Free tool · 2 minutes
Model the return before you commit
Every assumption is visible and editable, including the two that most calculators hide: how much saved time actually converts to value, and what the thing costs to run.
On this process specifically, not their whole job.
Salary plus benefits, taxes and overhead.
Be realistic. 100% almost never happens — exception handling and review remain.
Not all recovered time becomes cash or redeployed output. 70% is a defensible default; 100% is not.
Our fixed-price sprint, or your own estimate.
Model inference, infrastructure and support. Easy to forget, and decisive.
Net annual saving
$36,691
After running costs and a 70% realisation assumption.
- 2,208 hrs
- Time on this process per year
- $104,267
- Cost of that time today
- $72,987
- Gross saving before adjustments
- $14,400
- Annual running cost
- 0.9
- Full-time equivalents released
- 20 months
- Payback period
Across 4 people, 46 working weeks
Before realisation and running costs — the number most calculators stop at
Inference, infrastructure and support
Redeployable capacity, not necessarily headcount reduction
Time to recover the one-off build cost
Three-year net position
$51,072
Three years of net saving less the one-off build cost. Assumes the process volume, the running cost and the automation rate all hold — if volume grows, this understates it; if the process changes materially, re-run the numbers.
Want these numbers checked properly?
This model is only as good as your estimates. The two-week audit measures the actual hours, the actual error rate and the actual inference cost, then ranks every candidate process on that basis.
About this model
How the numbers work
Why is there a 'realisation' slider?
Because hours saved and money saved are not the same thing. Some recovered time gets absorbed rather than redeployed, and finance teams know it. Most vendor calculators assume 100% conversion and produce numbers nobody believes. We default to 70% and let you argue with it.
Why does running cost matter so much?
Because model inference is a real, recurring, per-transaction cost that scales with volume. An automation that saves ₹40 of labour and burns ₹55 of inference is a science project. We have seen this exact outcome in the wild, and it is the reason cost-per-successful-task is instrumented from day one on everything we build.
Can this produce a negative answer?
Yes, and it does regularly — that is the point. A calculator that can only produce good news is a sales toy. If your numbers come out underwater, that is genuinely useful information and it cost you nothing to find out.
How accurate are the defaults?
They are representative of a mid-sized back-office process, not a prediction about yours. The build and running cost defaults reflect our own fixed-price sprint and typical inference spend. Change all of them — the model is only as good as your estimates, which is exactly why the paid audit measures rather than estimates.
Estimates in, estimates out
The audit replaces every estimate above with a measurement — actual hours, actual error rate, actual inference cost — and ranks each candidate process on that basis.
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