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AI & Agentic
De-Risking Public Sector AI Innovation with Sandboxes: Zaizi’s Approach
Zaizi ran an AI sandbox hackathon to prove public-sector teams can prototype and validate AI safely and early — before procurement locks choices in.
Days
from idea to working AI prototype
The challenge
Public-sector safeguards around data, procurement and stability push AI experimentation late, so ideas stay conceptual and risk surfaces downstream when reversal is expensive.
How it ran on NayaOne
1
Safe sandbox environment
A secure, operationally realistic space where teams could build and prototype AI quickly without touching production systems or real data.
2
Rapid hackathon build
Teams tackled real operational challenges hands-on, moving from concept to working prototype inside the sandbox.
3
Early, evidenced validation
Ideas were tested against realistic conditions up front, so risk was understood before vendors or technologies were selected.
Outcomes
Early
validation before procurement, not after
Safe
experimentation with no production risk
Faster
path from concept to evidenced decision
More case studies
AI & Agentic
De-Risking Public Sector AI Innovation with Sandboxes: Zaizi’s Approach
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AI & Agentic
De-Risking Public Sector AI Innovation with Sandboxes: Zaizi’s Approach
Read case study →
AI & Agentic
De-Risking Public Sector AI Innovation with Sandboxes: Zaizi’s Approach
Read case study →