Anonymized work example / 03
Learning data and automation
Making learner status, AI activity and management reporting agree across separate tools.
- 01Course or form
- 02API and status layer
- 03Structured records
- 04Dashboard
- 05Action
A training workflow can scatter identity, call status, scores, transcripts and report links among a course, an AI provider, spreadsheets and dashboards.
Names, screenshots and learner records are intentionally excluded. The diagram uses synthetic labels to explain the system shape without revealing a client implementation.
Mahipal implemented API and Apps Script logic for request routing, status handling, sheet-based data flows and management dashboards, and used diagnostics to find mismatches across the chain.
Implementation choices
Decisions behind the work.
These choices address the points where a plausible demo can fail in real use.
- 01
Normalize the identifiers and statuses used by each connected system.
- 02
Use configuration-driven mappings rather than hard-coded assumptions for every course or agent.
- 03
Protect duplicate and repeat requests through locks, checks and idempotent handling where appropriate.
- 04
Show actionable status in dashboards while keeping sensitive details out of learner-facing messages.
How to verify it
Test the whole path.
Compare source records with the consolidated report for a set of synthetic normal, duplicate, delayed and missing-result cases. Resolve discrepancies before treating the dashboard as authoritative.
Practical lesson
A dashboard is useful only when the path into it is observable, recoverable and governed by clear ownership of each field.