Models of analytics e machine learning

We prepare the data structure, help build the analytical models, as well as define the rules and criteria for machine learning supervision

Optimizing content, processes, and dialogs

Using techniques such as Design Thinking and UX, we adapt the identification of the speaker, the directional phrases (the way the machine asks), and the delivery of the answers, to ensure the best user experience, the achievement of goals, and to support the expansion of the service offering.

Revitalization of existing information systems, with fast ROI

Adding AI and automation capabilities to legacy has resulted in several Operational Efficiency initiatives and market differentiation.

Promotion of self-service functions

By replacing IVR dialing options or application menus with verbal command recognition, rich self-service options can be offered without the navigational complexity that brings under-utilization of self-service

Scope and Value Creation

In which services AI gives the organization more efficiency and ease for the customer; how to adjust processes; and how to set focus, ensure quality, and scale implementations determine how and at what pace technological innovation generates results.

Vision and end-to-end solutions

To offer relevant services, with natural usability, and meet project objectives, we work on several layers vital to cognitive applications, such as: data infrastructure adaptation, process analysis, backend process automations, content curation and navigation flow with Design Thinking and UX techniques, machine learning configuration and supervision, implementation and curation of conversational gateways (chatbot, voicebot, etc.).