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Domain Specificity

Accurate A.I. Requires Focus

General A.I. is science fiction. For any A.I. to be accurate the problem must be confined, the right core capabilities selected, and then trained within a specific domain


Problem Confinement

Domain experts define the problem, data, and desired outputs for a product


Technology Selection

Technologists determine the right mix of components needed to produce these outputs


Domain Training

Domain experts train the system with a small set of examples that constitute a small percentage of all available data

4 AI Learns

A.I. Learns

The expert examples form a semantically rich training data-set the AI consumes to learn about the domain initially

5 Continuous Learning

Continuous Learning

With periodic expert feedback and continual hypothesis testing, the system keeps improving its understanding of the domain

6 Expertise Scaled

Expertise Scaled

Over time, the accuracy increases and ultimately the value to end users


As we grow out and expand our universe of domain layers here is a sample of what we have today

Mergers & Acquisitions

Industry Specific Topics

Private Equity Deals

Clinical Trial Efficacy

Oil & Gas Drilling

Disaster Relief

Team Dynamics

Corporate Strategy

Macro Sentiment Flow

Counterfeit Coins

The Accrete Way

Creating the domain layer and plugging it into the A.I. is a fast process. This is only made possible due to the modular design of the core capabilities and a robust scalable process.


Core Capabilities

We build inter-locking modules that allow the rapid creation of robust and custom A.I. solutions for specific domain use cases

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Scalable Process

A.I. that is dynamically continuously learning offers scalable expertise. As the system incrementally learns more it grows increasingly autonomous with decreasing amounts of human intervention

Coming Soon