Privacy
Private delivery evidence, governed use.
DropScore is designed to minimize sensitive data exposure while helping last-mile operators understand verified delivery friction.
Minimize raw identifiers
Tenant isolation by default
Redacted learning scopes
Collected
Operational evidence only
Operational delivery events such as arrival, contact attempts, failed access, completion status, package context, tenant metadata, and approved action outcomes.
- DropScore should not require driver-worn cameras for the core product.
- Customer, driver, address, and tenant identifiers should be scoped, hashed, minimized, or redacted wherever possible.
- LLM assistance must use governed evidence packets, not raw operational records.
Protected
No raw cross-tenant pooling
Raw customer details, driver details, exact coordinates, arbitrary free text, tenant secrets, private notes, photos, and tenant-identifying data are not appropriate for cross-tenant learning.
Cross-tenant learning is off by default and should require explicit tenant opt-in with privacy-preserving scopes such as aggregated features or redacted examples.
Controlled
Reviewable retention and access
Production deployments should include tenant-specific retention, deletion/export workflows, access logs, managed secrets, and reviewable audit history for prompt, approval, publishing, and integration events.
Bounded
LLM evidence packets
LLMs should receive only redacted, source-cited evidence packets and approved prompts. They should not read raw delivery stores or make automated employment, safety, or customer-impact decisions.