ROI calculator
Tune inputs to match your org. Results update instantly.
Chats, emails, messages routed to agents.
Used as your baseline staffing level today.
Fully loaded, salary plus overhead.
This model assumes the automation rate improves over time (you can edit that below).
Set to match your pricing.
Applies as a relative gain (compounded) and is capped by the max below.
Real-world deployments usually plateau. Set this to what you think is achievable.
3-year breakdown
Employee cost, AI cost, capacity freed, and savings.
| Before | Year 1 | Year 2 | Year 3 | Total | |
|---|---|---|---|---|---|
| Employee cost | $0 | $0 | $0 | $0 | $0 |
| AI cost | - | $0 | $0 | $0 | $0 |
| Agents freed | - | 0 | 0 | 0 | 0 |
| Your savings | - | $0 | $0 | $0 | $0 |
Assumptions and model
- Baseline annual support cost = employees × annual cost per employee.
- Each month, the resolution rate improves by monthly improvement (compounded) until it hits the max resolution cap.
- For each year, employee cost scales with the average unresolved workload: employee cost = baseline × (1 - avg resolution).
- AI cost per year = AI resolutions × price per AI resolution, where AI resolutions = conversations per month × 12 × avg resolution.
- Savings per year = baseline - employee cost - AI cost. ROI = total savings ÷ total AI cost.
Tip: If your staffing does not scale linearly with workload (for example, fixed coverage requirements), add a minimum headcount floor in the JS function employeeCostForYear().