About us
code4thought ® is a technology company founded in 2017 with a unique purpose, to render technology transparent for both large-scale software and AI-based systems. We do that either in collaboration with international companies, or by creating our own technology founded on internal R&D activities.
Activities
Software Quality
We analyze & evaluate large-scale enterprise software systems, in order to address IT-related flaws at the root and manage associated risks and costs. As a member of Software Improvement Group, we enable companies to measure, evaluate and monitor their software quality in every stage.
ACTIVITIES
Trustworthy AI
We analyze AI-based systems and their datasets with PyThia, our own platform and we advise companies worldwide, as to what are the best-practices for setting up the proper processes and infrastructure that will ensure their AI is Responsible and can be Trusted.
Vision
Our vision is to create technology products and services that people and organisations can depend on to encourage, lead and support their journey to knowledge and improvement.
‘We want to make technology
trusted and thoughtful.’
trusted and thoughtful.’
Powered by
decisively investing in R&D
international partnerships & project-participation
strong ties with Academia
TIER1 customer base
team with academic & enterprise experience
participations and publications in international venues
cross domain & technology agnostic solutions
factual and well-researched approach
Values
We deeply believe in
Never settle
Keep on learning, innovating & envisioning
Openness
of Mind, to Feedback, to each other
Respect
We’re all here to serve a good purpose and to help each other grow & improve
Excellence
always overdeliver
Our team
Our team consists of Software, Machine-Learning and DevOps Engineers and has the ability to provide high-end consulting services and conduct R&D activities, combined with commercial product development.
OUR CLIENTS
*in partnership with SIG
FURTHER READING
Tips on how to secure your ML model
Figuring out the reasons why your ML model might be consistently less accurate in certain classes than others, might help you increase not only its total accuracy but also its adversarial robustness.
In Algorithms We Need To Trust? Not There Yet
Artificial Intelligence is impacting our lives for good, so we need to take a closer look
Fix your recommender system to fix your profitability
How bias in Recommender Systems affects e-commerce, society and eventually your profits