we help Artificial Intelligence to understand
the complex world of eCommerce

The Problem

  • Preamble
    Over the last 2 years, we (and some of our partners) lost multiple voiceCommerce projects due to the complexity, low-quality, and incoherency of the eCommerce data. The vast majority of eCommerce owners have structured their catalogs/products in their own custom way and some rely heavily on images to transmit important information about their products.
  • The Analysis 
    Although the eCommerce sites are all unique and have a custom technology stack (different platforms + different plugins) the biggest issue we have faced is the heterogeneous catalog/product data structure followed by the low-quality data issues.
  • The Barrier
    The high heterogeneity of the data, blocks solutions re-usage. The cataloging errors and the low-quality data need to be corrected or else will generate quality issues. The impossibility of scaling and the data correction work, exponentially increase costs. These high costs are prohibitive for most companies.
  • The Problem
    The eCommerce data issues increase costs that scare clients. How can we lower the costs, lower complexity, and take advantage of scalability?

The Problem - Demonstration

We analyzed the top 30 Fashion websites looking for data issues on the most common product: T-shirts

100% of the sites were missing one or more
references to the basic t-shirts attributes.
complexity - low-quality data - scalability issues
80,7% of the sites had wrong items on the search results (cataloging errors).
low-quality data - scalability issues
76,9% of the sites had products that didn’t appear when we applied the search filters
low-quality data

The Problem - Demonstration


As we can see, the eCommerce data issues are common even on the top 30 websites of the most popular eCommerce industry.

From our experience, these are cross-industry issues. From our point of view,  they are blocking the development of chatbots, digital assistants, IVR’s. 

Our Solution

Creation of structured data schemas for products

Our Solution

Structured data is data that is organized in a way that a computer can easily read and understand.
At its core, structured data is a system of pairing a name with a value. We want to go further. 

We want to build product blueprints with all the product’s options, properties, and variables.

Our Solution Vectors

  • Less Complexity
    By having a map (structured data schema) of all the properties and options of a product, we can merge and organize all data obtaining a cleaner, more reliable, and faster way to analyze and process data.
  • Better quality
    With the blueprint in hand, we can easily discover and correct missing data and data inconsistencies.
    This will allow faster implementation of AI-based solutions and, by example, better search results
  • Scalability
    By using the structured data schemas the eCommerce data will become more tangible and will allow the use of cross websites solutions. This will allow the scalability of AI solutions.
  • Lower Costs
    With structured data (less complex and with higher quality) the AI solutions providers can take advantage of the scalability of their processes and lower significantly their prices.

Our Solution - Benefits

Example of benefits in the voice sector
digital assistants, chatbots, IVR’s
  • Automatic Dialog Generation

    By having access to a structured data schema, it is simple to generate product descriptions and answers to questions

  • Better search experience

    In the voice space, the users tend to ignore the limitations of the search filters. By having a structured data schema, is simpler to understand the user request, allowing more correct answers than the typical “I don’t know that one” answer

  • Faster Development and Maintenance

    By using the blueprints it is faster and simpler to construct/reuse an agent.  The maintenance work will be lower because there is no need for custom development.

Our Solution - How we do it


we identify the top store and blogs of the sector 

AI Process

we build context-specific models and feed them the curated information


we use a mix GTPs, vectorization, and clustering to generate structured data schemas

About is focused on solving one of the biggest bottlenecks that affect the eCommerce evolution.

Our work can be used widely in the eCommerce ecosystem. Structured data schemas will allow from better product recommendations up to the development of self-learning chatbots or digital assistants. is a Miguel Costa project.