Success stories
AI-DRIVEN MUSIC PRODUCTION COMPANY
What if… What if stopping was the fastest way to scale? How a strategic stop-and-pivot turned experimental R&D into a production-ready GenAI asset.
SERVICE
AI Engineer
INDUSTRY
Generative AI · Media Tech · FinTech
TECHNOLOGY
Transformer, EnCodec, AWS
Sagemaker
The client
An AI-Driven Music Production
Company focused on building
proprietary, copyright-free media
assets powered by Generative AI.
Benefits
- Prevented months of sunk R&D costs by stopping investment in a non-scalable GenAI architecture
- 100% strategic pivot that unlocked a clear path from experimentation to production
- Up to 50% cloud cost optimization through FinOps-driven infrastructure redesign
- Scalable roadmap secured from a 300M pilot model to 3.3B production-grade models
- Transformed GenAI R&D into a long-term business asset ready for commercial growth
Challenge
The Client wanted to build a commercial-grade Generative AI platform for music production, capable of delivering high-quality, copyright-free audio for film and media. However, the initial architecture failed to scale in quality, duration, and cost efficiency.
Continuing down the same path risked turning experimentation into sunk investment, making it critical to reassess the foundation before scaling further.
Solutions
- Performed a deep technical audit to validate architectural viability and business risk
- Led a decisive “Stop & Pivot” to prevent further investment in a non-scalable approach
- Re-architected the system using Transformer-based MusicGen with direct audio modeling
- Designed a scalable production roadmap from 300M to 3.3B parameter models
- Built a cloud-optimized, production-ready pipeline with cost control and long-term data strategy
What the client says
«We at Fulton Investors Group had the pleasure of bringing on Mindtech to help push our existing build to goals we wanted to achieve. Working with Rodolfo M., Gamaliel G. and his team was a productive, positive experience.»
— Robbie, Fulton Investors Group
Benefits
Improving product descriptions on the online store to enhance the customer shopping experience and increase sales.
Substantial reduce the time needed to input
product attributes by leveraging cutting-edge
technologies.
Challenge
The process of completing product attributes was cumbersome and time-consuming. It also resulted in incomplete or error filled product descriptions, impacting the user’s shopping experience.
Solution
Using computer vision, we generated product descriptions which contain information about attributes such as color, material, dimensions, size, etc. This information is published on the ecommerce along with the product images.
The project is based on the use of AI generative models, specifically Gemini Pro Vision, to achieve three main objectives:
The project is based on the use of AI generative models, specifically Gemini Pro Vision, to achieve three main objectives:
- Create new product descriptions by extracting attributes from product images.
- Enrich these product descriptions using previous information from other products
- Perform a retroactive process to update the product descriptions when new attributes are found when compared to embeddings generated by Gecko (Google model), i.e., add this information to existing similar products.
Google Cloud Platform (GCP) was chosen for its robust cloud computing capabilities and built-in machine learning services, providing a reliable infrastructure that is highly scalable for heavy computational tasks.
Python was selected as a high-level, interpreted language with an abundance of AI-related libraries, allowing for easy readability, quick prototyping, and sophisticated AI and machine learning projects.
Docker was utilized to automate deployment and scaling of applications, ensuring the consistent operation of the AI model across various environments.
The Gemini Pro Vision Model and Text Embedding Gecko Model were used to offer specialized functionalities in computer vision and natural language processing respectively, providing capabilities such as sophisticated analysis of visual content and representing text data as normalized numerical vectors.