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Best of Both Worlds: Agency and AI Products

Human Shaking Hands with Robot

We’ve talked previously about some of the reasons we believe companies are struggling to understand or successfully adopt AI technologies. These include data-or tech-centric approaches, a lack of experienced personnel, or a lack of integration into the executive team. Over the years helping firms adapt analytics and AI technology, we’ve seen many firms succeed and fail, and have tried to design a better approach. We recognize that of AI-aware organizations, only 20% have adopted this technology at-scale, and only half of them report success with it. That means that only 10% of AI-aware organizations have successfully deployed AI technology. And there’s no reason to commit to AI with all the certainty of buying a lottery ticket.

We’ve been using the agency model in both the web and application design, as well as emerging technologies and analytics for many years. At its core, this model is focused on partnerships with clients, rather than projects. One-off projects delivered expertly are an important component to this model, but they fall under a broader strategy. Without that broader strategy, these individual projects can struggle to be successful for several reasons:

  1. A lack of understanding of corporate pain points and business objectives. Without this partnership between technology and business, even well built and designed applications can fail at their most important task — delivering value to a business.
  2. A lack of focus on user experience. This is a broader problem than just design, although it encompasses that too. In this context, user experience refers to the larger “brand” experience that a user encounters across their entire customer journey. From first touch in the sales and marketing process through onboarding and ongoing service, it’s important to create experiences that continually address customer needs. The agency partnership allows us to create insight loops to inform this process.

In the analytics space, we often see firms struggling to understand their data in the context of their business — what are the right questions to ask of the data, and what are the pitfalls to avoid? What would be possible with some changes to the data they’re collecting, or how they’re collecting it? Without an integrated partnership, technology and analytics firms will struggle to help these companies, because their understanding will remain superficial.

We have found that deeply integrated partnerships with clients mean that we can better understand the challenges they face, internal company dynamics, their value proposition to their clients, and the workflows that they rely on every day. It provides the context for the projects we deliver to them, which leads to a greater value from what is delivered. It also lets our clients scale without hiring anyone — through us, they can gain access to experts across multiple domains.

We have found that deeply integrated partnerships with clients mean that we can better understand the challenges they face, internal company dynamics, their value proposition to their clients, and the workflows that they rely on every day.

With Urvin, we are bringing this approach to AI, and pairing it with a unique set of proprietary technologies. Our decades of experience in the analytics and AI space have led to some novel approaches to time series analysis, natural language processing, deep learning and complex systems analysis. This led us to create a hybrid model that combines the best of the agency world with a core product offering that sets us apart from others who solely rely on open-source or cloud-based systems (we think cloud providers have one goal, which is to sell more cloud computing and storage resources — not to increase client productivity or deliver unique insight).

A combination of agency agency and product

Urvin’s unique hybrid approach is not only a hybrid of product and agency, but also a hybrid of two historically separate parts of the development process — frontend and backend.

For the backend side of our company, as software developers and computer scientists, most of our exposure to agency firms historically has involved graphic and front-end design. We found that the most successful application development projects ordinarily involved a strong design agency, partnered with the client, who worked with us as backend developers. We grew to appreciate the importance of two major success factors, aside from the client partnership discussed above:

  1. User interface and experience (UI/UX) as a foundation for any project, and
  2. A well-integrated team that can offer full-stack development.

While we have always worked well with our agency partners, there are far more examples of fragmented teams tasked with building applications in which the design process is an afterthought, is driven by the software development team, or is disconnected from, rather than integrated with, the client firm. When we developed the model for Urvin, we focused on a different approach that would ensure successful client partnerships and long-lived relationships. This approach would integrate the frontend and backend development teams, giving us full-stack development capabilities and ensuring that UI/UX is the foundation for everything we do.

AI Thoughts + Insights

Case Study
Product Classification for a Global Tax Software Company
Helping one of the largest e-commerce platforms in the world figure out if there was an automated, scalable way to classify the product to collect the appropriate sales tax.
Product Classification for a Global Tax Software Company
Case Study
Legal Document Management and Robotic Process Automation
Urvin was approached by a Fortune 500 company whose in-house legal team was struggling with the volume of legal contracts that they had to review.
Legal Document Management and Robotic Process Automation
Solution-Centric AI
We partner with our clients to understand the problems that they’re facing, and what kind of solution and experience would help them leverage the value in the data that they have.
Solution-Centric AI