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EVIDENCE · NUMBERS THAT ALREADY HAPPENED

The headcount and the money peers have already saved

20 numbers for the result, 25 case write-ups for the process — from global giants to 200-person mid-caps, from China to the Middle East. Organized by industry, sources graded below.

Honesty note: these are public industry results, not our own delivery record. Sources are in the appendix at the end of this page, graded Ⓐ for media and official reports, Ⓑ for vendor-published claims. If you work with us, your numbers are settled by your own audit ledger: every AI action and every cent, on the record and traceable.
By industry · the full story behind each cell

25 cases, each one explained

Two deep reads · mid-caps count too

You don't have to be a giant to deserve this ledger

DEEP READ · SYNTHESIA · 400 PEOPLE

Customers grew 7× — support headcount grew by zero

The struggle: customers went from 40,000 to 316,000 in two years — the old playbook meant growing the support team 7× too.

What the AI did: holds the front line; routine questions resolved outright, complex ones escalated to a human, missed answers fed back into the knowledge base.

87%self-serve resolution
5 days → 4.6 hrsresolution time
+0support hires
DEEP READ · NEIGHBORHOOD CU · REGIONAL CREDIT UNION

One hotline, $4.4M saved per year

The struggle: the hotline caught only half of inbound calls; peak-hour queues, member complaints, and hiring costs that didn't add up.

What the AI did: voice AI answers every call first, handles routine business end to end, routes complex matters to a human agent.

48 hourstakeover 50→90%
$4.4Moperating savings
43,000 hrsmember wait time cut
Appendix · where every number comes from

Sources, graded

Ⓐ media / official / research reports ｜ Ⓑ vendor-published named cases. Methodology references: AWS Enterprise Agentic AI Guide 2026 · MIT NANDA 2025 · IDC 2025 · Menlo Ventures 2025 · Forrester 2024.

The next signed results report could be yours.

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