Part E · Operating economics
What bounded AI authority is actually worth
This is a working model, not a brochure figure. Move the inputs to match your program and the outputs recalculate live — hours returned, cost per assignment, cycle compression and the ramp to steady state.
Interactive model
Model your program
Monthly intake volume across all lender programs.
Deterministic coordination work today: chasing, re-keying, status calls.
Fully loaded hourly cost of an operations coordinator.
Share of routine work the bounded AI workforce handles without human queues.
Intake to certified closure, measured across the ten chapters.
Annual operating savings
$2.0M
$171K per month
Coordinator hours returned / mo
2,750
of 5,000 spent today
Cycle time after absorption
18.8d
from 28d intake-to-closure
Cost per assignment
$34.88
from $77.50
Model assumptions are transparent: savings are absorbed deterministic minutes valued at the loaded operator rate, cycle compression is capped at 45%, and no revenue uplift is assumed. Authority-bearing work stays with humans in every scenario.
Modelling discipline
What we refuse to count as savings
Absorb deterministic work, not judgment
The savings base is coordination minutes: chasing updates, re-keying data, reconciling status. Authority-bearing decisions stay in human queues and are excluded from every figure.
Capacity before headcount
Returned hours convert to throughput first. The capacity multiple shows how much more volume the same operations team can carry before hiring becomes the constraint.
Cycle time is the compounding asset
Shorter intake-to-closure cycles reduce carrying cost, holding exposure and lender escalation load. Compression is modelled conservatively and capped.
No savings from removed controls
Nothing in this model assumes fewer gates, thinner evidence or looser oversight. Guarded transitions, refusal records and hash-chained audit remain fully intact.