Success stories
INSURANCE COMPANY
What if... an insurance company could improve the way data is consumed and give more value to users and business.
SERVICE
Senior Data Engineer
INDUSTRY
Insurance
TECHNOLOGY
SQL, Azure, Snowflake, Airflow,
DBT, CI/CD, AWS.
The client
Vault is an insurance company that
specializes in providing coverage for
high-value assets and specialty risks.
Benefits
Improve the way data is consumed and give
more value to users and business
We deliver high-quality services, exemplified by our successful migration of the data layer from
traditional on-premises solutions to cuttingedge technologies like Snowflake, Azure, and Airflow.
Challenge
Move from different vendors and outdated technologies,
consolidate the entire process into a single, robust system with
enhanced security and scalability, retaining all necessary business
rules while seamlessly handling various types of data—including
flat files, JSON, and relational databases—and ensuring high-quality
data through comprehensive transformations.
Solutions
We enhanced the data consumption process, delivering greater
value to both users and the business. Leveraging cutting-edge
technologies such as Snowflake, Airflow, and Azure, along with
robust practices in Data Governance, Data Care, and Data Cleanup,
we’ve developed a streamlined process that ensures compliance
with all mandatory regulations.
Our selection of these tools was strategic, considering their costeffectiveness and some being freely available, as part of our
comprehensive global implementation strategy. As top-tier
applications, they have significantly elevated the overall delivery of
each component.
Nexts Steps
After more than 1 year the project was
extended without any modification in the team
structure.
The plan is to introduce AI and ML in order
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.