Digital Banking Services

Every undetected business opportunity, late after-sales service or incorrect analysis is important to the bottom line of the bank's management. Likewise, identifying problems correctly and consistently is essential for business management.

As a solution to these drawbacks, we created DeepLanding TaskControl, which provides a deep learning systemic inspection development and implementation platform that effectively helps management control areas to easily assess the efficiency of the deep learning model.

Telecommunications Companies

In telecommunications platforms, we monitor all services in an end-to-end mode to ensure not only a good quality of technical services and high predictability, but also to ensure an excellent customer experience in terms of satisfying their needs. , which allows continuous improvement.

We process and bring to life the clear understanding for operators of telecommunications companies thanks to operational intelligence and artificial intelligence analytics to detect any future anomalies in the entire spectrum of services that allow us to anticipate problems and market

Retail Services

In addition to managing all technical and business processes, we detect behavior patterns in traffic and multi-channel transaction processing. Likewise, we have specialized over the years in matching between the buyer (purchases, services and credit scoring) and the best option available by the provider to allow secure and high-quality financial/commercial transactions between the parties.

One of the phases of this service is predictive tracking, which will always provide the necessary lights to detect, process, activate and report success in the end-to-end cycle at all times.

Energy, gas, water and other services

It is clear that all services for the end customer must be operational in their entire range, but we must also pay attention to concepts such as: availability, performance and quality. In addition to this, it is very necessary to have customer feedback to offer continuous improvement in real time.

We have solutions and use cases that have benefited large clients that allow us to have complete visibility of the operations scenario. It is through these tools that we can offer monitoring of operational efficiency from end to end, going through the technical, commercial and financial aspects without neglecting the experience with the end customer, which enables such improvement.

NOC/SOC Management Control

The separation of the critical processes of the company, and the sustainability of the business —without compromising the organization— are key to implementing predictive operational models with dynamic parameterizations on demand. With this, it is possible to transfer the analysis of hard data to artificial intelligence tools so that these are the ones that can anticipate trends and the way forward.

It should be noted that these models are balanced between human and technological dynamism to favor high-level operational continuity without stressing the organization.

Likewise, we want to emphasize that our NOC and SOC solutions have been used in large industries.

Computer Vision

The amount of normal data and its business analysis are overshadowed by a growing trend, so it is necessary to investigate other information —which has always been present— that has to do with the interaction between people, systems and processes. We refer to looking at, capturing and understanding a painting in all possible dimensions. The key is to pay attention to relevant data over redundant data. The latter is an interesting task, but it requires advanced knowledge in multidimensional analytics and parallel processing.

It is necessary to point out that understanding and processing images is also part of what we offer with the use of artificial intelligence.

Production Quality

We deliver efficiency in the OEE production KPI, which is the result of calculating the three most used KPIs in the product manufacturing industry: availability, performance and quality.

When there is a greater volume of said elaboration, the tasks of supervision and the "clinical eye" grow exponentially. Because of this, the chances of errors can go down, but they could always happen. Identifying them is like detecting a needle in a haystack in real time.

Thanks to "dynamic parameters" controlled by machine learning tools, we can streamline processes that would be impossible for a human to inspect with high and continuous precision.

In conclusion, our parallel processing —which is also multidimensional— and our data analytics are the solution.