Selected Projects
Things we’ve built.
These are personal and portfolio projects built by founder Om Langade. They are not paid client projects, and we don’t list client results, user counts or testimonials.
01 · Fake Profile Detection System
AuraVerify
- Overview
- A fraudulent-profile detection system using behavioral analysis, led by Om Langade as Project Lead of a technical team of three.
- Problem
- Fake profiles erode trust on platforms that depend on real users.
- Approach
- Behavioral signals are analysed to flag suspicious profiles, with the work organised through the full software development lifecycle.
- What was built
- Behavioral-analysis detection system
- Backend services in Java and Spring Boot with Python and Flask components
- SDLC activities, development and testing
- Technical documentation
- Java
- Spring Boot
- Python
- Flask
An abstract illustration, not a screenshot of the product.
02 · AI Automation Agency Platform
Cortex Growth
- Overview
- A SaaS-style platform concept for an AI automation agency, built by Om Langade as Full Stack Developer.
- Problem
- An agency needs to explain what it offers and turn visitors into enquiries.
- Approach
- Responsive interfaces present the services, with lead-generation functionality built into the flow.
- What was built
- Responsive interfaces
- Service management
- Lead-generation functionality
- React
- Node.js
- REST APIs
An abstract illustration, not a screenshot of the product.
03 · Transit Management
Bus Route Optimization System
- Overview
- A transit management system built with Python and Flask.
- Problem
- Running a bus network means coordinating routes, timetables and vehicle locations in one place.
- Approach
- A Flask backend exposes REST APIs for the routing, scheduling and tracking logic.
- What was built
- Bus routing
- Scheduling
- Tracking
- Python
- Flask
- REST APIs
An abstract illustration, not a screenshot of the product.
04 · Native AI Application
Multimodal macOS AI Assistant
- Overview
- A native macOS AI assistant integrating voice, camera vision and screen intelligence through a modular Swift architecture. Ongoing.
- Problem
- Most assistants work from typed text alone and lack context about what the user sees and says.
- Approach
- Voice, camera and screen inputs are processed and combined into one context, which the AI uses to respond.
- What was built
- Voice interaction with speech recognition and synthesis
- Camera and screen context using Apple Vision and OCR
- Multimodal context fusion
- Modular Swift architecture with a capability registry
- Swift
- SwiftUI
- Gemini
- AI/Vision
An abstract illustration, not a screenshot of the product.
Want something like this built for your business?
Let’s turn the next idea into a working system.