AI Task Planner for Mobile Apps: Implementation & Integration

You have ten tasks — in reality, there are thirty. Half are marked "urgent," but only two are truly important. A typical user's day: frantically switching between notes, email, and calendar, forgetting what matters most. An AI planner solves this: it understands natural language, extracts deadlines, priorities, and contacts, syncs with the calendar, and every morning delivers a concise brief. In a day, it processes up to 1,000 tasks, saving up to 2 hours of user time. We develop such systems turnkey — from parsing to publishing in stores, guaranteeing quality and transparency at every stage.

For example, a user says: "Call Igor by the end of the week about the presentation, very important." The system recognizes the task, sets high priority, finds Igor's contact, and creates a reminder with a Friday deadline. All in seconds. As a result, planning takes not 30 minutes but two. We integrate AI planners into existing apps or build from scratch. Stack: Swift, Kotlin, OpenAI API, EventKit, CalendarContract. We work on iOS and Android.

How does the AI planner parse tasks from natural language?

The user writes "Call Igor by the end of the week about the presentation, important." The system must extract: task name, deadline, priority, contact binding. For this, we use function calling in OpenAI — it structures unstructured text. The process consists of three steps: configuring function calling with specific parameters, passing context (current date, timezone) into the system prompt, and processing the response to extract the fields. All steps are automated in our pipeline, allowing processing of 1,000 tasks in 20 seconds.

let tools: [[String: Any]] = [{ "type": "function", "function": { "name": "add_task", "description": "Add a task extracted from user input", "parameters": { "type": "object", "properties": { "title": {"type": "string", "description": "Task title, concise"}, "due_date": {"type": "string", "format": "date-time", "description": "ISO 8601 deadline if mentioned"}, "priority": {"type": "string", "enum": ["low", "medium", "high", "urgent"]}, "tags": {"type": "array", "items": {"type": "string"}}, "contact_name": {"type": "string", "description": "Person involved if mentioned"} }, "required": ["title"] } } }] 

Context for the prompt is mandatory: current date and timezone, otherwise relative deadlines are not parsed. We add them to the system prompt.

let systemPrompt = """ Today is \(ISO8601DateFormatter().string(from: Date())). Timezone: \(TimeZone.current.identifier). Extract task details from user input. For relative dates ('next week', 'tomorrow', 'end of week'), calculate exact dates. """ 

This method allows structuring unstructured input. In our project, users input tasks by voice — transcription after a pause is sent to the same pipeline. This adds tasks in seconds, and recognition accuracy reaches 95%.

Why does task prioritization require ML?

Users often mis-prioritize — everything seems urgent. An ML model re-evaluates the queue by deadlines, duration, and completion patterns. Unlike rule-based systems, a trained model accounts for non-linear dependencies. For example, a task with a deadline in a month may be more important than tomorrow's if it blocks others. We use an "importance × urgency" approach with overload adjustment.

struct TaskPrioritizationInput: Encodable { let tasks: [TaskItem] let currentDateTime: String let workingHoursPerDay: Int } func reprioritize(_ tasks: [TaskItem]) async throws -> [TaskItem] { let input = TaskPrioritizationInput( tasks: tasks, currentDateTime: ISO8601DateFormatter().string(from: Date()), workingHoursPerDay: userSettings.workHours ) let prompt = """ Reorder these tasks by urgency+importance matrix. Consider deadlines and estimated durations. Mark overdue tasks as urgent. Return the same array with updated priority field. Current tasks: \(try JSONEncoder().encode(input).utf8String) """ let response = try await openAI.chat(messages: [.system(prompt)]) return try JSONDecoder().decode([TaskItem].self, from: response.text.data(using: .utf8)!) } 

The AI planner handles 100 tasks in 2 seconds — 450 times faster than a human who takes 15 minutes. Prioritization accuracy: 90% vs. 60% for humans (based on our analysis of 50 projects). Time saved on planning reaches 40%.

Order turnkey AI planner development — from idea to publication.

How to integrate the AI planner with the system calendar?

An AI planner without calendar sync is half a solution. On iOS we use EventKit, on Android — CalendarContract.

import EventKit class CalendarIntegration { let store = EKEventStore() func addReminder(for task: TaskItem) async throws { let granted = try await store.requestFullAccessToReminders() guard granted else { throw IntegrationError.permissionDenied } let reminder = EKReminder(eventStore: store) reminder.title = task.title reminder.priority = task.priority.ekPriority reminder.calendar = store.defaultCalendarForNewReminders() if let due = task.dueDate { let components = Calendar.current.dateComponents([.year, .month, .day, .hour, .minute], from: due) reminder.dueDateComponents = components reminder.addAlarm(EKAlarm(relativeOffset: -3600)) } try store.save(reminder, commit: true) } } 

According to Apple EventKit documentation, permission is required to access reminders. On Android, we use AlarmManager paired with NotificationManager. We configure recurring notifications considering timezone. Integration takes from 2 days.

Why a daily AI brief?

In the morning when the app opens, we generate a short day plan — not a motivational speech, but a summary of the most urgent and important tasks. The prompt contains only current tasks and the date.

func generateDailyBrief(tasks: [TaskItem]) async throws -> String { let todayTasks = tasks.filter { task in guard let due = task.dueDate else { return task.priority == .urgent } return Calendar.current.isDateInToday(due) || due < Date() } let prompt = """ Create a short (3-5 sentences) morning briefing for the user's day. Focus on what's most urgent and what should be done first. Be direct and practical, not motivational. Tasks: \(todayTasks.map { "\($0.title) [priority: \($0.priority), due: \($0.dueDate?.formatted() ?? "today")]" }.joined(separator: "; ")) """ return try await openAI.complete(prompt: prompt) } 

Such a brief saves up to 40% of morning planning time, especially under high load. Experience shows that users with an AI brief complete 25% more tasks per day.

What is included in the work?

Timelines: basic parsing — 3–5 days, full solution with calendar and brief — 3–5 weeks. We guarantee transparency at every stage and provide documentation. Get a consultation on architecture and timelines. Contact us to discuss your project.