About OnCorps

We provide groundbreaking AI software to the world's premier financial institutions

A Mission to Elevate Work through AI

At OnCorps, our mission is to elevate work and performance by applying AI to handle predictable and laborious calculations, decisions, and tasks. We provide advanced AI solutions to the financial services industry, where our system has substantially reduced manual work, errors and risk. We solve problems with cross-disciplinary teams, drawn from top-tier research universities and consultancies, possessing  industry, data science and computer science expertise. 

Our pioneering algorithms have shown dramatic results. Our operational AI systems have reduced labor costs by over 90 percent. Our algorithms have identified millions in errors. And our forecasting systems have consistently beaten client forecasts. Our success has led to strong demand, attracting clients with over $10 trillion in assets under management - 20 percent of the global mutual fund industry. Our innovative algorithms have earned us the 2019 NOVA Award by NICSA and the 2019 Fintech Breakthrough Award for best banking infrastructure software.

Our business model has also achieved breakthrough results. We have consistently designed and deployed complex AI solutions in less than 100 days. We do this by leveraging state-of-the-art cloud technologies to design workflows, train algorithms, and integrate with legacy systems. Expert, cross-disciplinary teams deploy these technologies rapidly. In all cases, our clients have paid no external consulting fees nor required internal technical team support. We believe results from AI can only be achieved by providing constant, high-end support. We provide process, algorithm, and systems support delivered by the same team that designed the solution. This simple difference reduces significant inefficiencies and elevates our service far beyond that of any other firm.

We are committed to delivering our clients the most cutting-edge solutions possible. To accomplish this, we work closely with scientific advisors from Yale, Harvard, and Oxford to formulate and rigorously test new algorithms. Our work in behavioral AI has been published in the Harvard Business Review and other journals.

Core Principles


Solving problems with AI is not simple. It requires experimentation, hypothesis testing, production and deployment expertise, and industry knowledge. We seek a high-degree of craftsmanship in solving problems with our platform.

Symbiotic Learning Systems

Our systems learn from your best decision makers. They provide guidance and recommendations based on these decisions and continue learning as conditions change. We believe training AI algorithms takes years and requires patience and investment.

Holistic Problem Solving

Improving decision making requires an inter-disciplinary team. We train team members who have multiple skills in data science, behavioral science, software engineering, and user-centered design.

Industry Focused Algorithms

The decisions and processes we support require deep knowledge in industry terminology, algorithms, and regulatory requirements. We therefore concentrate our efforts around specific industry solutions in asset management and technology.

Fine-Tuning Algorithms

We understand each algorithm needs to be carefully curated. Each variable must be weighed for impact and quality of data. We review our algorithms quarterly with outside experts to ensure they are working properly.

Full Support Model

We price our subscriptions to handle most of your configuration, data analysis, workflow, and training needs. We believe the industry provides fragmented offerings requiring customers to pay extra charges to get what they need.

Business Model


Fixed Fee Customization. Changes in agile development, open sourcing, and cloud virtualization enable us to rapidly customize our platform. We develop customized decision guidance systems for a fixed fee. The fee includes developing algorithms and management dashboards, customizing UI/UX, API configuration, security testing, and user acceptance testing.


High-End Subscription Support. Once we deliver a production pilot, we offer ongoing support with the same team that configured the pilot. Our customers pay a platform fee and per app fee for this support. These subscription fees include support for data scientists, delivery managers, and engineers who monitor engagement rates and algorithms.


Conservative Algorithm Use. We almost always start systems using Policy Settings. These settings don't rely on algorithms, but instead enforce leadership-defined policies and thresholds. Once algorithms gain more consistency and accuracy, we can selectively use algorithms to trigger workflows and recommendations.

Current Economic Model

OnCorps Economic Model

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