Proof

The savings,
and the receipts.

// Measured how

Every figure here is measured against the client's own pre-engagement baseline — not a vendor benchmark. Names withheld under NDA.

0
Median annual spend reduction across the portfolio
$0
Cumulative annualized savings delivered to date
0
Average return clients see on our fee
Case 01 · Fintech

A scaling fintech, 41% lighter.

Series-D · payments · 3 clouds

Growth had buried $11M a year of waste the team had written off as the cost of scaling. We rebuilt their cost model, re-architected the data pipeline, and left a FinOps practice that keeps finding more.

41%
run-rate cut
$11M
recovered / yr
11 wks
to break even
Case 02 · Healthcare

Regulated workloads, 28% off — zero downtime.

HIPAA · imaging · 2 clouds

Compliance constraints had frozen their architecture for years. We found the savings inside the rules — storage tiering, commitment laddering, and egress redesign — without a single change to their security posture.

28%
annual cut
$6.3M
recovered / yr
0
audit findings
Case 03 · AI / ML

GPU spend halved as training scaled 3×.

Foundation models · 2 clouds + spot

Their bill was doubling every two quarters. We built a spot-first training fabric with graceful checkpointing and a commitment portfolio that flexed with demand — so cost fell while throughput tripled.

52%
cost / token cut
3×
training scale
9 wks
to break even
"They found $11M a year we'd written off as the cost of growth — and left us a team that keeps finding more."
VP of Platform Engineering
Series-D fintech · 3 clouds
[ before / after spend — placeholder ]
41%
annual run-rate cut
$11M
recovered per year
11 wks
to break even on fee
0
feature roadmap slip
Who we work with

Across regulated &
hyperscale workloads.

Client names are withheld under NDA — these are the sectors we work across.

Fintech & payments
Healthcare & life sciences
Logistics & supply chain
AI & ML platforms
Retail & commerce
Media & streaming
Banking & capital markets
Robotics & hardware
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