MoirAI is an Artificial Intelligence (AI), Machine Learning (ML), data analytics, and decision and risk optimization company with a huge difference: Causally aware AI for making preferred outcomes more likely in a changing and uncertain world.
MoirAI offers Software-as-a-Service (SaaS), research, and consulting to help businesses, engineers, scientists, regulators, and policy-makers maximize the return on their existing data analytics, AI, and ML investments by adding the missing link: Understanding of cause-effect relationships in data.
- MoirAI is based on causal analytics, the science of understanding why results occur and how to make preferred outcomes more likely.
- MoirAI supports causal analytics via an easy-to-use Causal Analytics Platform (CAP) that can be provisioned via a secure web portal or installed and run locally.
- CAP is especially valuable to customers and clients in new and novel situations where traditional AI/ML is handicapped by lack of relevant knowledge, data, and causal understanding.
How does MoirAI work?
MoirAI’s causally aware AI draws on the following stack of analytics and AI capabilities to learn and calculate quickly how to act effectively in a changing, uncertain world.
Descriptive analytics: What’s happening? What’s new? What’s changed?
- What should we notice?
- What should we worry about?
- What should we explore further?
Predictive analytics: What will (probably) happen next?
- What will happen if we don’t intervene? How soon? How certain?
- What are we not seeing, and what effects might it cause?
Causal analytics: Why is it happening? What can we do about it?
- What is (probably) causing the observed changes and anomalies?
- What other effect will these causes have? How soon? How certain?
- How do actions and interventions change outcomes probabilities?
- Diagnostic analytics: Why did that happen? How to prevent?
Prescriptive analytics: What should we do/try next?
- Quick responses: Behavior trees, optimized stimulus-response patterns
- Decision optimization + experimentation/exploration
- Strategy: Goals, plans and contingency plans
Evaluation analytics: How well is it working?
- For whom? Under what conditions? For how long?
Learning analytics: How might we do better?
- What causal laws explain the observed data?
- How sure can we be?
- How do these laws generalize to other settings?
Collaboration analytics: How to do better together?
Competition/evolution: How to thrive in open world?