Striped Donkey for teams
Build and test with connected synthetic city data. Start with a working sample, then define the identities, histories and relationships your team needs.
01 / Your workflow
A useful dataset starts with a clear question. Choose your focus to see what to scope and what to validate.
Explore population-linked relationships and historical context with explicit assumptions and source evidence.
Prepare features and evaluate analytical approaches using synthetic records and documented relationships.
Test data flows, integrations and pre-production behavior using connected synthetic structures.
Explore conditional effects across city systems, with validation appropriate to the decision.
Your team sources evidence, defines models, reconstructs connected identities and histories, and maintains validation and releases.
Inspect the published city and domain coverage, review source context and quality evidence, then validate fitness for your own use case.
Read the methodology →02 / Scope & delivery
Agree the operational requirements alongside the data. Delivery, usage rights and support are defined in the engagement.
Build your packageChoose the records, relationships, geographic detail and period your team needs.
Compare Nairobi & Lagos →Define the target environment, formats, access requirements and integration boundaries.
Explore access options →Specify a purchased snapshot or recurring updates. Each refresh has its own release identity and evidence.
Agree usage rights and delivery. Select preparation, integration and support deliverables from the service catalogue.
03 / Evidence & validation
Bring a working result into the conversation. Your team decides whether the data fits the intended use.
Use Python or SQL, compare exact expected outputs, and trace the records back to their original profiles.
Open the developer quick start
