Quality audits were fully manual. I built a platform that scores work automatically and surfaces trends nobody could see before.
What it does
Large operations team. Work gets audited for quality — did you identify the right issue, follow the right process, resolve it correctly? Before this tool, reviewers did that by hand: read the case, score it on paper, compile the results in a spreadsheet.
The platform pulls completed cases, applies a structured scoring rubric across multiple categories, and renders the results in real-time dashboards with trend charts, pass/fail distributions, and per-person benchmarking.
The problem
Manual audits don't scale. Dozens of specialists each doing a few audits per week adds up to hundreds of evaluations — just the scoring part. Then someone has to compile, analyze, and report. That's dozens of hours per week spent on manual quality work. No standardization between reviewers. No visibility into trends over time. No way to spot systemic issues.
How it works
flowchart LR
A["Case data\n(API)"] --> B["Apply rubric"]
B --> C["Score + weight"]
C --> D["Dashboard"]
C --> E["Export"]
Cases are fetched from internal APIs filtered by week, team, and type. Each case is evaluated against multiple quality categories with defined pass/fail criteria and weighted scoring. Results flow into dashboards.
The rubric
Multiple categories covering the full lifecycle of a case — from initial identification through process adherence to resolution quality. Each has binary pass/fail criteria with a weighted deduction. Categories are scored independently so you can see exactly where someone struggles.
The dashboards
- Score evolution over time (weekly trend lines)
- Pass/fail ratio with distribution visualization
- Per-person benchmarking against team averages (radar charts)
- Most-failed-items ranking — surfaces systemic training gaps
- Work-type analytics — different investigation types scored independently
What I learned
The hardest part wasn't the code. It was getting the rubric right — what counts as a failure, how much each category weighs, when exceptions apply. That required iteration with the people who actually do the auditing. The tool only works because the scoring logic reflects how experts actually evaluate quality.
Once the data was visible in dashboards, patterns emerged immediately. Certain process failures correlated with specific work types. Training gaps that were invisible in manual spot-checks became obvious trend lines. Supervisors could coach with data instead of anecdotes.