migration candidates
Documented project scope; grouped into reusable migration families.
Evidence / project archiveStress the AI automation—not just the diagram
Run mock failure scenarios and inspect how resilient automation handles duplicate records, unavailable services, payment gates, retries, escalation, and human handoff.
03 / Automation lab
Don’t trust the diagram.
Don’t trust the diagram.
Stress the system.
This mock-data simulator exposes the part most portfolios hide: what the workflow does when reality refuses the happy path.
01 Idempotency02 State gates03 Recovery04 Human context
Verified n8n creator profile ↗
RESILIENT_ONBOARDING.SIM READY
01INTAKEwaiting
02IDENTITYwaiting
03PROVISIONwaiting
04VERIFYwaiting
05HANDOFFwaiting
EVENT LOGMOCK DATA · NO LIVE INTEGRATIONS
- Choose a scenario and run the system.
04 / Evidence, not theatre
Numbers appear only when the source and status can travel with them.
people trained
Sales and professional training delivered across a 16-month leadership role.
Evidence / self-reportedration packs mobilized
COVID-response distribution through Yardim Foundation’s food program.
Evidence / self-reportedyears operating
Sales leadership, business development, CRM operations, and automation architecture.
Evidence / career historyWhat the lab demonstrates
The simulator makes workflow state and recovery visible. A scenario moves through intake, identity, provisioning, verification, and handoff while the event log records what the system decided. The useful evidence is not that the animation completes; it is whether a duplicate, unavailable dependency, pending payment, or timeout reaches the documented safe state.
How to read a run
Follow the canonical record through each checkpoint. Confirm which steps completed, which side effects remain safe to repeat, why the system stopped, and whether the next action belongs to automation or a person. A reliable implementation should preserve the same information in production logs and operator views: record identity, last good state, normalized error, attempt history, owner, and next safe action.
Evidence boundary
The lab uses mock data and local rules. It is evidence of the public failure-state design, not a claim that a client deployment, live CRM, payment system, calendar, or customer record participated in the run. Production acceptance would additionally require authenticated integrations, security review, load behavior, recovery exercises, and owner sign-off.
Frequently asked questions
Does the automation lab use live customer data?
No. It uses invented records and mock failure states. It does not contain client identities, credentials, production endpoints, or live write tools.
What does the simulator test?
It tests happy-path handling, duplicate identity, unavailable calendars, payment gates, webhook timeouts, checkpoints, recovery, and the context provided to a human operator.
What does a successful run prove?
It proves the public simulator follows its documented state and recovery rules. It does not prove an undisclosed client deployment, business result, or production integration.