Artificial Intelligence Leaders.
Deep analysis and critical decisions need human authority.
Engineers do not always know what businesses require, we jump start conversations by listening & discovering.
Explaining what happens inside software is a challenge, assurance and trust are important mechanisms for stability.
The disruption to business processes and personnel need steady hands to guide a transformation journey.
We utilize Neural Networks including all variants such as Machine Learning or Deep Learning. These techniques are cognitive inspired and are based on mathematical (implicit) techniques.
- Identifying Data (images)
- Matching Data
- Counting Data
- Searching Data
We engineer representations of human thinking, human tasks, or domain concepts, often described as rules or knowledge (explicit), which can be compiled or interpreted as software code.
- Functional Models
- Rule Models
- Ontology Graphs
- Knowledge Graphs
These types of systems are not suitable for important business functions at this time. There exists numerous opportunities to further research or discover new use cases.
- Genetic Algorithms
- Evolutionary Algorithms
- Neuro Evolutionary
We implement graph engines. These are tightly bound microservices designed to read, process, or manipulate software models. Attempts to mimic functional neuroscience create the most valuable systems.
- Recommendation Engines
- Decision Engines
- Case-Based Engines
- Cognitive Engines