Things I've solved, written down
Searchable internal docs: the playbooks behind the resolutions.
Fixing LMS grade sync failures caused by attempt limits
Integrations
Problem
Grades were not syncing to the LMS due to failed submissions being rejected after reaching the maximum attempt limit.
Approach
Reviewed logs to identify LMS rejection responses and worked with the customer to adjust LMS settings to allow unlimited attempts during grading sync.
Outcome
Grade sync restored after configuration change, with engineering later introducing notifications to flag assignments with limited attempt settings.
Resolving system access errors caused by missing configuration
Configuration
Problem
Users received a message indicating the system was not installed or properly configured.
Approach
Investigated setup flow and identified that a required configuration code was not being applied at initial setup. Provided the correct configuration code and verified system initialization behavior.
Outcome
System access restored after applying configuration once, enabling all users to successfully operate without repeated setup errors.
Tracing report data back to its source documentation
Database
Problem
Database values did not align with source paper records, resulting in reporting discrepancies.
Approach
Compared database entries against source documentation and isolated records with incorrect date assignments by entry user using targeted SQL.
Outcome
Inconsistencies pinned to specific rows and corrected at the source via SQL updates while preserving unrelated data integrity.
Preventing importer failures caused by malformed data
Data Imports
Problem
Importer failed due to malformed characters in the source file, resulting in stale pricing data being displayed on the website.
Approach
Identified an apostrophe in the dataset that broke the import process and updated the importer script to properly handle special characters during ingestion.
Outcome
Restored data by rerunning the import and fixed the script so future runs do not fail on malformed characters.
Improving customer request interpretation with LLM-assisted analysis
AI / LLM
Problem
Customer tickets were often vague or incomplete, making it hard to quickly identify the real technical issue or system impact.
Approach
Used LLM tools to translate customer messages into structured problem statements, highlight key technical signals, and suggest likely system areas. Verified interpretations using logs, data, and prior ticket context before acting.
Outcome
Faster and more accurate triage of ambiguous tickets, reducing back-and-forth and time spent clarifying requests.
Reading integration failures in HubSpot–CPQ API workflows
API
Problem
Updates made in HubSpot didn’t always show up correctly in the CPQ tool, and there was no clear error explaining why.
Approach
Checked API responses and system logs to see if the request was accepted, then verified data in both HubSpot and CPQ. Looked at authentication, data formatting, and rate limits, and used test updates to compare expected vs actual results.
Outcome
Faster identification of where the breakdown happened in the sync process, whether in HubSpot, the integration layer, or CPQ, leading to quicker resolution and clearer troubleshooting paths.
SQL diagnostics for database discrepancies
Database
Problem
Data integrity issues where database values do not match an external source of truth.
Approach
Compared expected vs actual data using SQL queries to isolate mismatches, narrowed results to specific patterns or contributors, and validated findings before applying targeted fixes.
Outcome
Discrepancies were accurately identified and resolved, and the investigation pattern was reusable for similar data integrity issues.
Turning a fix into a reusable playbook
AI / LLM
Problem
The same class of issue keeps reaching escalation.
Approach
Document the diagnosis, automate the repetitive checks, and draft the runbook with AI assistance.
Outcome
Next on-call resolves it first-touch instead of escalating.
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