Project Overview

AISWAPR is a web-based application developed by Beyond Labs LLC, aimed at empowering content creators to seamlessly swap their faces in videos and publish them directly to social media platforms. This project combines advanced AI models with scalable web technologies to deliver a high-quality, user-friendly, and secure face-swapping experience. The platform also integrates features like caption generation, video enhancement, and automated social media scheduling.

The Process

The development began with the creation of a proof of concept (PoC) centered on the core AI functionalities: face swapping, video enhancement, and caption generation. Once the AI server was stable, the team focused on API integrations to bridge the AI system with the front-end.

Key steps:

• PoC with AI models for core functionality.
• Development of REST APIs for interaction between front-end and AI.
• UI/UX development with Next.js.
• Deployment using AWS (back-end), TensorFlow (AI), and Vercel (front-end).
• Iterative sprints managed using ClickUp and tracked in Slack.

Challenges and Solutions

1. Face Swapping Accuracy:

• Initially, the team used the DeepFace VFX model, but it failed to deliver consistent results. Switching to the NFNet model allowed better training convergence and visual output. The training dataset was enhanced by requiring users to upload multiple facial angles.

2. Challenge: Synchronizing AI outputs with front-end functionality.

• Running AI inference on high-resolution videos posed performance challenges. This was addressed by optimizing model throughput and using TensorFlow-serving on GPU instances.

3. Ethical Use & Privacy:
AISwapr implemented a KYC onboarding system via Stripe and obtained explicit user agreements through a legal compliance partner. Captions were converted to text using ChatGPT and validated to avoid misinformation.

Key Contributors

• Darshan, Kunj - Sr. Front-End Developers
• Prashant, Victor - Sr. Data Scientists
• Chirag Gupta - CTO
• Rishab - Sr. Back-End Developer
• Madhuri, Yuvraj - Front-End Developers

Key Features

• Face Swapping: Accurate, user-verified face replacement in uploaded videos.
• Caption Generation: Audio-based transcript generated using ChatGPT API.
• Video Enhancement:  Frame-level enhancement using custom-trained models.
• Social Media Scheduler: In-app functionality to post videos to connected social

Development Journey

Stage 1: Research & Proof of Concept

• AI Model Exploration: The team evaluated multiple face-swapping frameworks, beginning with DeepFace VFX. Several limitations were identified, including inconsistent face morphing and motion blur artifacts.
• Switch to NFNet + Roop: Upon evaluation, a hybrid approach using Roop for initial facial landmarking and NFNet for model stability was proposed.
• User Flow Planning: Product designers collaborated with AI engineers to define the application’s user journey. High-level wireframes were drafted to capture essential steps—upload, face selection, AI rendering, and export.
• Initial PoC: A backend-only PoC was built to test face-swapping capabilities on a limited dataset, which served as the baseline for model tuning.

Stage 2: Front-End & API Foundation

• Front-End Scaffolding: Developers began building the front-end using Next.js, focusing on scalable component design and responsive video handling.
• API Layer (Django): Backend engineers architected RESTful APIs in Django, enabling communication between the AI models, video rendering services, and the front-end.
• Authentication & User Management: Sign-up/sign-in mechanisms and KYC workflows were integrated using Stripe APIs, ensuring secure and verified onboarding.
• Project Management Setup: ClickUp was structured for sprint planning, with Slack as the main channel for real-time collaboration.

Stage 3: Front-End & API Foundation

• Model Tuning: Based on PoC learnings, the face-swapping models were retrained with enhanced datasets, including multi-angle facial images from users to improve accuracy and realism.
• Video Rendering Engine: A frame-by-frame rendering pipeline was created using TensorFlow, applying face swaps and video enhancement layers in real-time.
• Caption Generation: Integrated OpenAI’s GPT APIs to transcribe audio and generate contextual captions, adding accessibility and SEO benefits.
• System Integration: APIs were stress-tested with the front-end to validate performance under concurrent video render requests.

Stage 4: Finalisation & Deployment

• End-to-End Testing: Comprehensive testing across the application’s modules ensured minimal latency, accurate face rendering, and seamless user experience.
• Legal Compliance: Legal advisors reviewed onboarding flows and user agreements. KYC procedures and consent forms were made mandatory for ethical safeguards.
• Deployment & Scalability: Front-end was deployed on Vercel, while the backend services and AI models were scaled on AWS, ensuring high availability and rapid media processing.

Measuring Success

Quantitative Milestones:

• Face Swapping Accuracy Rate: Target ≥ 92% face alignment precision; achieved ~95% after model optimization.
• Rendering Speed: Target of < 5 minutes per video (average 60s clips); achieved an average processing time of 3.5 minutes.
• Uptime & Availability: >99.5% server availability post-deployment on AWS infrastructure.
  Successful KYC Validations: 100% user verification with no fraud reports.

Qualitative Metrics:

• Client Satisfaction: The client was extremely satisfied, citing that previous agencies had failed to deliver even a working demo. BeyondLabs not only completed the core functionality but also exceeded expectations with stability and accuracy.
• User Experience: Positive feedback on intuitive UI/UX, minimal wait times, and high-quality outputs.
• Compliance & Trust: No complaints regarding misuse or unauthorized content, affirming the success of the ethical guidelines embedded into the platform.

Impact

AISWAPR has positioned itself as a cutting-edge personalization tool for digital creators and media studios. Its impact includes:

• Enabling new forms of media personalization
• Dramatic reduction in production turnaround time
• Empowering ethical AI deployment in content creation

Conclusion

AISWAPR is a testament to Beyond Labs LLC’s technical strength in building production-grade AI applications. From its robust architecture to its ethical handling of deepfake-like technology, AISWAPR showcases how innovation and responsibility can co-exist. With this launch, BeyondLabs continues to lead in scalable AI product development, empowering creators to do more — ethically and intelligently.