A North American startup
Retail & Ecommerce
R&D, IT consulting, ML
Java, Spring Boot, Spring Data, Spring Kafka, Stripe SDK, AWS SDK, Twilio SDK, React


While it gets harder for small and mid-sized fashion retailers to compete with larger brands and online tycoons, our client came up with an idea of a BI (business intelligence) platform with machine learning capabilities to help smaller luxury brands improve their manufacturing and buying strategies based on data. With initial funding secured, the only thing the client needed was a trusted IT consultant with expertise in machine learning and BI development that could help them carry out the discovery phase, validating the feasibility of their product vision and perfecting it further.

Specifically, we were commissioned to:
Validate BI platform viability
Research data sources for training ML models
Think out the logic behind ML models and choose an appropriate ML algorithm
Nail down functional requirements
Put together compliance requirements
Design a BI platform architecture
Define MVP scope and timeline, draw estimates
Determine MVP development priorities
Design a BI platform testing strategy
Prepare the needed deliverables for the client to secure the next round of investment


The BI platform would use open-source data from online stores and marketplaces about the popularity of certain styles in places of sales interest to generate predictions for designers, merchandisers, and buyers. The predictions would allow fashion brands to adjust production and sales strategies based on the knowledge about which items and styles are likely to be in high demand next season. Validating product concept
ITRex’s business analyst kicked the discovery stage off by investigating the sources of data for powering the business intelligence platform. The research centered around open-access data from online sales platforms and marketplaces, for example, Shopify, Farfetch, etc. The data would give insight about products, sales, and factors that influence sales.
The business analyst working together with an ML engineer and a solution architect nailed down the logic behind the ML model. Based on the researched data, they could extrapolate demand for certain products or styles at certain places of sales interest during a particular season for a particular customer category. The extrapolation logic was proven a success following several tests.
As a result of the activities above, we confirmed the viability of the customer’s vision, proving that there is enough open-access data to build a predictive model with the algorithm of choice.
Crafting functional solution
The discovery team described and visualized the BI platform's functional blocks, including back office, billing, reporting, legal and compliance, and others. They also identified functional priorities for developing a minimum viable product and a minimum valuable product.
We followed by nailing down an exhaustive functional requirements document.
As the BI platform relies on complex data flows and comprises several functional blocks, we designed a flexible architecture. The architecture boasted built-in flexibility that allowed expanding the number of data sources to power the prediction engine.
As the customer relied on open-source data and was unfamiliar with data compliance requirements, we put together data collection and storage recommendations to make sure the data used for machine learning was handled securely.
We finished the discovery stage by crafting a thorough product testing strategy.
BI platform with ML capabilities
business intelligence platform


With the business intelligence platform’s vision validated, the client felt confident to carry on developing their product.
With all the discovery-related deliverables on hand, including a functional requirements document, a technical vision document, a solution architecture, project estimates, and a test strategy, the client has a very high chance of winning a new round of funding to continue product development.

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