no fabricated stats, no fabricated case studies.

Key benefits

Why teams pick EonTech as their AI-powered mobile app development company

Six practical reasons EU and UK product teams choose our AI mobile practice over a two-vendor stack or a generic offshore shop.

  • Mobile and AI, one accountable team

    As a custom AI app development company we field mobile engineers and ML engineers inside the same squad. No handoff between the vendor who shipped the app and the vendor who bolted on the model.

  • On-device where privacy matters

    For sensitive data — health, biometrics, financial — inference can stay on the phone. Core ML, ML Kit, TensorFlow Lite, ONNX Runtime, and Whisper.cpp all live inside our default toolbox.

  • Cloud LLMs done properly

    Streaming, cancellation, tool calls, structured outputs, prompt caching, and cost controls — the difference between a demo and a production AI-powered mobile app.

  • EU nearshore, real-time overlap

    Poland-based senior engineers with full-day CET overlap for the UK, EU, and Nordics. Design and model-behaviour reviews happen live, not overnight.

  • Honest about when AI does not belong

    Some "AI features" ship as slower, less reliable versions of a rules engine. As an AI mobile app development company we say so — the goal is a better product, not a wrapper around the trending API.

  • You own everything

    App code, mobile prompts, tool definitions, evaluation datasets, and any fine-tuned or distilled model weights are yours from day one. No proprietary layer, no lock-in.

Where AI moves the needle on mobile

Six families of AI-driven mobile features we ship most often

"AI feature" is a broad label. On mobile it collapses into a small set of concrete patterns — each with its own model shape, its own UX constraints, and its own store-review considerations.

  • Conversational & assistant flows

    Streaming chat, tool calls, retrieval, and structured outputs — inside a mobile UI that respects background limits, offline states, and battery.

  • Vision & camera-driven features

    Real-time inference on the camera feed for document scanning, product recognition, gesture, or accessibility use cases — on-device where latency demands it.

  • Voice, transcription & translation

    On-device speech-to-text, live translation, and audio-first UX with a soft path back to the cloud when the model on the device is not enough.

  • Personalisation & recommendation

    Per-user ranking, journal-and-suggest patterns, and behavioural personalisation. Trained on your data, run inside your GDPR boundary.

  • Health, fitness & nutrition intelligence

    The kind of features an AI nutritionist app development company ships — food recognition from a photo, macro estimation, personalised suggestions, and honest confidence signals.

  • AR / VR / AI overlays

    As an AR VR AI mobile app development company we combine ARKit / ARCore with vision and LLM layers — try-on, spatial guidance, and inspection overlays that survive real-world lighting.

How it works

From an AI product hypothesis to a shipped mobile release

Every AI-driven mobile app development engagement runs the same arc: honest framing, feasibility, architecture, iteration, and monitored operation. You always know what the next milestone is.

  1. 01

    Frame the AI feature honestly

    We start with what the user is actually trying to do. Some workflows are transformed by AI; others are only slowed down by it. The output of step one is a written scope with the features we will and will not build.

  2. 02

    Data & feasibility spike

    We assess the data you already have (or can collect), pick a candidate model family, and run a one-to-two week feasibility check that answers the accuracy, latency, and cost questions in writing.

  3. 03

    Architect for the phone

    On-device vs cloud vs hybrid inference, tokenisation and streaming, offline behaviour, battery budget, and the fallback path when the model fails. Decided as a system, not per PR.

  4. 04

    Ship, evaluate, iterate

    We ship the AI-driven mobile app to TestFlight and Play internal tracks with real users, wire evaluation and telemetry, and iterate on prompts, models, and UX in short cycles.

  5. 05

    Operate and monitor

    Once live, model drift, cost, latency, refusal rates, and safety flags are on a dashboard your team owns. Not a black box, not a magic API — a monitored system.

On-device · cloud · hybrid

Four honest ways to run AI inside a mobile app

The deployment choice — where the model actually runs — is usually the most consequential decision in an AI-powered mobile app. These are the four shapes we recommend, keyed to the product problem in front of you.

  • On-device inference

    Runs the model on the phone via Core ML, ML Kit, TensorFlow Lite, ONNX Runtime, or MLX. Best for privacy-sensitive data, offline flows, latency-critical UX, and small-to-medium models (up to a few billion parameters on flagship hardware).

  • Cloud LLM & model APIs

    Streaming calls to Anthropic Claude, OpenAI, Google, or your self-hosted model, wrapped in a mobile-safe transport with retry, cancellation, cost caps, and structured-output validation.

  • Hybrid (edge + cloud fallback)

    The default we recommend for most AI-powered app development company engagements. Simple tasks run locally with zero network cost; harder tasks escalate to the cloud with the user's consent and clear signalling.

  • Custom fine-tuned & distilled models

    When a general model is expensive or slow, we distil or fine-tune a smaller model on your domain and ship it on-device or on your own inference infrastructure. Weights and eval sets stay yours.

Features built in by default

The AI-mobile plumbing that separates a demo from a shipped app

  • Streaming UX. Token-by-token responses with proper cancellation and mobile-safe transports (Server-Sent Events, chunked responses).
  • Offline & flaky-network first. AI features that behave when the phone loses signal, backgrounds, or gets throttled — not just when the demo Wi-Fi is perfect.
  • Structured outputs. Tool calls, JSON schemas, and typed responses so the model's answers slot into the UI without brittle parsing.
  • Prompt & model versioning. Every prompt, tool definition, and model choice is versioned like code, with the ability to roll back a change that regressed a metric.
  • Evaluation & drift monitoring. Held-out eval sets, LLM-as-judge where appropriate, and dashboards that catch quality regressions before users do.
  • Cost controls per user. Token budgets, per-tenant caps, cascading from cheaper to more capable models, and clear signalling when a limit is hit.
  • Safety filters & refusals. Content policies, refusal handling, and the UX for gracefully saying "the assistant will not help with that".
  • Native module bridges. Where React Native or Flutter cannot reach a native ML runtime directly, we drop into Swift/Kotlin — cleanly, only where needed.

Main use cases

AI-powered mobile app shapes we build most often

Six recurring product shapes — each with its own model, latency, and privacy profile. A specialised mobile AI app development company recognises them on the first call.

  • AI assistants inside consumer apps

    The default AI-powered mobile app shape: streaming chat, task-specific tools, and grounded retrieval. Built to survive real user prompts, not just demo happy paths.

  • AI nutritionist & health-log apps

    Food recognition from a photo, macro and micronutrient estimation, plan personalisation, and honest confidence signals — the pattern an AI nutritionist app development company ships when the goal is retention, not novelty.

  • AR/VR/AI try-on and inspection

    Retail try-on, spatial measurement, and inspection guidance that combine ARKit/ARCore geometry with vision models and (where useful) a language layer for narration or coaching.

  • Camera-first productivity

    Document scanning, whiteboard capture, translation overlays, and receipt intelligence — with on-device models for privacy and speed.

  • AI-driven personalisation at scale

    Per-user ranking and recommendations inside content, social, or commerce apps. Trained on your telemetry, run inside your compliance boundary.

  • Voice-first & accessibility flows

    Live transcription, translation, and dictation as first-class UX — plus AI-driven accessibility features that turn a well-built app into a usable one for everyone.

Industry relevance

Where AI-mobile teams already ship with us

Domain fluency shortens every AI conversation. These are the buyer shapes our senior engineers already speak — no glossary onboarding.

  • Health, wellness & fitness

    Nutrition, habit, mental-health, and remote-monitoring apps where AI improves adherence and personalisation — without pretending to be a clinician.

  • Fintech & payments

    Fraud signals, transaction intelligence, and support assistants inside banking, wallet, and trading apps.

  • E-commerce & retail

    Visual search, AR try-on, personalised discovery, and shopping assistants that convert without dark patterns.

  • Media & entertainment

    AI-driven discovery, transcription, translation, and creator tooling inside streaming, community, and content apps.

  • Productivity & SaaS

    AI companions inside mobile productivity apps — dictation, summarisation, action extraction, and calendar assistance.

  • Logistics & field-ops

    Photo-based damage capture, part recognition, and voice-driven data entry for drivers, technicians, and inspectors.

Trust & compliance signals

Signals procurement, legal, and store reviewers actually check

AI mobile compliance sits at the intersection of two moving targets: mobile privacy (Apple's privacy nutrition labels, Google's data-safety forms, GDPR consent) and AI-specific policies (Apple's Generative AI guidance, Google's GenAI policies, and — soon — the EU AI Act). Our recommendations always answer both.

As an EU-based AI app development company we sign standard DPAs and SCCs, run access reviews on your cadence, and design in the responsible-AI defaults (refusal handling, human oversight, bias review) that regulators expect from a serious partner.

Common questions

What teams ask an AI mobile app development company

  • What does an AI app development company actually build for mobile?

    AI-powered mobile apps — iOS, Android, React Native, Flutter — with intelligence built in, not bolted on. That usually means one or more of: conversational assistants, camera-driven vision features, voice/transcription, personalisation, or embedded fine-tuned models. Both the app and the AI layer come from the same team.

  • How is an AI mobile app development company different from a generic mobile shop?

    A generic mobile shop treats the model as a third-party API and hopes for the best. An AI mobile app development company treats the model as a system with real behaviour — latency, cost, refusal rates, drift, safety — and designs the mobile UX around that behaviour. It also owns the difference between on-device, cloud, and hybrid inference instead of defaulting to whichever is easier.

  • Do you run inference on-device or in the cloud?

    Both, depending on the feature. Sensitive data, offline flows, and latency-critical UX go on-device (Core ML, ML Kit, TensorFlow Lite, ONNX). Heavier reasoning and multi-turn conversations go to a cloud model. Most real products end up hybrid — simple tasks locally, harder ones in the cloud with clear signalling to the user.

  • Which LLM providers do you integrate with?

    Anthropic Claude, OpenAI, Google, Mistral, and self-hosted open models (Llama, Qwen, Gemma) via your own inference endpoints. We are provider-agnostic — the model that fits your accuracy, latency, cost, and data-residency profile wins, not the one with the best sales team.

  • Can you build an artificial intelligence mobile app development company engagement around fine-tuning our own model?

    Yes. When a general model is too slow or expensive, we distil or fine-tune a smaller model on your data — either on the phone via Core ML / ONNX, or on your own inference infrastructure. Weights and eval sets stay yours; you can move providers without losing the model.

  • Do you build AI nutritionist app features?

    Yes. As part of health and wellness engagements — the shape a specialised AI nutritionist app development company ships — we build food recognition from photos, macro/micro estimation, plan personalisation, streak/habit UX, and the honest confidence signalling that keeps users trusting the app past week two.

  • Do you combine AR, VR, and AI in the same app?

    Yes — this is a common ask. As an AR VR AI mobile app development company we combine ARKit or ARCore for geometry with vision models for recognition and (where useful) an LLM for narration, coaching, or Q&A. Common use cases are retail try-on, spatial measurement, remote inspection, and immersive training.

  • How do you handle privacy and consent for AI-driven mobile apps?

    Personal data is minimised, on-device where feasible, and processed inside your GDPR boundary when it must be in the cloud. Consent is explicit and revocable, prompt logging is opt-in with retention limits, and privacy nutrition labels (Apple) and data-safety declarations (Google) are drafted with you before submission.

  • How do you keep model quality from drifting in production?

    Held-out evaluation sets, LLM-as-judge where appropriate, per-feature metrics on dashboards, and canary rollouts for prompt/model changes. Regressions block the release the way test failures do.

  • How do you handle cost when a user hammers the assistant?

    Per-user and per-tenant token budgets, model cascading (start cheap, escalate only when needed), prompt caching, and clear signalling when a soft cap is hit. Cost is a first-class product concern, not a monthly surprise on the invoice.

  • What team shapes can you field for an AI mobile project?

    Full-project delivery, dedicated AI-mobile pods (mobile engineers + ML engineers + PM), or staff augmentation with senior mobile and ML engineers plugged into your team. Monthly rolling contracts, no long lock-in.

  • How long does a first release of an AI-powered mobile app take?

    For a scoped first release with one or two AI features, 12–20 weeks from discovery to TestFlight / Play internal is realistic. A pure PoC that answers "can this model do this task reliably enough on this device" typically fits in 3–6 weeks.

  • Do you support both iOS and Android in one engagement?

    Yes. Native iOS + Android, React Native, or Flutter — we pick based on the product shape and the AI features involved. On-device model support varies by platform (Core ML vs ML Kit vs TensorFlow Lite vs ONNX), and we weigh that when we recommend a stack.

Related practices

Where AI mobile connects at EonTech

Ship the AI feature the phone can actually hold

Tell us the product, the AI feature you have in mind, and the platform you ship on. We will scope a fixed feasibility spike in days, then hand you a written answer on accuracy, latency, cost, and the first shipping milestone.

Start your project [email protected]