A planning estimate, not a guarantee. Adjust the inputs to match your business and the model estimates the monthly economic impact of offloading recurring operating work — sales follow-ups, project handoffs, finance chasing, SOP capture, dispatch admin — to LeedAB. We are conservative on every line. Recovered capacity isn't claimed as cash unless it converts to billable utilisation or avoided hires.
Reasonable defaults pre-filled by ICP. Override anything you'd like.
This is a planning estimate, not a guaranteed saving. The model splits economic capacity (hours unlocked at full loaded cost) from cash impact (capacity × cash conversion). Without that split, hours-saved calculators routinely overstate ROI by 4×. The headline shows steady-state cash, not topline.
Operational capacity unlocked: team × hours × automation_rate × 4.33 weeks/mo. The hours your team gets back, shown separately from cash. The hours-per-person input is the work LeedAB actually offloads — sales follow-ups, project handoffs, finance chasing, SOP capture, dispatch admin — not all hours worked.
Cash conversion: capacity × cash_conversion%. Recovered hours only become P&L cash when they offset a hire, become billable utilisation, or remove overtime. Otherwise it's quality-of-life value: real, but not bankable. Default 25% is honest for mixed teams; 50% is right when capacity directly offsets a planned hire; 10% is right when it just becomes quieter weeks.
Ramp: month 1 ≠ month 12. The brain learns workflows. We apply a linear ramp factor min(1, month_i / months_to_full_value) to monthly value, default 3 months. The 12-month return is the sum of ramped monthly cash flows minus the full setup cost (including hardware if you need to buy a Mac mini).
Payback: first month where cumulative cash flow (including setup + hardware) crosses zero. Sharper than setup ÷ steady-state-net because it accounts for ramp.
Conservative ↔ Upside range: shown beneath the headline. We swing only cash conversion ±15 percentage points (floored at 5%, capped at 75%), holding everything else at your inputs. Directional sensitivity, not Monte Carlo. A range that doesn't survive cash-conversion swings is the wrong fit.
Not modelled: implementation friction (your team's hours during onboarding), NPV / time value of money (rounding error at this horizon), compounding value as the brain grows, retention improvements, faster new-hire onboarding, reduced founder dependency. Real but harder to put a defensible number on, so we leave them off rather than inflate. We also do not model SaaS savings — LeedAB runs on top of your stack, it doesn't replace it.