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Reimagine the future of

Logistics.

With the exponential growth of data, there’s a huge underlying potential to apply AI techniques and tools to transform data into actionable insights.

There’s probably more in your data than what meets the eye.
Talk with us

"Managing the supply chain effectively and meeting customer demands in the logistics industry is a big data challenge. Gathering and consolidating massive amounts of data from various sources into a cohesive logistics system is where companies can gain a competitive advantage.”

Ivo Bernardo, Partner

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Set your way into the future.

6

Automated Logistic Systems Deployed

+1000

Vehicles Addressed

Discover how to apply Data & AI in your business.

Discover how to apply Data & AI in your business.

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Case Studies

Government
Governments can benefit from AI tools because of the potential they can bring to various aspects of governance, such as public safety, healthcare, transportation, and social welfare.
Learn more

Case Study

Operations
Large Language

Large Language Model Implementation

The project involved creating a Large Language Model implementation (using GPT 3.5) specifically designed for a Government Agency. This model will serve as an AI chatbot that can provide information to Government officials regarding internal knowledge as well as interact with citizens, offering them assistance and support. The ultimate goal of the project is to create an effective communication tool that improves efficiency and customer satisfaction while reducing workload for the Government Agency.
Read more

Finance & Management

data lake

Segmentation
Model

We’ve built a segmentation model to classify companies into different groups according to several dimensions such as Financial Health, Innovation, Potential, Company X-Ray, and Exports.

Finance & Management

data lake

Recommendation
Engine

Our recommendation system enabled our customer to provide a better service to their stakeholders using advanced machine learning techniques. The goal of the recommendation engine was to provide recommended markets for companies to export based on their sector and historical information.

Marketing & Sales

data lake

Exports
Forecasting

The Exports Forecast Model is a tree-based approach that builds a probability of a company being able to grow their exports in the next couple of years, helping government entities shortlist companies with a higher probability of growing.

Big Data

Government Data Hub

Our Azure Synapse and Airflow-based Data Lake setup infrastructure was the solution that took our customer data management to the next level. With a highly customizable data management system, the government agency gained access to advanced data analytics and insights that helped them make data-driven decisions, ensuring a best outcome and service for their stakeholders.

Finance & Management

auto labelling

Auto Labelling
Invoices

The Auto-Labeling Invoices project used a multi-class classification approach on tabular data using lines from invoices, to automatically categorize your expenses and streamline the accounting process. This enabled the salespeople  to spend more time to focus on sales, boosting their productivity and driving more revenue to the pharmaceutical business.

Big Data & Cloud Management

Modern Data

Modern Data
Infrastructure
Migration

The modern data infrastructure project aimed to develop our customer data infrastructure and ecosystem by dropping information silos and allowing cross-department data usage through a self-service data culture, using five steps: Extraction, Ingestion, Transformation, Delivery, and Quality.This project kickstarted the usage of cloud infrastructure in our Pharma customer, an important milestone for the company.

Industrial Operations

Routing Matching

The Routing Matching was a data engineering initiative that aimed to help a logistic startup company. The project harvested logistics data from an API or web scraping and delivered recommendations of loads to purchase. The system was able to find good loads with trucks that were on the road in almost real-time and match them with cargo in the spot market. This enabled the company to secure loads that were 20% above the current market rate, giving them an advantage in terms of revenue.

Marketing and Sales

Cross Sell Recommender Engine

The Cross Sell Recommendation Engine is a project that involved building a recommendation engine for a major telecommunications company in Iberia. The company was struggling to identify which customers to contact with new offers for premium TV channels, leading to outbound calls that resulted in complaints from clients. The project aimed to predict which customers were more likely to buy the new products by consuming large amounts of past customer data to build a gradient boosting model that incorporated client characteristics, current customer packs, and TV usage data.

Big Data & Cloud Management

Cloud Migration

We helped our customer migrate part of their data to Google Cloud Platform (GCP), kickstarting a cloud revolution at the company. One of the key challenges faced during the transition is maintaining the quality of data while ensuring compliance with regulatory requirements.To address this, the team wanted to build an architecture and solution that was able to address the topics of data quality, lineage, discovery and privacy.

Training

Advanced Learning
Journey

Our Learning Paths project played a crucial role in equipping our customer's employees with the essential foundational skills needed for data science and data engineering. By identifying these crucial skills and their dependencies, researching and evaluating courses that covered them, and designing proficiency tests, we were able to train over 20 individuals in the company. Today, our Learning Paths serve as the go-to blueprint for onboarding new hires, setting the standard for every new Data hire the company brings on board. Our project has contributed significantly to building a skilled workforce, ensuring the company has the talent it needs to thrive in today's data-driven business landscape.

Marketing and Sales

Brand Penetration

Our data-driven sales project aimed at increasing a specific brand penetration. The team used a nearest neighbor approach and K-means clustering to rank points of sale that will likely have more demand for a premium brand, resulting in an extra 30% sales in the target group compared to the control group.

Marketing and Sales

Shelf Space
Optimization

Shelf Space Optimization is data-driven sales project that aimed to understand which brands can be replaced on the shelf for new products using a simulation model. The team used a Random Forest Simulation Model to increase the average sales and profitability of each shelf in the supermarket, maximizing expected revenue by using data. The goal was to choose the most appropriate SKUs that minimized the risk of cannibalization and achieve higher ROI.

Marketing and Sales

Promo
Optimization

The goal of this project was to optimize promotions for specific SKUs by determining the ideal promotion percentage and timing to maximize sales while maintaining margins. The team used a boosting model that analyzed past prices and sales data to simulate a grid of prices for the future. These simulated prices were then used to predict a sellout and the chosen promotion was the one that achieved an optimal balance between market share and margin.

Marketing and Sales

Assortment
Optimization

This project aimed to build an assortment optimization tool that provided guidance to the commercial team on which SKUs were appropriate for each client. The team used Maximum Likelihood Estimation per Cluster of Clients to determine which SKUs were most suitable for each customer group. The result was a guidance framework for assortment recommendations that improved sales performance. The team worked with 100GB of data and used DataBricks FileSystem to extract and pre-process data, perform K-Means clustering, and calculate Maximum Likelihood Estimation.

Marketing and Sales

Raw Materials Pricing Model

We helped our customer developing a pricing model using artificial intelligence to predict the best price for a Silicon Metal / FerroSilicon product.  The model uses past proposal data to determine the probability of winning new proposals based on price. The aim was to create a pricing governance system based on the balance between expected revenue and margin.

Marketing and Sales

Cross-Sell Project

Our customer developed a model to predict which customers are likely to buy new financial products, using a Tree-Based Classification Model. This helped them rank customers and increase sales commissions on new products by 7.8%, particularly by mapping customers with higher likelihood of buying other products from their portfolio.

Government
Governments can benefit from AI tools because of the potential they can bring to various aspects of governance, such as public safety, healthcare, transportation, and social welfare.
Learn more

Case Study

Operations
Large Language

Large Language Model Implementation

The project involved creating a Large Language Model implementation (using GPT 3.5) specifically designed for a Government Agency. This model will serve as an AI chatbot that can provide information to Government officials regarding internal knowledge as well as interact with citizens, offering them assistance and support. The ultimate goal of the project is to create an effective communication tool that improves efficiency and customer satisfaction while reducing workload for the Government Agency.
Read more

Finance & Management

data lake

Segmentation
Model

We’ve built a segmentation model to classify companies into different groups according to several dimensions such as Financial Health, Innovation, Potential, Company X-Ray, and Exports.

Finance & Management

data lake

Recommendation
Engine

Our recommendation system enabled our customer to provide a better service to their stakeholders using advanced machine learning techniques. The goal of the recommendation engine was to provide recommended markets for companies to export based on their sector and historical information.

Marketing & Sales

data lake

Exports
Forecasting

The Exports Forecast Model is a tree-based approach that builds a probability of a company being able to grow their exports in the next couple of years, helping government entities shortlist companies with a higher probability of growing.

Big Data

Government Data Hub

Our Azure Synapse and Airflow-based Data Lake setup infrastructure was the solution that took our customer data management to the next level. With a highly customizable data management system, the government agency gained access to advanced data analytics and insights that helped them make data-driven decisions, ensuring a best outcome and service for their stakeholders.

Pharma
Using AI in pharma can accelerate drug discovery, improve patient outcomes, and increase operational efficiency in the industry.
Learn more

Case Study

Computer Vision
Cancer cell

Cancer Cell Consensus Map

The project focused on creating a segmentation model that will be used to construct a Cancer Cell Consensus Map for a pharmaceutical customer. This involves analyzing medical imaging data to identify and segment cancer cells, which can then be used to create a comprehensive map of the cells' characteristics and behavior. The resulting map can help inform medical professionals in diagnosing the disease faster, potentially leading to more effective and personalized treatment plans for patients.
Read more

Finance & Management

auto labelling

Auto Labelling
Invoices

The Auto-Labeling Invoices project used a multi-class classification approach on tabular data using lines from invoices, to automatically categorize your expenses and streamline the accounting process. This enabled the salespeople  to spend more time to focus on sales, boosting their productivity and driving more revenue to the pharmaceutical business.

Big Data & Cloud Management

Modern Data

Modern Data
Infrastructure
Migration

The modern data infrastructure project aimed to develop our customer data infrastructure and ecosystem by dropping information silos and allowing cross-department data usage through a self-service data culture, using five steps: Extraction, Ingestion, Transformation, Delivery, and Quality.This project kickstarted the usage of cloud infrastructure in our Pharma customer, an important milestone for the company.

Logistics
AI has the potential to transform the logistics industry by enabling companies to analyze large amounts of data, identify patterns, and make accurate predictions about demand, inventory levels, and transportation routes.
Learn more

Case Study

Industrial Operations
routing optimization

Routing Optimization

The project aimed to optimize truck routing by utilizing machine learning and a routing engine. This involves integrating various restrictions, locations and users into the routing algorithm, in order to minimize travel time, distance and cost. By analyzing historical data and employing machine learning techniques, the routing engine can continually improve its performance, taking into account changing conditions and factors. The end result of our project was a highly efficient and effective routing system that can save time and resources, and ultimately improve users day-to-day.
Read more

Industrial Operations

Routing Matching

The Routing Matching was a data engineering initiative that aimed to help a logistic startup company. The project harvested logistics data from an API or web scraping and delivered recommendations of loads to purchase. The system was able to find good loads with trucks that were on the road in almost real-time and match them with cargo in the spot market. This enabled the company to secure loads that were 20% above the current market rate, giving them an advantage in terms of revenue.

Telecommunications
AI can help telco companies monitor network performance, detect anomalies and predict potential outages, allowing them to proactively fix issues and improve service availability for customers. 
Learn more

Case Study

Marketing & Sales

Call Center Relapses

The team focused on achieving the best possible precision for identifying customers who were likely to relapse on technical calls, using a very large dataset containing daily interactions with customers. They employed a tree-based model on data extracted from operational databases to produce a list of telephone numbers for proactive calling, which aimed to address technical issues and prevent relapses using data science to increase customer satisfaction.
Read more

Marketing and Sales

Cross Sell Recommender Engine

The Cross Sell Recommendation Engine is a project that involved building a recommendation engine for a major telecommunications company in Iberia. The company was struggling to identify which customers to contact with new offers for premium TV channels, leading to outbound calls that resulted in complaints from clients. The project aimed to predict which customers were more likely to buy the new products by consuming large amounts of past customer data to build a gradient boosting model that incorporated client characteristics, current customer packs, and TV usage data.

Big Data & Cloud Management

Cloud Migration

We helped our customer migrate part of their data to Google Cloud Platform (GCP), kickstarting a cloud revolution at the company. One of the key challenges faced during the transition is maintaining the quality of data while ensuring compliance with regulatory requirements.To address this, the team wanted to build an architecture and solution that was able to address the topics of data quality, lineage, discovery and privacy.

Training

Advanced Learning
Journey

Our Learning Paths project played a crucial role in equipping our customer's employees with the essential foundational skills needed for data science and data engineering. By identifying these crucial skills and their dependencies, researching and evaluating courses that covered them, and designing proficiency tests, we were able to train over 20 individuals in the company. Today, our Learning Paths serve as the go-to blueprint for onboarding new hires, setting the standard for every new Data hire the company brings on board.

FMCG
AI can help FMCG companies improve supply chain efficiency, optimize inventory management, and enhance the customer experience through personalized recommendations and targeted marketing.
Learn more

Case Study

Marketing & Sales
retail forecast

Retail Forecasting Model

The DareData forecasting project revolutionized a large corporation Forecasting Models by putting data at the centre of the process. By using cutting-edge machine learning technology, the team was able to accurately predict beer and water sales across different SKUs and regions, improving baseline forecasting accuracy. This breakthrough allowed the team to gain a detailed view of SKUs sold per client, helping them to make better sales adjustments. With hundreds of parallel-running forecasting models and the help of Azure Blob and Snowflake, the team was able to build a forecasting pipeline end-to-end that maximized accuracy and minimized the time spent on manual forecasting.
Read more

Marketing and Sales

Brand Penetration

Our data-driven sales project aimed at increasing a specific brand penetration. The team used a nearest neighbor approach and K-means clustering to rank points of sale that will likely have more demand for a premium brand, resulting in an extra 30% sales in the target group compared to the control group.

Marketing and Sales

Shelf Space
Optimization

Shelf Space Optimization is data-driven sales project that aimed to understand which brands can be replaced on the shelf for new products using a simulation model. The team used a Random Forest Simulation Model to increase the average sales and profitability of each shelf in the supermarket, maximizing expected revenue by using data. The goal was to choose the most appropriate SKUs that minimized the risk of cannibalization and achieve higher ROI.

Marketing and Sales

Promo
Optimization

The goal of this project was to optimize promotions for specific SKUs by determining the ideal promotion percentage and timing to maximize sales while maintaining margins. The team used a boosting model that analyzed past prices and sales data to simulate a grid of prices for the future. These simulated prices were then used to predict a sellout and the chosen promotion was the one that achieved an optimal balance between market share and margin.

Marketing and Sales

Assortment
Optimization

This project aimed to build an assortment optimization tool that provided guidance to the commercial team on which SKUs were appropriate for each client. The team used Maximum Likelihood Estimation per Cluster of Clients to determine which SKUs were most suitable for each customer group. The result was a guidance framework for assortment recommendations that improved sales performance. The team worked with 100GB of data and used DataBricks FileSystem to extract and pre-process data, perform K-Means clustering, and calculate Maximum Likelihood Estimation.

Utilities
Using AI in utilities can improve energy efficiency, optimize resource allocation, and enhance customer service by enabling predictive maintenance and real-time monitoring of equipment and infrastructure.
Learn more

Utilities

roof

Roof Detection Algorithm

We’ve built a segmentation model to classify companies into different groups according to several dimensions such as Financial Health, Innovation, Potential, Company X-Ray, and Exports.

Case Study

Computer Vision
roof detection

Roof Detection Model

The Solar Potential POC project was a new way to approach a problem that our client was facing. By leveraging the power of machine learning, the project was able to calculate the available space for solar panels on a roof based on satellite images, reducing unnecessary visits to homes by up to 20%. Using U-NET Convolutional Network architectures to detect obstructions, south orientation, and roof detection, the team achieved an impressive average IOU score of 0.7. This breakthrough has given technicians some help on where to install solar panels without encountering issues with the roof's typology, while giving the marketing team valuable insights into which homes have the greatest potential for solar energy.
Read more
Industrial
In industrial settings, AI can be used to monitor and analyze data from machines, predicting when maintenance is required and identifying the root causes of equipment failure before it happens, thereby reducing downtime and maintenance costs. 
Learn more

Case Study

Big Data Organization
data lake

Data Lake Infrastructure Setup and Management

This data engineering cutting-edge project aggregates multiple sources of data, from maps to video, and serves it through an API for advanced drone control.With features like weather forecasting and real-time integration, this project helps our customer make strategic, data-driven decisions to optimize their operations when it comes to several vehicles. The project also aims to drop information silos, enabling cross-departmental usage of data to maximize efficiency. Led by DareData engineers, this initiative is projected to generate a massive 5TB of data.
Read more

Marketing and Sales

Raw Materials Pricing Model

We helped our customer developing a pricing model using artificial intelligence to predict the best price for a Silicon Metal / FerroSilicon product.  The model uses past proposal data to determine the probability of winning new proposals based on price. The aim was to create a pricing governance system based on the balance between expected revenue and margin.

Banking
AI can enable personalized financial recommendations, fraud detection, and risk management in banking, improving customer experience and reducing operational risks.
Learn more

Case Study

Marketing and Sales
customer churn

Customer Churn

By analyzing a vast array of data, including transaction history, user behavior, and demographics, our solution accurately predicted customer churn, enabling our client to take proactive measures to retain customers before it's too late. Particularly in the banking sector, where switching costs are high, machine learning projects are essential to improve customer retention, and ultimately increase your bottom line.
Read more

Marketing and Sales

Cross-Sell Project

Our customer developed a model to predict which customers are likely to buy new financial products, using a Tree-Based Classification Model. This helped them rank customers and increase sales commissions on new products by 7.8%, particularly by mapping customers with higher likelihood of buying other products from their portfolio.

Client Success