AI products · Event-driven value
Applied AI and computer vision
Turning machine-learning and computer-vision signals into useful customer experiences and business workflows.
My approach
I have built applied-AI products that connect recognition and behavioral signals to event-driven cloud services, customer workflows, and measurable business intent rather than stopping at a technical demonstration.
Focus
In practice.
Problem-led AI
Start with the customer or operational decision that the model output should improve.
Signal-to-event design
Translate recognition and behavioral observations into events that downstream services can understand and act on.
Cloud integration
Connect AI capabilities with APIs, Functions, data stores, queues, and web or mobile touchpoints.
Responsible product boundaries
Keep identity, consent, accuracy, review, and business relevance visible as product-design concerns.
Evidence
Relevant experience.
Cardless loyalty recognition
Developed face-recognition capabilities that allowed a loyalty experience to identify participating customers without a physical card.
Behavior connected to workflow
Modeled customer presence and product interest as events that could trigger relevant downstream follow-up.
Event-driven Azure services
Connected computer vision with Functions, API Management, Cosmos DB, Azure SQL, queues, and blob storage.
Read the computer-vision case studyWhere it helps
Problems this
experience addresses.
- Applied-AI concepts that need a credible path to business value
- Computer-vision signals feeding event-driven products
- Retail intelligence and customer-workflow integration
- Teams connecting model output with cloud and product architecture
Related work
Related work.
Related disciplines
- Applied AI
- Computer vision
- Face recognition
- Retail intelligence
- Event-driven architecture
- Azure
Contact
Start a conversation.
If my work is relevant to something you are building, I would be glad to hear from you.