Using Machine Learning data, we've used different data sources to predict the demand for different SKU's across different regions.
Producing a timely forecast that can accurate predict demand and support decisions regarding shelf time and JIT(just-in-time) logistics.
Achieved an improvement of 10 percentage points in actual forecast accuracy.
Using data from past prices and product characteristics, we were able to produce price and promotion mix recommendations every two months.
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.
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.
Using data from past prices and product characteristics, we were able to produce price and promotion mix recommendations every two months.
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.
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.
Using data science models, we were able to find factors that justified the success of a specific product in a group of stores.
Finding stores that were more receptive to new products.
Improved targeted marketing and sales team focus to stores where there is a higher propensity for new products.
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.
Maximize lift of cross-sell offers and reduce number of irrelevant offers to customers.
Achieved targeted improvement of lift to new and existing customers.
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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
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.
Building clear customer segmentations that can be fed to CRM systems and support operators on their decision making process.
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.
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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
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.
Building a consensus map to help doctors make sense of different classifications.
Our model achieved an accuracy of over 90%, enabling doctors to have a better precision when building their diagnostic based on the image.
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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
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.
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.
Achieved optimization of hours used by the organization in value-added tasks by automating a huge chunk of the manual labelling 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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
Creating a Data Lake that supports a Public Sector Agency across all departments and gives timely information about different sectors, industries and companies.
Introducing contextual information, external to the organization, into the decision process of the agency.
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.
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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
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.
Extract information from millions of documents to structure the data in a relational graph database that helps all departments making better decisions.
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.
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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
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.
Identifying different objects in Satellite images to support infrastructure deployment decisions.
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.
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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
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.
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.
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.
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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
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.
Building a scalable tech environment that supports the daily operations of US Start-up company.
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.
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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
We act as interim CTO's for several startups, providing advisory, developing crucial tech processes and helping with hiring.
Supporting Start-Ups that don't have the resources to hire CTOs or are looking for tech advisory on their early stage phase.
We've helped a couple of Start-up companies scale by advising them on how to set up their data engineering infrastructure.
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.
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
We advocate for a delicate balancing act between quick wins and infrastructure build.
