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Discover - 

Build Models

We provide agile AI/ML building blocks, recipes, and tools for creating enterprise-grade models quickly and efficiently.

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AI/ML Thought Leadership

How it works?

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Feasibility Phase

Schedule a call with us to determine the feasibility of AI/ML project.

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Model Build Phase

Our team of data scientist and engineers work towards solving the challenge. Please rest assured that you are in good hands.

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Education / Knowledge Transfer

The last stage, when we transfer the knowledge to you team, to manage and maintain the system.

Impact: Value Creation from Day 1

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ML-Graph Models

In the real world, data is highly interconnected.

Today, most data science analyses revolve around aggregation and generalization. But we live in a hyperconnected world and personalization is needed at every stage of the customer lifecycle. For this reason alone, we have developed ML-Graph a scalable graph analytical solution.

How Graph Analytics works?

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Pre-Processing

  • Define the nodes and relationships
  • Apply filter conditions
  • Integrate 3rd party datasets
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Build Networks / Clusters

  • Create Clusters at scale
  • We are talking about Billion nodes processed in a matter of minutes
  • Imagine, creating clusters on all weblog data for a digital native Fintech for the last 4 years of prospect and customer activity
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Post-Processing

  • Analyze clusters for patterns and key attributes to solve for business case
  • 150+ graph-related attributes out of the box
  • Business metrics are generated at the cluster level to develop more meaningful outcomes

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When to use this approach?

  • Identify for anomalies in the interconnected data
  • Study the dynamic group behavior
  • Graph Analytics @ scale

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Key Use Cases

  • Online Fraud & business policy abuse detection
  • Customer 360
  • Recommendation engines and Personalization

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Want to learn more about ML-Graph?

1.3

Generative Rule Mining

Automatically generate high-performing intelligent alternative strategies that you'd never think of on your own from a single idea.

With our proprietary generative rule mining solution, you'll be up and running quickly and start developing non-hypothesis data-driven business strategies.

What goes into Generative Rule Mining Process?

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Generate

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Analyze

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Rank

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Evolve

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Explore

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Integrate

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When to use this approach?

  • Solves for Dirty Target Variables
  • Automatically generate valuable rules/Patterns of interest
  • Generates Rules are easier to understand and implement

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Key Use Cases

  • Fraud & business policy abuse detection
  • Customer Segmentation
  • Credit Risk Modeling

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Want to learn more about Generative Rule Mining?

1.4

Deep Sequence Hydra Models

As the name implies, time series data is an ordered sequence of a particular event spaced over time. In the real world, it is used to process clickstream data, financial analysis, and sensor data. These sequences contain valuable information that can be leveraged to build intelligence. 

Modeling time series data is challenging because cross-sectional or temporal aggregated views of the data effectively eliminates signals in the fundamentally ordered nature of the data. 

To solve these changes, we have built a deep learning Hydra Model architecture to  divide-and-conquer different types of data in different "heads", and then synthesize them to extract intelligence and assist decisions.

How does Event Stream Hydra Models Work?

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Event Stream

The stream of events, in particular, are represented as a sequence sorted in time.
For example, sequence of webpages visited by a user, sequence of transactions on an account, sequence of doctor office visits.

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Hydra Model Architecture

A hydra model divides-and-conquers different types of data in different "heads", and then synthesizes them to extract intelligence and assist decisions.

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Model in Action

During real-time streaming, the hydra model evaluates and provides recommendations for the "Next Best Action". These recommendations can then be incorporated into real-time decision making at scale.

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When to use this approach?

  • Modeling Event Sequence data
  • Combine multiple Event Stream and cross sectional data to develop a unified model

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Key Use Cases

  • Fraud & business policy abuse detection
  • Best Next Action
  • UX/UI Optimization based on user activity

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Want to learn more about Hydra Models?

Our Three Tenants of AI Governance

At Hudson AI Governance is not an afterthought, but integrated into the way we build every model and system. Our three pillars for governance consist of the following:

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Trust

Models are built on the character, ability, strength, and truth in Data, algorithms, and People.

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Transparency

Transparency about data origins, machine-learning intentions and model explainability

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Diversity

Diversity is required in people's minds, backgrounds and cultures, as well as data selection and algorithm choices.

Whats makes our approach powerful

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Time and Battle Tested ML Recipes

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Bespoke models for your mission-critical application

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In-depth knowledge of 3rd party datasets

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In the end, we build reliable machines to generate lasting value

We are open for Innovation

Contact Us →