We focused on model “scaffolding” and building systems to interact with models.
Our platform offers potential value to companies trying to leverage big data and helps them better understand subtle changes in behavior, preferences or customer satisfaction.
Machine Learning Platform
We build better models for many stakeholders to continuously improve their performance.
Machine Learning Analytics
We infer patterns from a dataset without reference to known, or labeled, outcomes.
Spatial Analytics Platform
We uncovers the connections and spatial patterns necessary to accelerate real-world insights in spatial interaction.
Machine Learning Platform
Unsupervised Learning
Unsupervised Learning an Angle for Unlabelled Data World.
Used unsupervised vision learning algorithms to decrease the demands of annotated data.
Unsupervised learning algorithms do not rely on an annotation to indicate the relationship between input and output.
Unsupervised learning uses the latent factors in data to conclude the relationship between data and the corresponding learning task, and there is no need to mark the data.
A technique with the idea to explore hidden gems / patterns.
Infer patterns from a dataset without reference to known, or labeled, outcomes.
The best time to use our platform is when you do not have data on desired outcomes, such as determining a target market for an entirely new product that your business has never sold before.
Some applications of unsupervised machine learning techniques include:
1. Clustering allows you to automatically split the dataset into groups according to similarity.
2. Anomaly detection can automatically discover unusual data points in your dataset. This is useful in pinpointing fraudulent transactions, discovering faulty pieces of hardware, or identifying an outlier caused by a human error during data entry.
3. Association mining identifies sets of items that frequently occur together in your dataset. Retailers often use it for basket analysis, because it allows analysts to discover goods often purchased at the same time and develop more effective marketing and merchandising strategies.
4. Latent variable models are commonly used for data preprocessing, such as reducing the number of features in a dataset (dimensionality reduction) or decomposing the dataset into multiple components.
ULbrain™
Data Analytics ACADEMY
Spatial Analytics Platform
You can’t change what you’re not measuring.
Human Interaction with Spatial
Spatial patterns are rich sources of data. Yet for many stakeholders in the industry, these data streams are often the least understood and most underutilized. The need to analyze spatial data generated by biological, behavioral and socioeconomic factors, including the physical space a person inhabits, has never been greater.
To fully realize Spatial Interaction based approaches, we must think beyond the highly conceptual and policy-oriented methods of the past.
With artificial intelligence and machine learning, organizations can process gigabytes of geospatial data and generate actionable insights to make informed business decisions.
High-performance analytics at scale. Native AI/ML data models built from the ground up
Our spatial analytics platform is built from proprietary models and algorithms, along with machine learning and artificial intelligence (ML/AI) techniques for determining the influences of geo-spatial and other factors.
Our comprehensive Spatial Analytics platform continuously evaluates the human interaction with the spatial to deliver streamlined insights across the industry.
We then take this a step further by implementing robust data models we call the Spatial behavior detector and Spatial Prospector, which allows us to create individualized analyses that are dynamic and forward-thinking. Finally, we offer our Spatial database primitive, an on-demand repository of data analytics tailored specifically for application services.
Implementation Services
Our data model is fully customizable, tailored to specific needs of every stakeholder, with self-learning algorithms trained to identify both repeating and repeatable spatial patterns.
Spatial Prospector
Spatial Behavior Detector
Machine Learning Analytics
It’s not an option, it’s an imperative.
Analytics
For marketers and policy makers, increasing results is the surest way to maintain a competitive advantage.
Leveraging our cutting-edge Machine Learning platform, we’re taking advanced analytics and model building to new heights for our clients. Whether you’re looking to boost response rates, decrease cost to acquire, increase ROI/LTV, reduce churn, Increase policy effectiveness or all the above, our analytic services help drive continuous program improvement and cover the full range of descriptive, predictive and prescriptive analytics.
Machine Learning Analytics
The most advanced method for optimizing your acquisition, upsell/cross sell and retention marketing campaigns. We build better models.
We combine our Big Data assets with a purpose-built Machine Learning platform optimized for marketing applications.
The platform optimizes your acquisition, upsell and retention campaigns, enabling you to cost-effectively obtain more customers and maximize their value. This approach generates results that are 30%-50% greater than traditional segmentation models. Think of it as modeling on steroids.
How Our Platform Works
We gather thousands of pieces of data for every client project, aggregating your campaign histories and customer responses with our in-house databases which include demographic, socioeconomic, psychographic and buyer/donor behavior data on every household and firmographic details on every business in the country
Our platform churns through the data on a massive scale, analyzing thousands of combinations of variables in sub-second intervals. It then creates highly complex algorithms to isolate high-value prospects wherever they may be found within the data set.
Engagement Detection
