Services Showcase

Developing Fully Interactive, User-Driven
Data Applications and Dashboards

Making data accessible to all, including researchers, analysts, decision-makers, and the general public, is our Team's top priority. That's why we're constantly innovating new methods for interactively visualizing and analyzing data through intuitive, user-driven applications and dashboards.

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Analyzing trends, investigating time series, and storytelling data narratives shouldn't just be limited to data scientists with programming experience.

Our Team is pioneering new applications and dashboards that prioritize user-driven data experiences. We're giving users the freedom to conduct their own sophisticated analysis without programming language expertise.

By creating sleek, modern interfaces that seamlessly connect with optimized back-end processes capable of handling simple and advanced data analysis tasks, we're accelerating the push to democratize data analysis for all.

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View our example application, built with the power of Google Earth Engine, that examines hourly meteorological data for the continental United States using variables including temperature, wind speed, precipitation, cloud cover, and many more. Interactive hourly time series spanning weeks and months are generated from users clicking any location on the map.

Creating Interactive 3D Environments that
Merge Big Data with Our Living, Changing World

Data integration is paramount to successfully understanding the Big Data picture. Organizations often have data coming from a variety of sources--our work is to integrate that data into intuitive applications, transforming disjoint analysis into one combined, informative product.

Taking advantage of the speed and responsiveness of modern graphics language developments, we've developed high resolution, fully interactive 3D environments simulating our living, changing world.

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With our 3D environments, entire communities can be fully rendered with buildings, elevation, terrain, boundaries, and different data sources integrated into a single 3D application accessible by all users through a web browser.

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Our 3D environments illuminate the world and the nuances of the data within it. Fast and responsive, users can interact with the Earth like never before. Events can be updated in realtime giving critical support to communities and emergency response personnel for identifying at-risk communities during flooding events, wildfire incidents, and other extreme phenomena.

Combined with statistical analysis and data mining capabilities for a comprehensive user-driven experience, our integrated applications yield impressive insights for data of any size.

Designing Pipelines for Data Preprocessing and
Performance Boosting through Parallel Processing

Pipelines are efficient methods for extracting data, storing in data warehouses, and transforming data to generate insightful features. Embracing the versatility of Python, our Team's developed pipelines that prepare data for models using Scikit-Learn, PyTorch, and TensorFlow.

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Efficiently handling Big Data for systems of scale requires parallel processing, where large data is first partitioned and then loaded, processed, analyzed, and modeled chunks at-a-time. In addition to this, our Team focuses on optimizing Python functions for speed and efficiency that operate in parallel during data preparation and model training.

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We've programmed pipelines for handling small-sized data, moderate-sized data, and Big Data for a variety of data types including tabular structures, images, videos, and text.

Generating Responsive, Fast-to-Display Maps from
High Resolution Spatiotemporal Data 

Our Team brings strong experience visualizing high resolution raster (GeoTIFF, COG, GRIB, DEM, NetCDF) files and vector files through tiling software for immediate loading and rapid display in web browsers.

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Using Python, Google Earth Engine, JavaScript, and additional map tiling services, we've designed interactive maps built for business, climatology, meteorological, and socio-economic data applications. Our products are capable of displaying multiple datasets on a single map that are responsive to user inputs and generate mosaiced tiles rapidly.

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Our interactive maps are connected to spatial databases allowing quick information retrieval for any supported location or geography that users select. Our maps are easily incorporated into applications and dashboards and incorporate the latest open source technologies to render high resolution, beautiful visualizations in any web browser for all users to interact with.

Developing Beautiful Visualizations that Showcase Decision-Making Insights for Data of All Types

Dealing with numeric attributes, time-sensitive features, categorical variables, and geo-referenced data requires visualizations that are meaningful, vibrant, and informative. Inherent to all analysis is the ability to tell a story with your data, and our Team's experience producing charts, diagrams, and dashboards makes those stories even more compelling.

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Using open source programming languages including Python and R, our Team brings to life static, interactive, and animated visualizations tailored to our Client's specifications.

We automate processes that generate stunning and fully-customizable visualizations and give our Clients the freedom to create their own dashboards and visuals with no software product licensing fees!

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From interactive dashboards to stunning data visualizations for reporting, our Team eliminates the costs associated with fee-based dashboard and visualization services by automating these processes with popular open source languages such as Python and R.

Optimizing Cutting-Edge Machine Learning & Deep Learning Algorithms that Scale to Structured, Semi-Structured, and Unstructured Big Data

Organizations are faced with multiple questions on their road to training a successful model. Some of these questions might include the following:

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How do we prepare the data?

Which model is best suited for the data?

How do we fine-tune the model for our specific purpose?

Which algorithm best generalizes to unseen data?

How much training is too much training?

Which metrics are best for measuring this particular model's performance?

Our data is not labeled, now what?

Which features are highly important to the model's predictive capabilities?

How do we deploy this model into production? 

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Our Team answers these questions for our Clients to deploy optimized models using Python, R, and SAS.

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Our Team's trained, tested, and evaluated models for supervised regression, classification, and object detection, unsupervised learning on data without labels, segmentation neural networks for images and videos, and generative A.I. using deep learning.