Product management
Define the problem, shape an early design, and use AI to create a first-pass prototype for the team to evaluate.
AI-first technical staffing
Experience
Runs Deep.
Thirty years of technical excellence.
A new standard for applied AI capability.
30 years of technical excellence
A trusted Utah partner.
A new standard for AI capability.
For 30 years, SolutionStream has helped Utah technology leaders solve complex problems and build teams they can trust. That experience runs deep—in the people we place, the judgment they bring, and the standard of excellence we expect from their work. Our Certified AI Practitioner program carries that standard into the next chapter of technical delivery.
AI-first staffing begins with experienced people who understand your business. Training expands their capabilities; professional judgment determines how those capabilities should be used.
The evolving delivery team
Broader capabilities.
Fewer handoffs. More momentum.
Software development
Use AI to explore approaches, accelerate a first draft, and generate tests—then apply engineering judgment to review and refine the work.
Quality assurance
Use AI to expand test scenarios, investigate patterns, and strengthen coverage while keeping human accountability for quality.
Examples of broader contribution. Role scope and approved AI tools are aligned with each client engagement.
Certified AI Practitioner
The training behind
the badge.
We require every SolutionStream staffing employee to earn AI certification. Our program is designed to connect AI knowledge with the work clients actually need: understanding a problem, selecting an appropriate tool, developing a useful first pass, and evaluating the result before it becomes part of your business.
The program is designed around six areas of capability. Together, they connect technical expertise, applied AI skills, and responsibility for the final work.
01
AI foundations and tool selection
Understand capabilities, limitations, and appropriate use cases. Choose tools based on the task and the client environment.
02
Prompting and context
Translate a business problem into clear instructions. Supply relevant context and refine the approach when the first result misses the mark.
03
Application within a discipline
Apply AI to realistic work in product management, engineering, QA, design, or analysis. Core professional expertise remains the foundation.
04
Verification and judgment
Check accuracy, test assumptions, review generated code and designs, and recognize when specialist review or escalation is needed.
05
Responsible use of client information
Work within approved tools and data policies. Recognize sensitive information and understand when AI use is inappropriate.
06
Broader contribution and continued learning
Identify opportunities to support adjacent delivery tasks. Keep learning as tools evolve while maintaining clear scope and accountability.
Confidence through demonstrated capability
The badge should stand for more than attendance.
Our certification approach is built to move from learning to application. The goal is a professional who can explain their choices, show useful work, and recognize the limitations of an AI-assisted result.
01Learn the foundations
Develop a shared understanding of tools, responsible use, and the professional judgment needed to use them effectively.
02Apply the skills
Practice on role-relevant tasks that connect AI capability with real delivery challenges.
03Review the work
Evaluate the output, explain how it was checked, and identify where additional review is needed.
Your staffing augmentation partner
What your team should expect.
Depth before breadth
Start with the discipline and experience your role requires. AI capability builds on that foundation; it does not remove the need for qualified technical talent.
Capability matched to your work
Discuss the professional's relevant AI experience, the tasks they can support, and where specialist oversight belongs before expanding their responsibilities.
Respect for your environment
Align AI use with your tools, access controls, data requirements, and delivery process. Your organization's standards remain part of the engagement.
A broader contribution, with clear accountability.
A product manager may use AI to explore a design and create a first-pass prototype. An engineer may use it to generate tests or investigate alternatives. In each case, the work still needs appropriate review, clear ownership, and alignment with your team's expectations.
What this means for your team
How does the certification build confidence in the people we hire?
It creates a shared expectation for applied AI knowledge alongside the professional's core expertise. Ask about the candidate's role-specific application, review practices, and fit with your environment—not just whether a badge appears on their profile.
How do we know their AI skills fit our role?
Use the staffing discussion to define the work, approved tools, expected contribution, and review requirements. A general AI credential should be considered alongside the person's discipline, experience, and relevant examples.
Does AI certification replace experience?
Experience remains the foundation. Certification adds a shared expectation for practical AI capability and responsible use.
Does one person take on every technical role?
Each professional retains a core discipline. AI can broaden their contribution, while specialist review and clear ownership remain part of delivery.
Can AI tools be used in our environment?
Tool access and use must follow your organization's policies, security requirements, and engagement scope.