Clear Organizational Need and Process are Vital to RPA Success

Over the last decade, automation technologies have become generally ubiquitous across almost every sector and field. A 2019 study co-led by The Economist Intelligence Unit found that more than 90% of private-sector organizations around the globe are replacing manual tasks with machine-operated ...

Digital Transformation and Telework are Improving the Workforce

The ubiquity of connectivity, increasingly portable and robust computing power, and more attentiveness put to security measures like dual authentication and biometric logins have fueled a corporate shift that has big cultural and economic implications. Organizations are embracing a flexible telew...

Case Study: Digital Transformation Eliminates Inefficiency and Waste

In 2014, a federal agency approached NT Concepts with a request to help them transition from a paper to a digital environment. In particular, the Agency was seeking to digitize its vast paper files and modernize its processes for handling the ever-growing workload. In this case study, we’ll discu...

Mission Readiness with Machine Learning and PHM: Failure is not an Option

Federal agencies understand that the health of their large-scale equipment is mission-critical. From fleets of machinery to military installations to aerospace, success often depends on ensuring the mission is not impacted by costly asset downtime and extending the life of the assets supporting...

Shift Your Culture to Advance Digital Transformation

As we pointed out in the introduction to this blog series, Enabling Missions Success with RPA, AI, and Common Sense, data is the “central, fundamental component of every program.” Public and private organizations are striving to manage the onslaught of input and documents in a rapidly changing op...

Enabling Mission Success with RPA, AI, and Common Sense

Moving from paper to digital to enhance the mission Government agencies have begun to make significant strides in their paperless journey. NT Concepts performs in the customer’s reality, then collaboratively designs and implements solutions like Robotic Process Automation (RPA) and Artificial ...

The [Hidden] Challenges of ML Series: Quadrant 4 Inference & Deployment

If you haven’t read through the first three quadrants of Machine Learning (ML) Lifecycle series yet, we encourage you to take a moment to familiarize yourself with the building blocks of  AI/ML project design, data preparation, and model fitting. Once the team reaches the Inference and Deploym...

The [Hidden] Challenges of ML Series: Quadrant 3 Model Fitting

Even though the bulk of the work happens during the data preparation and labeling phase in quadrant two, model fitting constitutes the meat and potatoes of Machine Learning (ML). This third phase of the ML Lifecycle is iterative and slow—a full training run may take days or even weeks. Early vers...