Cross-Platform Data Archiving App

Заказчик: AI | Опубликовано: 24.03.2026
Бюджет: 1500 $

I'm looking for a checklist-style app that can archive approximately 10 daily data sets. The app should be developed for both iOS and Android platforms. Requirements: - Archive daily data sets containing text, numbers, and images. - User input via manual entry. Ideal Skills: - Experience in cross-platform app development (iOS & Android) - Proficiency in handling text, numeric, and image data - Strong UI/UX design for checklist-style input The data would then be tracked and analyzed for results trending or reaching defined limits. These trends would trigger actionable prompts for the end user. They would also be analyzed and listed on a dashboard. This dashboard would be available for supervisory guidance. This is a basic information and observation recording system which will include a daily punch list. The data analysis will consist of approximately 10 fields and the rest is recording a simple done / not done result with a few records capable of storing images for anomalies.There may exist thirty task but they are approx 20 (completed) entries and 10 variable entries which need some basic analysis resulting in prompts. Example “you’ve recorded a temp of 32 F”resulting in 1. “you may want to wear a coat. 2. Make sure windows are shut. 3. Make sure pets are inside “ The data is then looked at with basic parameters to provide a green, yellow, red condition. Green - All good Yellow - Caution (actionable) Red - Danger take immediate action This would be deployed to small to medium rural municipalities so security is important. The data would be archived for 10 years but the calculations would usually be based on prior 5-10 days results I would want the software super simple and easily navigated. With the management able to see the data and the dashboard. Operational Inputs ↓ Behavior Comparison Engine ↓ Condition Stability Rating ↓ Color Guidance Assigned ↓ Operator Response Suggested (possible query AI) ↓ Action Logged ↓ Outcome Evaluated ↓ System Pattern Learning ↓ Management Insight Output