Let’s start from common ground: you have a business challenge for which you are presented with one of two evils:
Have I hit home? Then keep reading and hear the case for customizable digital delivery at scale and how we, at LTPlabs, are achieving it, anchored at the backs of our data and tech teams. We won’t sell you another perfect-fit-yin-yang story, but rather a real example of how an amalgamation of different profiles managed to achieve the oxymoron that is “customization at scale”.
When we set out to build AIR, our digital delivery brand, we have done so with a clear goal in mind: excel in delivery, assuring that our internal teams can deploy fast and seamlessly without disrupting their natural development rhythm, that customers can reap benefits as soon as possible and that no compromises are made to new feature rollout and support.
One place to look for inspiration is at motorsport, where there is a recurring saying: “To finish first, you first have to finish”. To build a great digital experience there are a plethora of things that just can’t fail or be missing.
Just like any driver trusts that turning on the ignition will get their machine running and that no wheel will come loose while cornering, customers of a digital product came to expect a great and intuitive user experience, availability, speed, scalability, and security. This required robustness is the focus of our Tech Team.
With that covered, we can start working on finding an edge – be it raw performance, fuel efficiency, or aerodynamic upgrades. Our Data Science team, just like engineers in an F1 team, is composed of the people that bring innovation to the table, dressing up the product with all the latest bells and whistles.
At this point, you might be baffled by promises of ‘delivery at scale’, trying to understand how such strategy links with the promise to deliver customized analytics solutions. Those are question marks that will disappear once you get to know the scope of action of our Data Science team.
Deeply rooted in LTPlabs’ desire to strive for analytical excellence, the team works fundamentally for internal customers and has room to rethink, propose novel approaches, and disseminate knowledge throughout the organization. Its main goals encompass:
1. Systematizing the best approaches for specific business problems
With a bird’s eye view of all past and ongoing projects, the Data Science team has the needed distancing to pinpoint common ground in analytical approaches that address similar business problems. Systematization is, thus, this process of creating an overarching environment that implements a set of best practices we found through vast experience in tackling these challenges.
These generic environments are also highly customizable, to chase the business nuances found across customers and industries.
The validation of this versatility is assured by a panel of seasoned professionals in the field.
2. Incorporating innovative elements from state-of-the-art methods
Embedded in LTPlabs’ culture is a deep passion for analytical approaches capable of positively transforming businesses. Our Data Science team takes this trait to heart, relentlessly bridging the gap between innovation and business applications, ensuring that every solution shipped relies on an ever-evolving state-of-the-art approach.
Now that you know how our data scientists work, let’s talk about the role of our Tech Team. Creating algorithms that improve businesses processes is just one part of the whole picture. We also need to integrate these algorithms into systems that can be used and perform at scale with the reliability that is expected/needed from them.
The Tech team works on building the avenues that allow LTPlabs to deliver solutions to its clients using AIR. This is achieved through:
That’s what, in today’s world, has come to be expected from digital products. Failing on any dimension means failing in the digital transformation of our clients. Achieving these results for the LTPlabs’ approach is the focus of our Tech Team.
Another key responsibility within this team is creating new systems that also abstract typical software development complexities.
Thus, largely reducing our time to market and making advance analytics available in the palm of your hand.
Experience told us that fancy architectures are just void of a principle without quality algorithms and pipelines that can explore their scale; great and complex mathematical engines fail to deliver value if they are not presented in intuitive ways through well thought of UI; support gets chaotic, and improvements get dead on their tracks without great integration and DevOps practices. With all of that, user adoption withers and customers walk out the door.
The joint efforts of both teams are already reaping benefits:
AIR aims to be the main accelerator for full analytical and digital democratization.
A stable version of the Sales Forecasting Module, an Inventory Management Module, and a platform to quickly deploy solutions to clients have already proven that digital delivery is the path forward.
The ever-growing amount of new business problems lurking on the horizon creates an unsettlement attitude of aspiring for more. Our vouch is to keep broadening AIR’s product range and make it emerge as the de facto alternative to those pesky broad-yet-monolithic-mega-systems and those ultra-specific-impossible-to-maintain-tools that are straining companies now.
By: André Morim , João Alves , Luís Guimarães
