OpsAI — Operations Intelligence Platform
Executive operations control tower. KPIs are calculated from a synthetic 95-day, multi-department dataset — not hardcoded. Filters update every metric, chart and table live, and every KPI is traceable back to its input records.
OpsAI control tower is live
Executive KPIs are derived from 760 synthetic team-day records across 4 departments. Change the filters below to recalculate every metric and chart.
Data Source
Select which dataset powers this dashboardUnderstanding the Operational Scenario
OpsAI simulates a multi-team customer operations environment where managers must balance service quality, operational efficiency and workforce performance. The dashboard uses synthetic operational records to demonstrate how raw activity data can be transformed into actionable business intelligence.
Date coverage
2026-08-05 → 2026-11-07
Records
760
Departments
4
Teams
8
Dataset type
Synthetic
Grain
Team-day aggregate
91.0%
Target: ≥ 95.0% · n=189,314
no prior data
384s
Target: ≤ 360s · n=182,748
no prior data
90.2%
Target: ≥ 90.0% · n=59,748
no prior data
7.4%
Target: ≤ 8.0% · n=25,075
no prior data
75.6%
Target: ≥ 75.0% · n=23,222.9
no prior data
10.4/h
Target: ≥ 12 /h · n=17,550.8
no prior data
Performance Trend
Daily weighted aggregation · 95 data points
Operational Risk Indicators
Threshold-based flags from computed KPIs
- Medium
SLA below target
SLA at 91.0% vs ≥95% target (4.0 pts gap).
- Medium
AHT above target
AHT at 384s vs ≤360s target (+24s).
- Low
Productivity below target
Productivity at 10.4/h vs ≥12/h target.
What Do These Results Tell Us?
Operational insights · structured for executive review
Observation: SLA adherence is 91.0%, below the 95% target.
Possible impact: Longer customer wait times, increased service-level risk, and potential penalty exposure under SLA contracts.
Potential contributing factors
- Demand exceeding staffing coverage during peak periods
- Higher contact complexity or new contact types lengthening answer times
- Absenteeism or schedule gaps reducing available capacity
Recommended next action: Examine demand distribution and staffing coverage for the affected periods; reallocate shifts or escalate capacity gaps.
Evidence: contactsAnsweredInSla=172,251 / contactsOffered=189,314 (gap 4.0 pts).
Observation: TS Team Charlie (Technical Support) is below target on multiple KPIs simultaneously.
Possible impact: Concentrated SLA and satisfaction risk within a single team.
Potential contributing factors
- Multiple KPIs below target at once
- Likely staffing, complexity, or coaching gaps
Recommended next action: Run a targeted team review covering staffing, process complexity, and coaching.
Evidence: status=at_risk, SLA=86.1%, AHT=520s, contactsHandled=18,349.
Team Comparison
Weighted KPIs per team · sorted by SLA ascending
| Team | SLA | AHT | CSAT | Absent. | Util. | Prod. | Handled | Status |
|---|---|---|---|---|---|---|---|---|
TS Team Charlie Technical Support | 86.1% | 520s | 86.1% | 10.9% | 68.2% | 10.9/h | 18,349 | At Risk |
TS Team Bravo Technical Support | 88.1% | 479s | 88.4% | 8.9% | 72.0% | 11.7/h | 22,103 | At Risk |
Billing Team Alpha Billing Support | 90.3% | 360s | 88.8% | 8.0% | 73.1% | 9.2/h | 20,252 | At Risk |
CS Team Bravo Customer Support | 91.4% | 355s | 91.1% | 7.5% | 73.7% | 13.0/h | 32,049 | Watch |
TS Team Alpha Technical Support | 91.9% | 417s | 89.8% | 7.2% | 80.2% | 12.1/h | 25,544 | At Risk |
Sales Team Bravo Sales Operations | 92.3% | 322s | 89.9% | 6.5% | 76.3% | 6.3/h | 13,543 | At Risk |
CS Team Alpha Customer Support | 93.1% | 330s | 93.0% | 5.7% | 77.8% | 13.3/h | 35,525 | Watch |
Sales Team Alpha Sales Operations | 93.6% | 299s | 92.0% | 5.0% | 82.4% | 6.5/h | 15,383 | Watch |
| Targets | ≥ 95.0% | ≤ 360s | ≥ 90.0% | ≤ 8.0% | ≥ 75.0% | ≥ 12 /h | ||
KPI Explanations & Methodology
Transparent formulas · worked examples · management interpretation
Dataset & Calculation Lineage
Records use a consistent (date, team) grain. Every KPI is a weighted aggregation of numerator/denominator totals — never an average of team rates. Zero/missing denominators return null rather than NaN. No personal or agent-level data is stored. The dataset is deterministic (seeded) so SSR and client renders match.
Case Study & Methodology
OpsAI demonstrates an executive operations control tower: SLA monitoring, productivity & AHT analysis, and threshold-based risk detection. Read the full project brief for the methodology, scope and planned expansion path.
Read OpsAI Case Study →