Technology for business solutions.

Cloud Governance

Security, cost, reliability, operations and performance management. Public cloud adoption requires a set of components, practices and policies for proper deployment and subsequent scalability.

Data Governance

Data governance is about more than control, it’s about providing confidence in data, analytics and decision making, taking the enterprise from a reactive to a proactive state, to being aware of the value of its data.

Data Modernization

Migrations to cloud-native platforms, tailored digital products to solve business challenges, data lakes at the center of data strategy, decentralized models of data architecture, industrialization of machine learning for use at scale….


A Data Product is the sum of data, automated cloud infrastructure and algorithms. Our data products are designed to deliver measurable business value, integrated into business processes.

Improved sales conversion

Improved sales conversion

Smart segmentation and customization of customer quotations.

Marketing campaign optimization

Marketing campaign optimization

Improvement and optimization of return on investment in marketing and attribution models.

Customer service process optimization

Customer service process optimization

Smart client segmentation and smart routing to conversational assistant or to agent.

Call analysis

Call analysis

Natural Language Understanding to detect feelings, entities and intentions in calls to the call center to improve the quality of service and client interactions.

Email analysis and classification

Email analysis and classification

Both e-mail text and the attached documentation with unstructured information.

Data extraction from invoices and contracts

Data extraction from invoices and contracts

Natural Language Understanding to detect entities in documents and perform automatic processing of the information.

Price optimization and customization

Price optimization and customization

Demand prediction, propensity to purchase and price optimization based on internal and external data.

Sales forecasting and logistics optimization

Sales forecasting and logistics optimization

Logistics process optimization based on sales forecasting.

Entity detection in videos

Entity detection in videos

Artificial vision for detecting situations such as intrusions, facial recognition, material or video text detection; with the deployment of data models in the cloud or in the Edge.

Fraud detection

Fraud detection

Identification and detection of fraud patterns in transactions in real time or in batches of transactions.

Predictive maintenance

Predictive maintenance

Early detection of component failures via automatic and smart identification of anomalies.

Manufacturing input optimization

Manufacturing input optimization

Descriptive analysis of variables to identify optimal and most efficient conditions.

Material defect detection

Material defect detection

Detection of cracks, oxidation, color changes and quality losses in infrastructure, construction work and manufacturing.

Facial identification

Facial identification

Identity validation via facial recognition for authorization and check-in processes.

Quality optimization in manufacturing

Quality optimization in manufacturing

Detection in the process of key factors affecting quality and recommendations of adjustments to improve the quality of the final product.

IoT in Edge Computing

IoT in Edge Computing

Processing in Edge of information which, due to low latency requirements or communication problems, cannot be uploaded to the cloud.

IoT sensor analysis

IoT sensor analysis

Data and indicator visualization in industrial environments on sensor data that is ingested, converted and enriched in real time.

Operational data analysis and visualization

Operational data analysis and visualization

Calculation and visualization of operational indicators from data ingested from various sources.

Web browsing analytics

Web browsing analytics

Web behavior analysis of customers and website visitors using data from Google Analytics, Adobe Analytics, etc.

360° view of customers

360° view of customers

Integration and consolidation of browsing, transactional, operational and satisfaction data in a holistic view that allows the customer portfolio to be segmented and evaluated.

Some of our customers

They are very competent. Their workers are very professional and high-talented with lots of skill in cloud infrastructure, cloud computing and Machine Learning. 

Data Scientist Lead, Transportation Company

They are a very solid team with serious knowledge of data science, algorithms, model training, and model scalability. They work with a high level of best practices in the field of development.

CTO, Retail Company

Keepler is a very reliable and skilled provider, always understanding the client’s needs and providing the right piece of advice at the right time. Clearly, they are a key piece of the team

Advanced Analytics Lead, Insurance Company

They have a very qualified team of professionals that make them manage and complete projects with a very high degree of satisfaction. Their knowledge of cloud services is very powerful and they always advise the best way to make the products more efficient and easier to manage.

Data Engineer Lead, Entertainment Company

They provide very valuable service based on quality and knowledge. Their consultants are highly skilled and know public cloud in depth.  

Global Head of Data Engineering & Tech Architecture, Banking Company

Impeccable professionalism and market knowledge make Keepler an ideal partner for strategic projects in our company.

Data Insight Lead, Travel & Tourism Company

Trusted company specialized in Cloud with extensive experience in the Data & AI field. We have been collaborating for several years on various initiatives with highly talented individuals both professionally and personally.

- Head of Data Engineering, Telco Company -
customer satisfaction
cities with remote workers
cloud certifications

We create your data products.

A Data Product solves a business use case, combining public cloud technology, custom developed business logic and data. Data products are integrated into company business processes, optimizing customer acquisition and retention capacities, reducing operating costs and enabling better data-based decision making.

Keepler offers a Full-Stack Analytics service based on its architecture capabilities in the public cloud, data engineering, cloud and data governance, data science and data visualization. Keepler offers consulting and uses agile methodologies to identify, define and manage the entire data product life cycle.

Using public cloud technologies makes it possible for Keepler to deploy components and ingest data that is reused in multiple data products, making it possible to scale the use of data in an organization to scale.

We provide data analysis with a focus on Data Products to ensure that we supply continuous and operational value from the outset.

Why should you choose us?

At Keepler we believe that it is possible to do things differently. We share our work philosophy and culture with our clients which makes us part of a single team.
We are Experts

The need to build products and services around data is common in all business sectors, whether they are fully digital or physical products. At Keepler we are experts at getting the full potential out of data.

We’ve got real world experience

Our experts are highly qualified and have years of experience developing native software products on the most advanced public cloud platforms: Amazon Web Services, Microsoft Azure and Google Cloud Platform.

We are agile

Data isn’t static, so products have to constantly and quickly adapt to clients’ needs. At Keepler we are agile at continuously changing how we act, which is an important requirement for a data product that brings real value to Businesses.

The journey to build data products.

Having the ability to build data products to scale requires mastery of a number of techniques and technologies that will be deployed as a essential part of these products that are then continuously reused. Keepler provides support for adopting and scaling the use of techniques and technologies that are essential to optimize use of the public cloud and data at a company.

Adopting the public cloud at scale requires the establishment of a number of components from the ground up, facilitating the subsequent management of security, cost, reliability, operation and performance in large cloud deployments.


Design and deployment of the public cloud account structure where the data products will be hosted. Setting up mechanisms to centralize information on cloud use and costs, security and performance monitoring. Integration with third-party monitoring and security tools.


Assessment of software already deployed in the cloud with the aim of applying public cloud best practices. Validation of architectures and recommendations in order to improve security, costs, reliability, performance, reduce expenses and improve the operating model.


We define and deploy processes and automation to optimize the software life cycle (DevOps), automate the response to security incidents (SecOps), monitor and reduce the costs of the cloud (FinOps), scale the use of Machine Learning (MLOps) and standardize the provisioning of cloud services (Vending Machine).


We migrate use cases from on-premise and legacy platforms to the public cloud, undertaking reengineering in order to use the best native data services and optimize costs and performance. We also migrate use cases from one public cloud to another using corresponding services between cloud platforms and mitigating the cloud’s vendor lock-in.

Data is the main component in the data products. Therefore, building data products requires understanding the business data model and identifying business value datasets, including structured data stored in databases; unstructured data, such as documents, e-mails, or videos; and semi-structured data, such as IoT sensor data.


Development of processes for extracting, loading and transforming data in batch or real time mode using Spark, Python, Scala technology and processes based on microservices and serverless computing. Integration of ingestion with data governance systems to document the lineage of information.


An operational Data Lake permits the maximum information to be available regardless of the structure or volume. We work on the ingestion, integration and consolidation so that it can be harnessed by businesses.


We consolidate customer information from multiple data sources to a single one, allowing us to generate a full picture of reality for a true 360-degree strategy.


We gather all of the information from employees and partners in one place (history, promotions, surveys, incidents, etc.) to analyze and propose improvements in order to increase loyalty.


The increasing quantity and complexity of information makes it difficult to explore. We generate an analysis and exploitation system, both in batch and real time, applying ML models to strengthen its operation.


All marketing, customer and sales actions in a single repository to understand and predict behavior, helping departments to optimize campaigns and new product launches.


Data Lake capabilities in the cloud allow the storage and processing of large volumes of valuable information with the necessary agility while maintaining costs compared to traditional information systems.

Millions of pieces of data accumulate each day incrementally. This information has the potential to provide business value and decision making capabilities if it is exploited properly. At Keepler we can help to build real use cases adapted to the specific needs of each organization, based on Data Lakes of information, translating business or innovation requirements into data products that provide solutions to today’s needs and that will be able to evolve to meet future needs.


We deploy public cloud services for Data Governance and integrate them into data ingestion, transformation and consumption processes. Data Governance provides a centralized view of the data life cycle, providing information about its quality, security level and business importance.


We design, deploy and optimize cloud data warehouse technology, which leverages the separation of information processing and storage to ensure scalability and access to information for large numbers of users. We design the Data Warehouse data model to optimize the ability of it to be queried from different applications.


We design data access systems and Machine Learning models based on APIs through using managed API Managers and microservices and Serverless Computing. The architectures are completely auto-scaling and their cost is based on use.


Business insights can be obtained by using advanced data visualization. Charts, graphs, maps, etc. Visual resources for identifying and understanding trends, exceptions and patterns in the data We design and deploy dashboards in cloud native business intelligence services or in licensed solutions such as Microstrategy, Tableau and Spotfire.

Artificial Intelligence enables new information to be generated from what already exists, thereby increasing the value of a company’s information. Integrating Artificial Intelligence into a company’s processes requires being able to explore, model, train, monitor and retrain data models at scale and in such a way that they are reproducible.


Co-creation oriented dynamics allowing for the creation of a catalog of use cases, as well as the conceptualization of data products in order to start building Minimum Viable Products.


Automation of environment deployment for data scientists. Teams of scientists and data experts require configurable and scalable environments. Cloud Environments provide data access and storage capabilities for exploitation by BI tools and ML applications.


The first step in successfully exploiting the data through Artificial Intelligence is to investigate it in depth. We’ll help you to analyze the quality, completeness and dispersion; while at the same time deepening the relationship to move closer to the business hypotheses mentioned above.


Statistical techniques allow us to go beyond visualization and find hidden relationships between all of the data set variables and key indicators identified for the business or processes.


Applying machine learning techniques allows hidden patterns in information to be identified. We apply these patterns to new information to infer or predict behaviors, values or classifications in an automated fashion and therefore optimize processes and costs, or generate new revenues.


Discipline within Machine Learning whose techniques are particularly useful when processing unstructured data such as when in text or images/video. Applying them enables us to streamline processes that were previously carried out manually or supervised by people.


Use of devices such as cameras and industrial servers to run models by locally performing inference without uploading data to the cloud and with significantly reduced latency. Device installation and automation of model deployment on these devices.

We are partners

Work with us.

We are looking for the best public cloud software engineers and architects, data scientists with real project experience and Scrum Masters. If you are one of them, we are waiting for you at Keepler. Have a look at our job offers.
We are waiting for you
At Keepler we want to grow our team with people who want to develop data-driven software with two goals: to help our clients in their digital transformation and to enjoy the process of creating value through technology.

If you want to join us and there is no open vacancy that fits you, leave your resume here and you’ll be considered for future positions.

Our salary transparency model allows both candidates and employees to know the potential salary that they would earn in the company.

We talk about data, ML, how the public cloud will drive analysis, learning, new work philosophies and cultural change in business.

A guide intended for candidates and employees and for all those who want to get to know us better and how our culture works.

You can find us here.




Juanma Aramburu
Juanma AramburuCEO
Esteban Álvarez
Esteban ÁlvarezGlobal Business Lead
Richard Cooke
Richard CookeUK Business Lead
Florian Sieren
Florian SierenDACH Business Lead
Pedro Matos
Pedro MatosPortugal Business Lead
Contact us

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