From idea to Agentic AI solution

Each AI-related project is a journey into a bit of the unknown. It is a learning experience with a magnitude of unknowns in the process, which range from technical feasibility to real value delivered with automation.

That is why at Vstorm, we work according to a specific method that spans the entire purchase process — from the moment we first get in touch with our potential customers to the moment we wrap up implementation projects, delivering either a single AI agent or an entire agentic framework.

How to deal with the unknowns of AI adoption?

 During our conversations with customers, it is uncommon to face questions such as:

• Is the AI agent I need technically feasible?
• Would running it be economically viable?
• How may it help me to differentiate from the competition?
• What could be the long-term systemic impact on my team?

To answer them, we blend decades of experience in IT solutions deployment from Agile to Scrum with risk-preventing measures necessary when working with novel, cutting-edge technologies. In this hotbed of a field, solutions, and technology tend to evolve as fast as customer expectations.

Thanks to the methodology we work with, we’re able to manage expectations across the entire scope of engagement, from proposal to deployment of custom solutions.

At Vstorm, we manage customer expectations by blending low-level expertise with high-level business advisory and entrepreneurial expertise. This allows us to combine what businesses ask for with what technology can reliably deliver.

How to assess the value of a future AI solution 

When planning new projects, we look into three areas to assess the value of future AI solutions:

• Technology — to verify the feasibility of deploying AI agents 
• Economy — to measure key performance indicators and cost vs savings,
• Differentiation — to capture factors that will impact business that can’t be easily quantified

Our methodology helps us to keep focus on each of the three areas without losing one from sight when evolving the other. It also curves expectations when some answers come later in the process — such as those related to differentiation. Instead of taking things that are unknown from thin air, we point to the moment in time when any meaningful assessment will be possible — and with it, we manage customers’ expectations.

There’s a lot that could be said about how we work. That is why, if you want to learn more about it before engaging in any AI project with us, feel free to download our one-pager or contact the author