Intro

We frequently use the term “full-stack AI team” throughout our website, but what does the term really mean? We get this question quite a bit, so we wanted to take a moment to answer the question both in a larger sense, and what it means at Synergise AI.

In the software development world, “full-stack” means you have a team of engineers focused on developing the front-end (user interface, “UI”), application layer, and back-end. The team approaches the product from every angle to ensure a sound, seamless customer experience as a result. 

At Synergise AI, we extended the term for artificial intelligence (AI) to embrace all the tech, steps, and roles that might be needed for a project. 

The Tech

As you likely know, the universe of AI tech is broad and deep. It includes open source and commercial offerings and covers everything from:

• Cloud platforms such as AWS, GCP and Azure each of which has dozens or hundreds of services providing different technologies to support AI projects.
• Tools and frameworks such as Pytorch, TensorFlow, HuggingFaces, etc.
• The above platforms and frameworks provide models and analytical tools. In addition, targeted products for data management (DataBricks), MLOps (Weights & Biases), etc.
• Software engineering platforms, applications and frameworks such as Atlassian, GitHub and so on.

The Roles

The roles required for each AI project may differ based on the desired outcome. For example, if you’re doing a data assessment, you’ll want to work with mostly data scientists, however, if you’re doing an integration, you’ll want to leverage software engineers. For a data engineering project, you’ll need data engineers and for testing, you’ll need a QA team. 

The Steps

To put something into production, you might first need Development Operations, then Machine Learning Operations, and then another separate team in a different order. It all depends on the project.

That’s why when putting together a full-stack AI team to help with your AI challenge, we focus on using Agile for AI methodology to ensure we have the right expertise, roles, and team for each step of the process to achieve your desired AI goal.

When should I engage a full-stack AI team? 

The best time to begin engaging a full-stack AI team is during an AI opportunity evaluation. (Get our free AI evaluation guide here.) By bringing in a full-stack AI team from the start, you’ll be able to leverage the diversity of the team’s expertise to map the best way forward to ensure an efficient and cost-effective buildout to achieve your end goal. 

The only time you might not need a full-stack team is when you have some of the roles already covered. In this case, select just the specialists you need. Additionally, should you need the team to be fully on-site, that’s not something Synergise AI can accommodate at this time. 

That said, you can bring in a full-stack AI team at any point in your project to help remove blockers, bring different skills and thinking to your project, and finish the project strong. 

‍How does collaboration work with my current in-house engineers? 

At Synergise AI, when you engage our full-stack AI team, they join your team of in-house experts. The collaboration between your in-house team and Synergise’s full-stack AI team is critical because while our team has AI expertise, they don’t have the subject matter expertise in your business, nor the client-specific information, to make your project work for your business on their own. Your team’s company-specific expertise combined with our team of AI experts is one key aspect of what makes our AI projects so successful. 

To our partners, Synergise AI can be white-labeled as part of your team if you’re interfacing with other clients. Our team can help you scale to meet your client demands, getting the project done on time and with the desired outcome. 

How much does a full-stack AI team cost? 

Unfortunately, there’s no easy blanket answer here. The cost of hiring a full-stack AI team depends on the nature of the work, the duration of the project, and the type of project. Projects that require more time, are more complex, and/or require more diverse roles will be more expensive than simpler, faster projects. 

For example, a simple data assessment might prove to be less expensive than an AI implementation project, as only certain types of engineers are required and the time needed to complete the assessment may be less than the time needed to complete a more complex AI implementation. 

Conclusion

A full-stack AI team brings together a diverse set of skills and expertise to navigate the complexities of AI projects. Understanding their roles, collaboration dynamics, and pricing considerations is essential for harnessing the full potential of AI in your projects. By engaging a full-stack AI team at the right time and leveraging their strengths effectively, you can pave the way for successful AI implementations. 

For more information on everything AI implementation, check out our growing guide here.

If you’re interested in learning more about Synergise AI’s full-stack AI team for your project, please don’t hesitate to send us a note here.