No-Code Client Portal Handoff (Handoff)
A handoff with 7 steps: Inventory every external integration and data flow before the handoff meeting.
By InnovaAI ResearchPublished
No-Code Client Portal Handoff (Handoff)
- 01
Inventory every external integration and data flow before the handoff meeting
List each connected service, the direction of data movement, and the credentials owner. Confirm whether the client has admin access to the underlying data sources, not just the portal interface.
- 02
Document the data model and field-level permissions in a client-accessible format
Export the schema, including table relationships and user roles. Specify which fields are read-only, which are editable, and who can approve changes, so the client can govern the portal without guesswork.
- 03
Run a load test with the client's real user count and data volume
Simulate peak concurrent usage and large record sets to verify response times. Record the baseline numbers so future performance regressions are measurable against a known starting point.
- 04
Create a rollback plan that covers both the app definition and the underlying data
Identify the platform's versioning or export features, and schedule a backup of the data store. For example, if the portal is built on Airtable, confirm the client can restore a previous snapshot if a change goes wrong.
- 05
Define the support escalation path with named owners and response SLAs
Assign a primary contact at the agency and a backup, and specify which issues go to the platform vendor versus the agency. Agree on response times for critical, high, and low severity tickets.
- 06
Transfer ownership of the build environment and any custom code or scripts
Provide the client with access to the platform workspace, source control if used, and any API keys or webhook endpoints. Document where each credential is stored and how to rotate them.
- 07
Schedule a post-handoff review at 30 and 90 days to measure adoption and performance
Set calendar invites before the handoff meeting ends. Use the review to compare actual usage against the load test baseline and to catch workflow gaps that only appear in production.