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There is no need to exaggerate the significant growth of artificial intelligence (AI) which is gaining traction both among various organizations and in the scope of companies seeking to penetrate the industry. Estimates say that the field of AI is expected to grow 20 times by the end of the decade, and possibly even beyond that.
Artificial intelligence and its many technological derivatives cover a huge scope of industries including supply chains, marketing, product manufacturing, research, analysis, content and more.
We stand alongside venture capital funds that invest in start-ups that deal with artificial intelligence and serve the high-tech companies themselves at various stages of their development. This is how we also help companies from all sectors of the economy that are looking for AI-based solutions to integrate into their operations.
The advice and support is derived from the experience we have gained for over 10 years as a software house , which has managed and carried out quite a few projects in the worlds of artificial intelligence and machine learning. Our company has deep expertise in artificial intelligence, machine learning, natural language processing, computer vision and other technologies related to the world of AI.
We advise in the characterization of AI systems and infrastructures, as well as in the design of artificial intelligence algorithms. Our service is based on the understanding that the development of AI systems does not end with the construction of an algorithm (however sophisticated and groundbreaking it may be). The success of the development lies a lot in the development of AI infrastructures
At the company level that enable a faster learning process and a better adaptation to the end customer ( %-90% of the work).
The end customers, and as a result the developer company, are looking for the value and less the way (AI). A successful and profitable AI project must adapt the algorithm for practical and effective use.
Successful AI solutions must rely on quality data collection for learning from diverse sources.
Building strong infrastructure
that enable learning cycles
and fast optimizations.
Realization of profit potential and knowledge
From an existing activity and on a basis
the current infrastructure.
Savings in computing costs
and during work hours.
