Build a student AI lab. Turn curiosity into capability.
Give students a place to explore, build, and learn with AI. I help your institution set up a practical pilot lab using low-cost or open-source tools, then mentor your team so they can put it to work. You work with me directly, side by side, from the first conversation to the handoff.
- You work directly with me, not a rotating vendor team
- Low-cost or open-source tools, set up with my tested scripts
- Built around your students, equipment, and goals
- Mentoring alongside your faculty, staff, and student leaders
Bridge AI theory and real-world practice.
Based on my Student Lab for AI Learning whitepaper, this offer helps educational institutions create a collaborative space where students and staff can experiment with AI, develop practical skills, and think critically about how it should be used.
The approach is modular: begin with a focused pilot and suitable existing hardware, learn what works for your students, and use that experience to guide future investment. Students across disciplines can participate, with activities matched to their starting point.
A good fit if this sounds like your school.
Every lab is shaped around the institution. If a few of these describe you, we should talk.
- You want students learning AI by doing, not only reading about it.
- You’d rather start with a focused pilot than a large up-front purchase.
- You have an engaged faculty or program sponsor who wants to lead it.
- You have an IT contact who can help with access and installation approvals.
- You want your own people able to run and grow the lab after the sprint.
The setup gets you started. The mentoring helps you move forward.
This isn’t a software install followed by a goodbye email. I work hands-on with your people, in your environment, until they’re comfortable on their own.
Make the first decisions easier
I connect your learning goals, available equipment, and technical constraints to a manageable starting scope, so your team can spend less time sorting through tools and more time learning.
Build skills alongside the lab
Faculty, staff, and student leaders learn through guided work in the environment we configure together. We discuss how it works, where it falls short, and how to evaluate its output.
Leave with a foundation to grow
A documented setup, the scripts that rebuild it, a starter project, and practical next steps help your team continue experimenting and make informed choices about what to expand.
Maintained, not just installed.
Plenty of AI pilots work on day one and quietly stop working a few months later. I bring experience from my own local AI work, along with the setup scripts and tool configurations I use, so your lab is repeatable and easy to keep running after I leave.
- Ollama
- Open WebUI
- AnythingLLM
- Jan
- LM Studio
We choose the right mix for your goals and equipment during the free assessment. As better tools appear, the mix can change.
- Low-cost or open-source tools and openly available models, with no long-term license commitments.
- Setup scripts that reinstall or rebuild the lab when a computer is reset or replaced.
- Saved tool configurations, so every machine is set up the same way.
- Simple steps for adding computers, updating tools, and trying new models.
- Plain-English notes for common fixes, written for your team and your IT contact.
From an idea to a working pilot.
We agree on the pilot environment, participants, mentoring schedule, and a realistic starter project before the sprint begins. It all starts with a free assessment conversation.
Choose, configure, and get running.
- Clarify learning goals and assess available hardware, access, and IT requirements.
- Select and configure a focused set of low-cost or open-source tools and suitable models for the agreed pilot environment, using my tested setup scripts.
- Walk your team through basic operation, model interaction, and initial experiments.
Work through a project together.
- Guide the team through a starter project connected to your program’s interests.
- Mentor participants on testing outputs, recognizing limitations, and responsible use.
- Document the setup, hand over the scripts and configurations, review what we learned, and map the next stage of the lab.
What could your students explore?
We choose a starter project that fits your participants and hardware. These examples show possible directions; the sprint focuses on the project we agree on.
Ask your own documents
Explore how AI retrieves information from a small collection of approved learning materials, and examine whether answers are supported by the sources.
Compare model responses
Try the same task with different local models. Discuss accuracy, usefulness, bias, and the tradeoffs between model size and available resources.
Build a useful workflow
Prototype a simple research or text-analysis workflow. Learn where AI can help, where it makes mistakes, and where human review belongs.
A focused sprint with a clear price.
You’re paying for my time with your people: setup, guidance, and mentoring. Scope is agreed before work begins, so there are no surprises.
Technical setup, hands-on mentoring, and a practical handoff for the agreed pilot scope.
Talk With Jorge About Your Sprint →Keep the momentum going.
Extend on a weekly basis as needed for additional mentoring, new projects, faculty enablement, troubleshooting, or the next stage of your lab. Each extension’s focus is agreed before it begins, and travel expenses apply if you prefer on-site sessions.
The pilot uses low-cost or open-source tools selected for your institution’s intended use. You are paying for my setup, guidance, and mentoring. Hardware, paid software, and cloud or API usage are separate. If you prefer on-site sessions, travel expenses are added at cost and agreed in advance. Final pricing depends on scope.
A few practical questions.
Work directly with Jorge Pereira
I bring experience across technology consulting, AI adoption, local and hybrid AI, and hands-on development, along with the setup scripts and tool configurations I use in my own local AI work. My role is to connect the technical setup with the people who will use it, helping your team turn an initial idea into something they can learn from and build on.
Who is this for?
Colleges, universities, schools, and education programs that want a practical starting point for a student AI lab. It works best with an engaged faculty or program sponsor and an IT contact who can help with access and installation approvals.
Do we need to buy new hardware?
We first assess what you already have and match the pilot to equipment that can support it. Existing computers may be sufficient for a modest starting scope. If they are not, we discuss the options before committing to the sprint. Model size, performance, and simultaneous users depend on available resources.
What does “low-cost or open-source tools” mean?
The core pilot is built on tools that are open source or free to use for your agreed purpose, such as Ollama, Open WebUI, AnythingLLM, Jan, and LM Studio, paired with openly available models. Some are fully open source; others, like LM Studio, are free to use but not open source. We check suitability and usage terms during scoping. This does not mean operating the lab has no costs: equipment, electricity, IT support, and any optional paid services still need to be considered.
How do privacy and responsible use fit in?
We start with approved, non-sensitive materials and discuss data handling, permissions, model limitations, and human review. Local deployment can support greater control over data, but the actual configuration matters. Your institution retains responsibility for its policies and approval of student access and data use.
What happens when something needs fixing or updating?
Your team keeps the setup scripts, tool configurations, and plain-English notes from the sprint. If a computer is reset, you add machines, or a tool needs updating, your IT contact can follow the steps without starting over. If you want help, weekly extensions or ongoing support can be scoped separately.
Do you work on-site or remotely?
Both. Much of the mentoring works well remotely, and hands-on setup often benefits from time on-site with your team. We agree on the mix during the free assessment. If you prefer on-site sessions, travel expenses are added to the sprint price at cost and agreed in advance.
How large is the sprint?
This is a focused pilot engagement. The number of devices and participants, session schedule, delivery format, and project complexity are agreed before work begins. Larger rollouts, advanced model training, ongoing administration, and additional projects can be scoped separately.
Start with a free assessment.
Tell me about your students, your goals, and the equipment you have. We’ll talk through what a realistic pilot looks like for your school, with no obligation.
Looking for other ways to bring AI to your school? See all Schools & Education offers →