Technology for business solutions.
Cloud Computing
Government, architecture migration and optimization on the public cloud.
Big Data
Construction of Data Lakes with a focus on big data as a service in order to provide business solutions.
Artificial Intelligence
Algorithms for optimizing customer-oriented business solutions.
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.
Price optimization and customization
Price optimization and customization
Demand prediction, propensity to purchase and price optimization based on internal and external data.
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.
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.
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.
By applying the Agile methodology, we help you to conceptualize your products and to align all of a project’s stakeholders in order to carry it out whilst ensuring success from the start.
We create the cloud environment using the best, most secure and flexible practices. Operational landing pages are built within a framework based on the Well Architected Framework and information and data governance.
We build and update Data Lakes and Data Warehouses, in order to carry out the descriptive analysis of information. Through exploring data and visualization techniques, a complete view of the organization can be achieved in a short period of time.
Adding AI / ML into descriptive analytics makes it possible to perform a more complex and sophisticated analysis. Machine learning generates new information that enriches the data flow and feeds the ML models to increase the value.
Development of the end-to-end project is orchestrated by DevOps, SecOps, MLOps and FinOps work philosophies. They guarantee the continuous delivery of functional, quality software that meets its requirements and that has security built-in to the design, cost control, and the option of making modifications.
Why should you choose us?
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.
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.
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.
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.
ENTERPRISE LANDING ZONE / FOUNDATION
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 AND BEST PRACTICES
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.
AUTOMATION
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).
DATA PLATFORM MIGRATION
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.
DATA ENGINEERING
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.
DATA LAKE OPERATIONS
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.
CUSTOMER DATA LAKES
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.
PEOPLE DATA LAKES
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.
IoT DATA LAKES
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.
SALES & MARKETING DATA LAKES
All marketing, customer and sales actions in a single repository to understand and predict behavior, helping departments to optimize campaigns and new product launches.
TRANSACTIONAL DATA LAKES
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.
DATA GOVERNANCE
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.
CLOUD DATA WAREHOUSES
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.
DATA APIS
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.
DATA VISUALIZATION
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.
USE CASE DISCOVERY
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.
CLOUD DATA LABS
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.
DATA & RESEARCH
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
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.
MACHINE LEARNING TECHNIQUES
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.
DEEP LEARNING TECHNIQUES
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.
INFERENCE IN THE EDGE
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.
Work with us.
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.
Emails
Info and clients: hello@keepler.io
Media and events: marketing@keepler.io
Jobs: people@keepler.io
