ChartPull analyses your organisation’s historical data to surface trends, flag risks, and help you plan ahead. Predictions get smarter over time as more data accumulates.
Every time ChartPull syncs with your Google Workspace, it records a snapshot of your organisation. The AI analyses these snapshots over time to identify patterns — which departments are growing, which are losing people, whether team sizes are healthy, and where anomalies are occurring.
Unlike simple reporting (which tells you what happened), predictions tell you what is likely to happen. For example, if a department has lost 3 people in the last 6 weeks while every other department grew, the AI flags it as an attrition risk before it becomes a crisis.
Data collection period
Predictions improve significantly after 2 to 4 weeks of data collection. During the first week, you will see basic metrics but the AI does not have enough history to make meaningful predictions. After two weeks, trend lines start appearing. After four weeks, predictions are fully active.The Attrition Risk panel shows AI-predicted departure probability by department. Each department gets a risk rating:
| Rating | What It Means | Typical Signal |
|---|---|---|
| Low | Department is stable. Headcount is flat or growing consistently. | 0–1 departures in the past month, healthy team sizes. |
| Medium | Some departures detected. Worth monitoring but not urgent. | 2–3 departures in the past month, or team sizes above average. |
| High | Significant departures detected. This department is likely losing people faster than expected. | 4+ departures in the past month, declining headcount trend, or loss of senior staff. |
The risk rating is based on the rate of change, not just the absolute number. A department of 200 losing 2 people is very different from a department of 10 losing 2 people. The AI accounts for department size when calculating risk.
What predictions cannot tell you
ChartPull tracks people in your Google Workspace directory. A “departure” means someone was removed from the directory — the AI does not know if they resigned, were let go, or transferred to a different entity. Use attrition risk as a signal to investigate, not as a definitive diagnosis.The Growth Trends panel shows headcount trajectory projections for the overall organisation and per department. It answers the question: “If current trends continue, how big will we be in 30, 60, and 90 days?”
What you see:
Growth trends are especially useful for workforce planning. If your Sales team is growing at 15% per month while your Support team is flat, you can anticipate that Support will eventually be overwhelmed as Sales brings in more customers.
Click any department in the Predictions dashboard to open a per-department deep dive. Each department page includes:
The AI continuously monitors your organisation for unusual changes. When it detects something outside the normal pattern, it flags it as an anomaly. Examples:
When the AI detects an anomaly, it sends an email alert to the workspace admin. Each alert includes:
Turn alerts into action
When you receive an anomaly alert, click the dashboard link and check the department analytics page. Look at the “Recent changes” section to see exactly who joined or left. Then reach out to the department head if something looks off.Everything in the Predictions dashboard can be downloaded as a PDF report. Click the Export button in the Predictions toolbar and select “Predictions PDF.” The report takes 5 to 10 seconds to generate and includes:
This PDF is designed to be shared with leadership. Hand it to your CFO before budget planning, your CHRO before a headcount review, or your CEO before a board meeting.
Predictions are based on historical patterns in your specific organisation — they are not generic industry benchmarks. Accuracy improves with more data. After 90 days of data collection, growth trend projections are typically within 10–15% of actual outcomes. Attrition risk is directional (it flags which departments are at risk, not exactly how many people will leave).