Our Case Studies.

Sales Forecast

The Process

Using Machine Learning data, we've used different data sources to predict the demand for different SKU's across different regions.

The Objectives

Producing a timely forecast that can accurate predict demand and support decisions regarding shelf time and JIT(just-in-time) logistics.

The Results

Achieved an improvement of 10 percentage points in actual forecast accuracy.

Retail fridge
Retail store

Price Optimization

The Process

Using data from past prices and product characteristics, we were able to produce price and promotion mix recommendations every two months.

The Objectives

Our customer wanted to set the optimum price to balance their sales and profitability, dynamically. Each week, several factors influence the optimum price point of their SKU's and it was important that each model was able to find explainable patterns for each product.

The Results

Achieved stability between sales and profitability for different products across different regions. This model enabled the customer to build better price-promotion mixes for more than 100 different stores.

Price Optimization

The Process

Using data from past prices and product characteristics, we were able to produce price and promotion mix recommendations every two months.

The Objectives

Our customer wanted to set the optimum price to balance their sales and profitability, dynamically. Each week, several factors influence the optimum price point of their SKU's and it was important that each model was able to find explainable patterns for each product.

The Results

Achieved stability between sales and profitability for different products across different regions. This model enabled the customer to build better price-promotion mixes for more than 100 different stores.

Product Launch & Acceptance

The Process

Using data science models, we were able to find factors that justified the success of a specific product in a group of stores.

The Objectives

Finding stores that were more receptive to new products.

The Results

Improved targeted marketing and sales team focus to stores where there is a higher propensity for new products.

Discover how to apply Data & AI in your business.

Discover how to apply Data & AI in your business.

Recommendation System for New Products

The Process

We've built several recommendation engines to cross-sell new products to Telco Customers. Our Recommendation Engines took into account data from customers and products and produced "Next Best Offers" to current customers during touchpoints.

The Objectives

Maximize lift of cross-sell offers and reduce number of irrelevant offers to customers.

The Results

Achieved targeted improvement of lift to new and existing customers.

Case Study #02

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Customer Segmentation

The Process

We've built several customer segmentations for our telco customers. These segmentations enable our customers to better predict which customers are more likely to acquire value-added services or which ones are more likely to use the services without paying.

The Objectives

Building clear customer segmentations that can be fed to CRM systems and support operators on their decision making process.

The Results

Achieved improvement of LTV of segmented customers and reduced percentage of portfolio in default by avoiding the recommendation of new products to customers with high likelihood of default.

Telco #03

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Discover how to apply Data & AI in your business.

Coming soon...

Discover how to apply Data & AI in your business.

Coming soon...

Computer Vision for Medical Imaging

The Process

We've used models to classify images and obtain a consensus map based on several "true labels" from different doctors. The model we've built for our customer uses components of computer vision and clustering analysis to target specific areas of an x-ray that should be the ground truth for diagnosis.

The Objectives

Building a consensus map to help doctors make sense of different classifications.

The Results

Our model achieved an accuracy of over 90%, enabling doctors to have a better precision when building their diagnostic based on the image.

Case Study #02

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Operational Efficiency Project

The Process

Our customer needed to extract hundreds of text data from documents to label those documents according to several categories. We've solved this problem using text classification algorithms that are able to use both structured and unstructured data to predict the label of the document.

The Objectives

Reducing the manual input by users when classifying new documents. This process took roughly 10% of the team's effort and automating it was crucial to enable the team to focus on value-added tasks.

The Results

Achieved optimization of hours used by the organization in value-added tasks by automating a huge chunk of the manual labelling process.

Pharma #03

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Discover how to apply Data & AI in your business.

Coming soon...

Data Lake Setup and Management

The Process

Creating a Data Lake that supports a Public Sector Agency across all departments and gives timely information about different sectors, industries and companies.

The Objectives

Introducing contextual information, external to the organization, into the decision process of the agency.

The Results

Enabled several departments to use information that was expensive or acessible only through complicated processes, improving the quality and speed of virtually every decision in the agency.

Case Study #02

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Knowledge Database and Relevant Search

The Process

Our Customer needed to integrate scattered information throughout different documents in their decision process. We've built a graph database that ingests documents in all types of formats (spreadsheet, word and pdf), mapping entities and building their relationships using unstructured data.

The Objectives

Extract information from millions of documents to structure the data in a relational graph database that helps all departments making better decisions.

The Results

Our customer is now able to incorporate information that was scattered in files stored in deep folders. This information is crucial to support employees on their day to day basis, giving them visibility and insight on entities relationships.

Public Sector #03

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Discover how to apply Data & AI in your business.

Coming soon...

Banking #01

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Case Study #02

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Banking #02

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Banking #03

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Discover how to apply Data & AI in your business.

Coming soon...

Computer Vision for Utilities

The Process

Our customer needed to segment several elements using noisy satellite images to obtain relevant information that could be used when setting up new installations across the network. We've developed state-of-art models to identify several objects in a satellite image and build multi-layer image identification processes.

The Objectives

Identifying different objects in Satellite images to support infrastructure deployment decisions.

The Results

Our Customer is now able to save hundreds of hours of manual work by receiving automatic input from satellite images. Our model is able to detect relevant information in the images and calculate available space to deploy infrastructure on roofs or landscape.

Case Study #02

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Predictive Maintenance

The Process

Using a machine learning model, we've deployed an infrastructure able to predict, in real time, the probability of failure of an essential component of several machines.

The Objectives

If the component had some type of problem, the whole operation would stop, costing hundreds of dollars to the company. The objective from our customer was to have timely and precise predictions of when that component may fail in a specific time-window.

The Results

Our customer is now able to accurately predict when the component will fail, in real time. We were responsible for building the API that communicates with the machine, receiving data from several endpoints and sources. We've managed to make this data engineering process take few minutes so that the warning is timely activated and the company can act accordingly.

Industrial #03

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Discover how to apply Data & AI in your business.

Coming soon...

Routing Optimization

The Process

We've helped a startup setting up a routing optimization system from top-to-bottom, building the backbone for the organization processes. These processes range from data collecting to making routing decisions that impact the daily operations of the startup.

The Objectives

Building a scalable tech environment that supports the daily operations of US Start-up company.

The Results

The company is able to scale their data processes with no restrictions on size or complexity. Additionally, the optimization algorithm for routing enabled the company to expand across the US.

Case Study #02

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Interim CTO - SaaS Company

The Process

We act as interim CTO's for several startups, providing advisory, developing crucial tech processes and helping with hiring.

The Objectives

Supporting Start-Ups that don't have the resources to hire CTOs or are looking for tech advisory on their early stage phase.

The Results

We've helped a couple of Start-up companies scale by advising them on how to set up their data engineering infrastructure.

SaaS #03

The Process

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response.

The Objectives

We advocate for a delicate balancing act between quick wins and infrastructure build. This balance is achieved with a strategic mix of careful planning and agile response. Your goal should be to make the most out of Data & AI

The Results

We advocate for a delicate balancing act between quick wins and infrastructure build.

Discover how to apply Data & AI in your business.

Coming soon...

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