AI Engineering and environmental Data Science
Striving for simplicity
About Us
Our Story
With decades of experience in various engineering and construction environments we shifted emphasis to data science and the extraction of intelligence for mainly optimisation, but also for cross correlation and discovery.
We have extensive experience in digital engineering - BIM, point data manipulation and analysis, algorithmic design and current machine learning practices.
Recently we have dipped into robotics with a novel prejudicial genetic algorithm that optimises mesh geometry in a fluid dynamics simulation.
We also handle environmental data and are in the process of commercialising a methane detector using satellite imagery ai engineering, data pipelines, machine vision
Our Approach
Our service includes a comprehensive consult to untangle the information model and place business process in perspective. We believe strongly that, connectivity in data is essential for extracting intelligence. Dimensionality in data is encouraged, so as to cross correlate and cluster across functions.
Smaller datasets are useful, even in a Big Data dominated field. To harness the relationships between functions, connectivity is key, and therefore the information model.
Cataloging of siloed data is an expensive way to nowhere and we avoid this approach. Instead we advise on business process that enable automated data acquisition and retain primary and foreign keys.
By respecting the dimensionality and interdependence of data, machine learning becomes plausible, extracting intelligence and presentable as simplistic relations between its sources.
Why Us?
We only take on work where the methodology is sound and value for the client is likely. Data should be converted to intelligence and not merely handled.
This principle ensures a timeline of thorough strategic planning and then execution of a tactical plan. If the tactical element hits a blocker, it can be evaluated on the spot and either unblocked or terminated.
We have found that enforcing the transition to action and milestone evaluation , brings better value to the market. Agile methodologies are acknowledged, but not blindly followed for the sake of it. Unexpected turns of events happen, and our problem solvers need freedom to be creative. This is the value we sought to create.
We will take on smaller projects. Scalability is essential though and ensuring continuity between small and large data solutions takes precedence.