Custom AI Training
"What if your team could go from AI-curious to AI-capable — and actually ship something real?"
The Gap Is Real
45% of companies have paid AI subscriptions. Only 12% are using AI in their operations. The problem isn't access. It's capability. nvisia has been closing this gap from the inside out — first with our own teams, now with our clients.
Have AI Subscriptions
Companies paying for AI tools today
Actually Using AI
Companies using AI in real operations
Feel Overwhelmed
Employees anxious about AI's implications
Are AI Proficient
With skills to capture real value
Source: Section AI Proficiency Report, 2026
Our Approach: Build, Don't Just Learn
The most common mistake in AI training is treating it like a classroom. Slides. Concepts. Teams leave with vocabulary but no capability. nvisia's training works differently — we run it like a consulting engagement: structured problem, architectural design, hands-on build, present and defend.
1. Ground the Room
Brief foundations so everyone has a shared mental model, regardless of prior experience. AI landscape, how models work, where the market is headed.
2. Design It
Architecture Katas. Small groups take a real-world problem, design an AI solution, present and defend their architecture. This is the moment that changes how people think about building with AI.
3. Build It
Starter GitHub repos, Claude API access, real-world datasets. Teams build working AI solutions in a 2–3 hour session. Not a proof of concept. Working code.
"Today is 70% hands-on. You'll build something real — not just a slide deck."
Training Formats
Every engagement is shaped to your team's starting point, role mix, and goals. These are the formats we've run and refined.
Lightning Talks — 15 min
High-density focused sessions: Claude and RAG pipelines, AI in the SDLC, Responsible AI and data guardrails, agentic architecture patterns. Ideal as conference tracks, lunch-and-learns, or engagement kickoffs.
Architecture Katas — 75 min
Short, focused design exercises for the agentic era. Small groups receive a real-world AI problem, design a solution architecture, sketch a diagram, present, and defend. Builds architectural thinking without writing a line of code.
Full-Day Curriculum
Role-specific modules covering AI foundations, model selection, agentic AI, multi-agent system design, system prompts, and AI product architecture. Delivered for engineers, PMs, and technical architects. Measured +25% average confidence lift in our PM cohort.
AI Retreat / Build Day
Immersive full-day session. Morning: foundations + architecture patterns + kata. Afternoon: hands-on build with starter repos and real-world datasets. Teams ship working AI solutions the same day. Evening: present, defend, and celebrate.
Hackathon Follow-Up — Half Day
~4 weeks after a full-day or retreat, teams return to ship a real AI artifact tied to an actual engagement. Reinforces learning through applied stakes. Requested by every cohort we've run.
Who We Train
AI training is not one-size-fits-all. Different roles need different skills. We tailor every engagement to the people in the room.
Software Engineers & Architects
- AI-assisted development workflows
- Spec-driven development
- RAG pipeline construction
- Agentic system design
- Multi-agent architectures: Centralized, On-Demand, Stateful, Parallel
- Guardrails and validation
Tools: Claude, GitHub Copilot, LangChain, Claude Agent SDK
Product Managers
- How LLMs work without the jargon
- Identifying AI product opportunities
- Writing system prompts and AI feature specs
- Evaluating AI output quality
- Collaborating on AI architecture
- Discussing trade-offs: cost, latency, privacy
Biggest gains: +38% on identifying AI risks · +38% on architecture collaboration
Technical Architects
- AI architecture patterns: RAG, agentic loops, multi-agent
- AI in all six phases of the SDLC
- Discovery → Design → Development → QA → Operations → Client Delivery
- Responsible AI policy and data guardrails
We Measured the Lift
We ran pre- and post-training surveys and measured real capability changes. nvisia AI Product Management Training — May 2026. Matched-pair pre/post analysis · n = 9
Avg. Confidence Lift
Across 10 PM-AI capabilities. No skill regressed.
Would Recommend
Every participant would recommend this training to a peer.
Rated Valuable+
Rated Valuable, Very Valuable, or Extremely Valuable.
Will Apply Skills
Likely or very likely to apply skills with clients.
Identifying AI Risks & Failure Modes
Architecture Collaboration with Engineering
Explaining LLMs to Stakeholders
Discussing AI Trade-offs
What Participants Said
Real feedback from the May 2026 cohort — unedited, unfiltered.
"The group activities really tied things together and the size of the groups were perfect."
— Lynn Ugent
"The deep dive into how agents work."
— Michael Arce
"Really good background information on AI in general. It helped to solidify all the concepts really well."
— Michael McNutt
What You Actually Build
We use real, well-scoped problems sized right for AI and the time available. Teams receive starter GitHub repos, Claude API keys, and sample datasets. The repos contain meaningful structure but require teams to fill in the key architectural pieces. The point isn't to complete the repo — it's to grapple with real AI architecture decisions under time pressure.
Automated Content Moderation
Multi-turn classification with edge cases. Teams design and implement a system that handles nuanced, ambiguous content at scale.
Contextual Chatbot
RAG-backed conversational agent with grounding. Build a chatbot that retrieves and reasons over real documents rather than hallucinating answers.
Automated Claims Adjusting
Document ingestion and decision support. Ingest unstructured insurance documents and surface structured recommendations for adjusters.
Processing SEC Financial Submissions
Structured extraction from unstructured filings. Parse dense regulatory documents and extract key financial signals reliably.
The four problems from our 2026 AI Retreat — all ready for external delivery with real-world datasets and nothing nvisia-specific in the repos.
Honest Findings
We've run this enough to know what's true. Here's what we've learned — no spin.
It Always Becomes Custom
Every organization ends up wanting training shaped to their problem space, their team's starting point, and their tools. We plan for that, not against it. Generic AI training produces generic results.
The Materials Are Ready
The GitHub repos we use contain nothing nvisia-specific. The datasets are real-world. With modest preparation, they're ready for external delivery. You're not waiting on us to build something from scratch.
One Day Is the Starting Line
A single day moves the needle — we've measured it. Organizations that see lasting impact build on it with hackathons, use case libraries, and a continuous learning structure. The training opens the door. What follows determines what comes in.
The Proficiency Bar Keeps Rising
The gap between experimenter and practitioner is widening as AI capabilities advance. Build continuous learning infrastructure now, not a one-time event. Create clear progression paths from basic → intermediate → advanced use cases within each function.
Talk With Brandon
Brandon Phillips
Client Partner · nvisia Cross-Regional
Brandon designed and led nvisia's Technical Architects AI Retreat in May 2026 — a full-day immersive build session where teams designed and shipped working AI solutions. He delivers lightning talks on Claude and RAG pipelines and leads "Exploring the Boards: AI in the SDLC," a gallery walk through all six phases of software delivery and where AI integrates at each one.
Brandon works with organizations across the Midwest helping them go from AI-aware to AI-capable — and understanding what that actually requires.
At the AI Lab
June 24, 2026
Loramoor B, Lower Level
Grand Geneva Resort & Spa
12:30–3:30 PM
Ready to train your team?
Every engagement starts with a conversation about where your team is today and where you need to be. No pitch deck required.