Striped Donkey for teams
Build and test systems with connected synthetic city records. Start with a working sample, then define the identities, histories and relationships your team needs.
32 Nairobi residents. Seven joined tables. Run Python and SQL, compare expected results, and trace records to their original profiles.
Start with the job
Choose a workflow to see the scope and validation questions to bring into a discussion.
Planning focus
Explore population-linked relationships and historical context with explicit assumptions and source evidence.
Review the population, geography and historical period required by your research question.
Check calibration sources and model assumptions; plan validation for your intended analysis.
Planning focus
Prepare features and evaluate analytical approaches using synthetic records and documented relationships.
Choose the entities, features and relationships your development workflow needs.
Assess relevance and performance against the intended real-world context.
Planning focus
Test data flows, integrations and pre-production behavior using connected synthetic structures.
Identify the schemas, relationships and test cases required by your application.
Check the sample against your integration and acceptance requirements.
Planning focus
Explore conditional effects across city systems, with validation appropriate to the decision.
Define the mechanisms, assumptions and time range for the question you want to explore.
Treat outputs as conditional model results and validate against the consequences of the decision.
Build or license
Starting from a documented release changes the preparation work. Your team still decides whether the data fits its use.
Build the environment
Start from FCTE
Define the engagement
Bring the operational requirements into the same conversation as the dataset.
01Choose the records, relationships, geographic detail and period the team needs.
02Define the target environment, formats, access requirements and integration boundaries.
03Specify a purchased snapshot or recurring updates. Each refresh has its own release identity and evidence.
04Agree usage rights and delivery. Select preparation, integration and support deliverables from the service catalogue.
Decision context
Describe the intended use, affected users, decision consequences and empirical validation plan when requesting access.
Synthetic records support systems and scenario work. They do not describe actual individuals. Stronger consequences need stronger validation.
Review the use classes →Start your review
Explore the available scope before you define the engagement.
City, domains, intended use and delivery requirements.
32 residents, seven tables and exact expected outputs. Check a task in your own environment.
Inspect the working example →Read the original results, exclusions and unresolved gaps for your city and domain.
See measured evidence →Choose the domains, then include formats, timing and acceptance checks in your quote request.
Build your package →
