Useful AI.
Inside a real workflow.
Shipping LLM-powered content-generation automation for a tourism application, supported by Python workflows and AI coding tools.
Tourism content generation
Built and shipped automation
LLMs · Python · AI coding support
Tourism content,
with less manual work.
The workflow being automated was content generation for a tourism application. I built and shipped an LLM-based solution, supported by Python automation and AI coding tools.
This is applied product work: bringing AI into a concrete workflow rather than treating the model itself as the product.
The release is
part of the product.
Separately from the content-generation solution, I designed AI-assisted software delivery across product planning, architecture, development, QA, and release acceptance for Unity mobile apps, websites, and analytics.
- Established Jira and Confluence requirements, dependencies, estimation, and reusable runbooks with ownership and approval gates.
- Automated mobile build, deployment-to-device, and evidence collection while preserving traceable artifacts.
- Kept software release decisions under human control.
- Developed telemetry validation to distinguish production usage from tests and detect missing data.
These are related delivery capabilities, not a claim that the content system uses a particular architecture or evaluation framework.
Define the work
Requirements, dependencies, and ownership.
Build and test
Implementation, device evidence, and QA.
Accept the release
Traceable artifacts and human-controlled decisions.
A reconstruction of the related software delivery process, not the LLM content architecture or a content fact-checking system.
From possibility
to shipped capability.
This work connects product ownership with practical implementation: identifying a repeatable workflow, shipping automation, and building structure around delivery.
The content solution has shipped. Model providers, system architecture, quality checks, deployment scale, and quantified efficiency gains are not documented in this case study.
Hear the content.
See the experience.
Space Needle and the Edgewater Hotel.
English narration · 42-second project demo.
Read the narration +
Space Needle. On your right is the Space Needle, built for Seattle’s 1962 World’s Fair. At 605 feet tall, it brings that fair’s story into the city’s skyline. Its observation deck offers a sweeping perspective on Seattle and the surrounding region.
Edgewater Hotel. On your right is the Edgewater Hotel, which opened on Seattle’s Pier 67 in 1962. Its waterfront setting became part of Beatles history just two years later. During their 1964 Seattle visit, the band stayed here and famously fished from a hotel window.
A good product starts
with a conversation.
Product leadership, AI workflows,
and ambitious startup opportunities.