Capacity was capped by the people qualified to review
Case review was manual, slow and tightly regulated. Every additional unit of throughput meant another qualified specialist, and specialist capacity was the constraint on the entire service.
Manual by necessity
Every case was read end to end by a qualified specialist. There was no step that could simply be skipped without changing what the review meant.
Slow, and slow in a way that compounds
Hours per case put a hard ceiling on daily throughput, and the queue absorbed every fluctuation in demand.
Regulated, so speed alone was never the answer
Any change to how a case is reviewed has to hold up under scrutiny. Faster is only useful if the result is still defensible.
The system does the reading, the specialist does the judging
Amaris built clinical-grade AI scoring around the existing review pipeline, keeping a qualified specialist in the decision path. That division is what makes the speed-up acceptable in a regulated setting.
Drag the handle, or focus it and use the arrow keys.
Watch the constraint move with the case load
Capacity used to be priced in specialist hours. Set the weekly volume and the two staffing curves separate in front of you.
Your inputs and round assumptions, not the engagement’s measurement. The published figures stay banded at 5×+ faster and 30k+ hours a year.
Capacity returned, without a hiring round
Figures below are banded and rounded down from the measured result.
Wherever a qualified human is the bottleneck, this shape applies.
Every regulated review process eventually runs into the same wall: the only way to do more is to hire more of the hardest people to hire. The pattern proven here — let the system do the reading, keep the qualified human on the decision — lifts the ceiling without moving the accountability. The domain changes; the division of labour does not.