Built on receipts.
Not opinions.

NGO · 500 FTE · donor-funded

AI subtracts bloat. It doesn't add cost.

A donor-funded NGO ran the diagnostic over a stack of 22 active platforms. The fleet found half of it was load-bearing in name only — parallel CRMs, dormant dashboards, automations untouched in eighteen months. The recommendation was to cut. Output stayed flat over the 90 days that followed. Donor dependency dropped because burn dropped.

"The president didn't love the conclusion. His funding model depended on the appearance of complexity — bigger stack reads as bigger work. The lift was technical. The resistance was political."

Stack cut

50%

Output at 90d

Unchanged

Diagnostic clock

2 wks

MyGoat · sales agent · act 2 · today

Susan stopped doing tasks. She started doing the job.

Susan today reads every sales-tagged email, call transcript and meeting note across MyGoat — including the ones the rep was just CC'd on. Pipeline scoring, deal retention, demand discovery, lead-gen, CRM audits — all running in the background. The new pattern is rep-initiated voice handoff:

"Just met Dallas airport. Fetch the content, relay the cost analysis sized to their org, send it by me first, and update the CRM."

The agent pulls the transcript, sizes the analysis from the diagnostic library, drafts the proposal in MyGoat's voice, holds at the approval queue — "send it by me first" — then sends, writes the CRM, sets the SLA, and logs the evidence-ledger entry. The rep keeps the relationships. The agent keeps the receipts.

Hours reclaimed

~20/wk

Approval gate

1 step

Background tasks

0 human-sec

MyGoat · token economics · ongoing

Same fleet. Same model class. Half the token spend.

At scale, the diagnostic fleet hit a cost ceiling. Per-action inference costs were climbing faster than throughput. The agentic core treated this as an infrastructure problem, not a model problem — caching, intelligent routing across model tiers, prompt distillation across the fleet, and selective batch evaluation where latency allowed.

The same workload that previously cost $150 a week now runs at roughly $60 — same fleet, same model class, same fidelity on findings. The agent fleet now scales with traffic, not with model cost. That's the difference between a tool that costs you more as you grow, and infrastructure that gets cheaper.

"Inference is a metered utility. We treat it like compute, not magic."

Per-workload

$150 → $60

Reduction

~60%

Fidelity loss

None