Powering Creative Automation with Scalable AI Infrastructure
At DEIENAMI, we partnered with a media technology company to engineer the backend systems and infrastructure for a cutting-edge Generative AI platform designed to automate the breakdown of movie scripts and generate scene-based storyboards. While the frontend was handled separately, our core focus was on building the robust backend, ensuring secure, scalable deployments, and implementing an efficient MLOps pipeline to manage model lifecycles.
The Challenge
The platform needed to:
- Efficiently process long-form movie scripts using large language models
- Extract structured data for scenes, props, environments, and characters
- Generate consistent and scene-accurate visual outputs via image generation pipelines
- Handle high volumes of concurrent usage without compromising performance
- Maintain data privacy and ensure model reproducibility
Our Role & Solution
DEIENAMI’s responsibilities included:
- LLM Infrastructure & Backend API Engineering
- Built Python-based backend using FastAPI, optimized for streaming inference and token-based processing
- Designed prompt orchestration and segmentation logic to handle large scripts
- Scalable Data Architecture
- Implemented PostgreSQL for metadata storage and Redis for async task handling
- Integrated file-based and database logging for traceability and audit trails
- Image Generation Pipeline Handling
- Orchestrated stable diffusion models (SDXL, ControlNet) via task queues
- Ensured reproducibility by saving generation parameters and prompts per frame
- MLOps Integration
- Created model versioning and rollout pipelines using Git, MLflow, and Docker
- Built CI/CD for retraining, testing, and deploying model updates seamlessly
- Security & Compliance
- Deployed infrastructure on secure VPC-based AWS environments
- Applied encryption at rest and in transit, role-based access control, and container-level isolation
- Designed all systems to follow ISO 27001-aligned practices for data handling and deployment
Tech Stack
| Component | Technology Used |
|---|---|
| Backend Framework | FastAPI (Python) |
| Data Store | PostgreSQL, Redis |
| MLOps Tools | MLflow, Git, Docker, S3, GitHub Actions |
| Model Serving | Custom inference runners for SDXL, LLaMA |
| Queueing System | Celery, RabbitMQ |
| Deployment Infra | AWS EC2, ECR, VPC, CloudWatch |
| Security | Token-based auth, Role-based ACL, TLS |
Results
| Metric | Outcome |
|---|---|
| Inference performance | Improved by 3x with async processing |
| Model update cycle | Reduced from 3 days to 6 hours |
| Security & data compliance | Aligned with ISO 27001 guidelines |
| Deployment time (new releases) | ~30 minutes with automated CI/CD |
Collaboration & Delivery
We handed over the system to the client’s product team with:
- Comprehensive technical documentation
- Deployment automation scripts
- Training sessions on MLOps best practices
- After-sales support for tuning and model scaling as needed
The product company successfully integrated our backend into their full system, delivering a game-changing solution for directors and producers to visualize and plan movies more efficiently.
Why DEIENAMI?
- Proven expertise in backend engineering for AI
- Experience deploying LLMs and generative models in production
- Strong MLOps practices to maintain quality and reproducibility
- Commitment to secure, scalable, and industry-compliant development
Ready to Build High-Performance AI Infrastructure?
Let DEIENAMI be your backend and MLOps partner for intelligent products—from creative industries to industrial AI systems.
Let’s talk. Your innovation needs the right foundation.
