THE PROBLEM
Despite Huge AI Advancements…
It still gives us suboptimal guidance every day in every industry.
Leading AI products are a black box which is not acceptable for applications where you must understand the “thought process” such as portfolio management, route planning, and healthcare.
The world is ruled by algorithms and current ML requires humans to discover and test models which introduces their biases, blind spots, and slow pace compared to a machine.
AI has reproducibility issues where outcomes are not consistently replicated or understood.
It still gives us suboptimal guidance every day in every industry.
Leading AI products are a black box which is not acceptable for applications where you must understand the “thought process” such as portfolio management, route planning, and healthcare.
The world is ruled by algorithms and current ML requires humans to discover and test models which introduces their biases, blind spots, and slow pace compared to a machine.
AI has reproducibility issues where outcomes are not consistently replicated or understood.
Machine Learning Based Optimizer
Enter Perceiver AI™
Generates algorithms and predictive models as the result of optimizing against a training dataset.
Output is immediately inspectable and usable.
Can use an existing algorithm as the starting point.
Has produced superior performance across very different domains.
Works Out of the Box
- Requires minimal human intervention. Eliminates the need to pre-analyze how the data behaves.
- Uses a novel form of genetic programming.
- Makes no assumptions. It only uses observed data patterns to determine the optimal model.
Scalable and has a multitude of applications
- Built for large datasets using distributed programming principles.
- Not limited to pre-existing models.
Perceiver creates complex models using intelligent cross-dataset relationship analysis.
How We Work With Customers
Strategic Discussion to Decide on Best POC
- Video collaboration with SMEs
- Agree on fitness function (what we are solving for)
- Agree on success criteria
Onboard to Platform for First Problem Domain
- Mutual understanding of existing process and pain points
- Agree on test dataset (can be anonymized)
- Perceiver AI develops initial model
- Collaborative iteration until success criteria are met
Onboard Additional Problem Domains
- Repeat step 02 for each additional problem domain
Support, Maintenance, and Professional Services
- Technical Support
- Automatic Updates
- Consulting Services to maximize your ROI
Strategic Discussion to Decide on Best POC
- Video collaboration with SMEs
- Agree on fitness function (what we are solving for)
- Agree on success criteria
Onboard to Platform for First Problem Domain
- Mutual understanding of existing process and pain points
- Agree on test dataset (can be anonymized)
- Perceiver AI develops initial model
- Collaborative iteration until success criteria are met
Onboard Additional Problem Domains
- Repeat step 02 for each additional problem domain
Support, Maintenance, and Professional Services
- Technical Support
- Automatic Updates
- Consulting Services to maximize your ROI
The Levels of Improvement Perceiver Achieves are Groundbreaking
30%
Incremental Fuel Savings
For commercial flights using fuel tankering
2,235 tons
Carbon Emissions Reduction
For a single business aviation fleet per year