GenAIFuel
Knowledge design for production AI

Well-designed knowledge
is the fuel for GenAI.

Most AI projects stall not because the models are bad, but because the foundation is. I design knowledge architectures, voice agents, and AI experiences engineered to perform from the first real user, not just the demo.

See what I buildStart a conversation
10+
Years in product
4
AI specialties
RAG
Native architecture
0
Vaporware delivered
Services

Four things I do exceptionally well

Not a consultancy. Not an agency. I work directly with founders and product teams on the specific problems where deep AI expertise makes the biggest difference.

Foundation

Knowledge Base Design

The difference between an AI that answers correctly and one that confidently hallucinates is almost always the knowledge layer. I design retrieval architectures, content taxonomies, and chunking strategies that give your AI the right information at the right time.

  • Retrieval-augmented generation (RAG) architecture
  • Content taxonomy and metadata strategy
  • Chunking, embedding, and indexing optimization
  • Knowledge quality audits and gap analysis
Conversation

Voice Agents

Phone and web-based voice agents that handle real conversations, not just the easy ones. From intent design to fallback flows, I build agents that hold up under the pressure of actual users with actual problems.

  • Conversational flow and intent architecture
  • Real-time speech-to-text and TTS integration
  • Fallback handling and escalation logic
  • Ongoing tuning based on conversation data
Presence

Synthetic Video Avatars

Cloned and AI-generated video presenters for training content, onboarding, customer communications, and more. Scale your message without scaling your production costs.

  • Voice and likeness cloning for consistent brand presence
  • Script-to-video pipelines for rapid content production
  • Multilingual localization without reshooting
  • Integration with LMS and content delivery platforms
Learning

AI Coaching Platforms

End-to-end platforms that combine personalized AI with structured learning paths. Built for engagement and measurable behavior change, not just completion rates.

  • Adaptive learning path architecture
  • AI coach persona design and prompt engineering
  • Progress tracking and personalization logic
  • Integration with existing HR and LMS systems
How I work

Built by a product person who actually builds

I came up through product management, which means I think about user needs, delivery constraints, and what "done" actually looks like. AI is the tool, not the point.

01

Foundation first

Every AI project I take on starts with the knowledge layer. What does the system need to know? Where does that content live? How fresh does it need to be? Getting this right before writing a single line of agent code is what separates working systems from expensive prototypes.

02

Ship, then iterate

I have a product management background. That means I think in terms of working software, not decks. I scope things so they can go live, then improve based on what real usage reveals. You get momentum and you get learning, not a 6-month design phase.

03

Build for the edge cases

Happy-path demos are easy. The hard work is designing for the user who asks the question you didn't expect, the caller who rambles, the learner who falls behind. I spend disproportionate time on the scenarios that break things.

Recent work

Products I've been shipping

Voice collaborators, neighborhood tooling, and multi-agent systems — each one built to hold up outside the demo.

Contact

Let's figure out if I can help

Tell me what you are working on and where you are stuck. I will let you know honestly whether it is something I can add real value to.