Once upon a time, having an assistant in your pocket meant using Siri or Alexa. But the expectations of 2025 go further. Users don’t just want a generic assistant – they want help that’s embedded directly into the apps they use every day.
Think of a fitness app that talks back when you miss a run, or a banking app that answers questions about card transactions instead of redirecting you to a call center. These assistants aren’t separate apps. They’re part of the experience itself.
Igor Izraylevych, CEO of S-PRO, calls it the “post-app menu world”:
“Nobody wants to dig through tabs and buttons just to find one piece of information. AI assistants take that work away. They sit inside the app, understand context, and respond instantly.”
Apple’s Building Blocks: Core ML and SiriKit
Apple has spent years laying the groundwork for developers to bring intelligence into iOS. Two frameworks stand out:
- Core ML – Apple’s machine learning framework that allows AI models to run directly on the device. No cloud round-trips, no privacy nightmares. It supports natural language processing, image recognition, sound analysis, and more. For instance, a travel app can use Core ML to scan a boarding pass via camera and instantly pull flight details.
- SiriKit – The bridge to Apple’s native voice assistant. Apps can integrate with Siri to handle tasks like payments, messaging, or workouts through voice. A ride-hailing app can register an intent so users just say: “Hey Siri, book me a ride to the airport”.
Together, these frameworks give developers the tools to merge AI intelligence with mobile convenience. Many companies partner with mobile app development companies to build solutions that feel natural instead of clunky add-ons.
Beyond Apple: Third-Party APIs and Custom Agents
Core ML and SiriKit are powerful, but not always enough. Businesses often want assistants that can handle more than Apple’s predefined intents. That’s where third-party APIs come in.
For example:
- OpenAI’s GPT models for conversational chat inside apps.
- Stripe’s API for voice-enabled payments.
- Wit.ai or Rasa for building domain-specific dialogue systems.
Imagine a healthcare app where the AI assistant helps patients log symptoms, schedules follow-ups, and even reads lab results in plain English. None of this requires a new app – the assistant lives right where patients already interact.
Why On-Device AI Matters
Running AI directly on iPhones through Core ML isn’t just a technical detail – it’s a business advantage.
- Privacy: Sensitive data (like health logs or financial info) never leaves the device.
- Speed: Users get instant responses, not delayed by network calls.
- Cost: Companies save on cloud inference bills by offloading work to devices.
Igor points out:
“Every business that builds an assistant faces the same decision – do you process in the cloud or on-device? For iOS, Core ML shifts the economics. It’s not just greener, it’s also cheaper long-term.”
Industry Examples Already Emerging
- Banking: Capital One built an AI-powered assistant, Eno, that’s available across web and mobile. It answers transaction queries with natural language – no scrolling through endless statements.
- Health: Apps like Cardiogram use on-device AI to detect heart irregularities in real time. Patients don’t need to send data to a server; the app flags issues instantly.
- E-commerce: Shopping apps now experiment with conversational checkout – users can ask, “Did my order ship yet?” and get an answer right in the chat.
These examples show the future isn’t theoretical – it’s already in users’ hands.
The Design Challenge: Building Assistants People Trust
AI assistants are only useful if people trust them. That means designing assistants that:
- Explain why they’re giving a certain recommendation.
- Admit when they don’t know the answer.
- Protect sensitive data by default.
For business owners, this is the hardest part. Building the assistant technically is possible with today’s frameworks. Building it in a way that users feel comfortable relying on – that’s where the expertise of AI developers is needed.
Reflections: Where It’s Headed
The assistant inside apps won’t stay simple for long. As Igor reflects:
“We’re moving toward apps that don’t just answer questions but take initiative. Imagine a finance app that nudges you when spending spikes, or a wellness app that suggests rest when it notices fatigue patterns.”
That future requires blending AI models, APIs, and thoughtful UX design. It’s not about replacing apps, but about making them feel less like software and more like helpful companions.
