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Vision-Based App Automation

A React Native mobile agent that automates any phone app by reading the screen with an LLM and performing taps like a person.

Services
Custom Mobile App Development ServicesUI/UX Design ServicesTeam Extension services
Client
Under NDA
Location
USA
Industry
Results
20x
smaller screenshot payload per request
70%
lower cloud infrastructure cost
15x
15x less training and inference data
Current thumbnail
Team
React Native DeveloperAI / LLM Engineer
Technologies
React Native
React Native
Python
Python
Neo4j
Neo4j

Vision-Based Automation

The agent operates any installed app by reading the screen and simulating human actions.
  • Scans installed apps to target automations
  • LLM analyzes screenshots to decide the next action
  • Executes taps and gestures through LADB on-device
  • Works across any app with no API needed
Vision-Based Automation

Image Preprocessing Pipeline

A preprocessing stage prepares each screenshot before it reaches the LLM.
  • Strips a channel to reduce image weight
  • Upscales frames to preserve LLM detection accuracy
  • Cuts payload from 2MB to 100KB per frame
  • Reduces training and inference data 15 times
Image Preprocessing Pipeline

Train-Then-Infer Lifecycle

Each automation moves from supervised training to autonomous execution.
  • Users describe each automation with a text prompt
  • Training mode lets users confirm, edit, or reject actions
  • Inference mode runs automations autonomously and faster
  • LangGraph and Neo4j store learned automation steps
Train-Then-Infer Lifecycle

Composable Automations

Simple automations combine into multi-step cross-app workflows.
  • Chain up to 10 automation blocks per workflow
  • Reuse simple automations as building blocks
  • Combine actions across separate apps in one run
  • Example: food ordering linked to calorie logging
Composable Automations

Kanban

  • Visual board organizing tasks by workflow status
  • Five stages from upcoming through testing to completed
  • Story points estimate task complexity and time
  • Task cards hold specifics, assignees, and details
Kanban

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Challenges & Solutions

During our work on this project, we've faced a lot of complicated challenges. We believe that each challenge has a solution. See yourself!
challenge #1
Every app behaves differently
solution #1

A vision-based engine that reads each screen with an LLM and simulates human taps, removing the need to integrate with any app's API.
challenge #2
Heavy images slowed the engine
solution #2

An image preprocessing pipeline that strips a channel and upscales each frame, cutting payload from 2MB to 100KB and reducing data volume 15 times.
challenge #3
Some actions needed native code
solution #3

Native Kotlin modules powering the on-device overlay widget and system-level actions outside React Native's reach.

Clients say

Once we showed designs (e.g. Figma) of what we needed, along with aggressive deadlines, Incora always delivered on time and in many cases when above and beyond to build beautiful, robust UI interfaces for us. In addition to the high skill level of their engineers, I am very impressed with how personable and kind every employee, including their leadership, is. Their values and professionalism stand out, and they make it easy to partner with and communicate with them!

David Park

David Park

CEO & Co-Founder, Narada AI

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Danylo Karpa
Danylo Karpa
Head of Strategic Partnerships

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