Data, product & AI systems
Shawn Ganz.
Designing the conditions
for useful work.
Shawn’s work spans civic data products and human–agent collaboration. At NYC Parks, the problem is turning spatial data into usable planning tools. In Corpus, it is giving AI agents a working environment where responsibility, evidence, and unfinished work can survive a session.
The common thread is system design: understand the real decision, bring the right information together, and build a process people can inspect and improve.
Professional profileSelected work
Two settings.
One design practice.
Corpus
Give agent work
a life beyond the session.
- Contribution
- Shawn originated the project and shaped its product choices. Agents co-designed, built, investigated, and reviewed.
- Observed result
- Continuing obligations survived handoffs; review caught an omission before work was judged complete.
Civic data
Connect analysis
to a city’s decisions.
- Contribution
- Data & product design at NYC Parks, including a tree planting model and collaborative support for canopy data work.
- Public result
- A model presented at NYC Open Data Week and a contribution acknowledged in The Nature Conservancy’s practical canopy release.
What carries across
From domain knowledge
to system design.
A portfolio reading of the two bodies of work.
| Practice | Civic data | AI collaboration |
|---|---|---|
| Problem framing | Turn a city planning question into a decision model. | Turn an agent failure into bounded, judgeable work. |
| Data & source integration | Combine spatial datasets while preserving the meaning of each input. | Connect work records, source state, and reviews through a recoverable trail. |
| System boundaries | Keep data inputs, assumptions, and use distinct. | Separate current state, obligations, making, and review. |
| Operational design | Connect spatial analysis to the people using it. | Design handoffs and continuation around actual working conditions. |
| Evidence & iteration | Make the model’s basis and limits legible. | Measure friction, inspect candidates, and preserve corrections. |
Looking toward AI development
Bring the work
into the conversation.
Shawn wants to bring this practice into AI development: framing useful problems, designing the working environment, and testing whether the structure earns its cost.
Start with a concrete question: what should an agent leave behind so someone else can judge and continue its work?