Value proposition-based thinking. Integrum rethinks the entire approach to this problem. Rather than breaking a business problem into smaller parts and tackling each piece in silo, we look at a client’s business value propositions as a whole and engineer tech capability towards an orchestration that fulfills these propositions. This contrasts with the typical approach of creating various functional software for multiple tasks, which often do not meet business value propositions efficiently and effectively.
Continuous AI refinement. Most digitalization tools employed by organizations today are solely focused on fulfilling functional requirements. Data science and AI solutions, especially those centered around decision-making, are served by separate software stacks and teams. This creates frictions, not just between internal systems but employees as well. Our AI solutions are continuously refined with the latest research in both the domain and tech space and are embedded into business use-cases.
Explainable AI. The results of our AI modules are presented by a layer of performance measures that are crafted to be highly explanatory. Managers can now make the right decisions without extensive communications with data science teams who may not share the same business lingo. On the other hand, our AI modules are built according to the suite of tools that Data Scientists are familiar with, which allows them to finetune the solution for customization purposes.
Digitalization meets data science. Data science meets AI engineering. Today, most data science and AI teams are either proficient in business objectives, data, modeling, optimization, or deployment at scale. Very few corporations in the world see the opportunity or economic feasibility to house a team with all of the above capabilities. Our solution avails our clients with the opportunity to enhance their economic value through efficiency and new capabilities powered by AI.