What We've Built
Selected projects and case studies.
Overview
An autonomous quadrotor system integrating real-time AI reasoning with physical flight control. Designed to operate within structured and semi-complex environments, the system uses visual perception and language-based decision-making to guide navigation.
By combining traditional control engineering with modern AI inference, it forms a closed-loop autonomous navigation pipeline.
The Objective
Design and implement an autonomous drone system that:
- Maintains stable flight using PID control
- Interprets visual input from onboard camera feeds
- Uses a Vision-Language Model to determine movement decisions
- Executes navigation commands in real time within a simulation environment
The focus was not just autonomy — but structured integration between control systems and AI reasoning.
System Architecture
The solution was designed as a layered system:
Control Layer
A PID-based stabilization system maintained quadrotor balance and altitude control. Damping parameters were tuned to prevent oscillation while maintaining responsiveness.
Perception Layer
A simulated onboard camera captured environmental imagery. Frames were sent through an API pipeline for analysis by a Vision-Language Model.
Decision Layer
Two VLM implementations were evaluated: a PyTorch-based model and a locally deployed LLaVA model via Ollama. The selected model interpreted visual context and returned structured movement guidance.
Execution Layer
Parsed responses were translated into directional commands and sent to the drone controller, completing a closed feedback loop.
This architecture allowed perception, reasoning, and actuation to operate as coordinated subsystems.
Simulation Environments
Two environments were developed to validate system behavior:
Structured Obstacle Arena
A controlled test environment with fixed walls and static objects. This phase focused on validating stability, object recognition, and response timing.
Urban Community Scenario
A more complex environment featuring houses, vehicles, and trees. The drone was tasked with navigating between defined points while adapting to environmental obstacles.
These environments enabled iterative testing of both flight stability and AI decision reliability.
Technical Challenges
Stability vs Responsiveness
- PID parameters required careful tuning.
- Excess damping reduced maneuverability.
- Insufficient damping caused oscillation and drift.
Balancing control responsiveness with stability was critical to achieving smooth flight behavior.
Real-Time AI Inference
The initial PyTorch model delivered accurate perception results but introduced significant inference latency. The added computational load disrupted flight timing and reduced system responsiveness.
To address this, the system transitioned to a locally deployed LLaVA model via Ollama, which provided:
- Faster inference
- Reduced overhead
- Improved compatibility with real-time control loops
This tradeoff prioritized system performance over raw model complexity.
Results
The final system achieved:
- ✓Stable quadrotor flight within simulated environments
- ✓Real-time VLM-driven navigation
- ✓Successful obstacle-aware movement
- ✓Fully integrated AI-controller communication pipeline
Interested in Building Systems Like This?
Whether integrating AI into applications or designing structured system architectures, we apply the same engineering discipline to every build.
Start a Project →Overview
The Driver Incentive Platform involved designing and implementing a structured incentive platform that connects drivers, sponsors, and administrators within a unified system.
The application supports role-based access, multi-organization management, and secure point tracking — providing a scalable foundation for reward-based engagement programs.
The goal was to build a production-ready system capable of supporting real-world usage scenarios, authentication workflows, and administrative oversight.
The Objective
Design and deploy a full-stack platform that:
- Supports three distinct user roles (Driver, Sponsor, Admin)
- Manages sponsor-driver relationships through controlled linking
- Tracks point balances across multiple sponsors
- Provides secure authentication and audit logging
- Enables scalable database modeling for long-term growth
System Architecture
The platform was designed around a clear separation of concerns:
Frontend Layer
Built with React, the frontend provides role-based dashboards tailored to:
- Drivers (point tracking, sponsor switching)
- Sponsors (organization management, catalog oversight)
- Admins (bulk uploads, user management)
Backend Layer
Node.js and Express handle:
- RESTful API routing
- Authentication logic
- Role validation
- Data processing
- Secure endpoint control
Sequelize ORM was used to enforce relational integrity and model associations cleanly.
Database Design
MySQL schema includes:
- users (authentication, roles, login tracking)
- SponsorDriverLink (many-to-many relationship)
- Organization structures
- Role-based point tracking
- Audit fields for security logging
Key Features
Multi-Sponsor Architecture
Drivers can be linked to multiple sponsors simultaneously, with isolated point balances and organization switching.
Role-Based Dashboards
Each user role sees a custom interface and functionality based on access permissions.
Bulk User Loading
Admins and sponsors can upload structured text files to onboard users efficiently while handling line-level errors gracefully.
Organization-Specific Catalogs
Sponsors manage curated product catalogs tied to their organization, enabling structured reward redemption.
Authentication & Security
Password reset flows, failed login tracking, and account locking were implemented to ensure basic security hygiene.
Results
The final platform achieved:
- ✓Fully functional role-based incentive management
- ✓Scalable relational data modeling
- ✓Secure authentication flows
- ✓Cloud deployment with live environment support
- ✓Structured admin oversight tools
The system demonstrates practical full-stack engineering, database architecture design, and scalable user-role management.
Interested in Building Scalable Systems?
Whether developing internal tools, incentive platforms, or multi-role web applications, we apply structured engineering principles to ensure systems are clean, scalable, and production-ready.
Start a Project →