Scale signal
4 -> 2000+
employees across growth stages, hiring surges, and operating-model redesigns
About
Executive operator. Founder. Builder.
A background spanning national service, enterprise data and taxonomy platforms, cloud-scale SaaS, teaching, writing, and founder-led execution.
Matthew Loschiavo is a founder-minded technology operator working at the intersection of platform architecture, operating-model design, enterprise data systems, and practical AI.
Across SaaS platforms, digital transformation work, and founder-led ventures, he has helped scale teams from 4-person environments to enterprises with 2,000+ employees, supported fundraising across four rounds, and led through rapid hiring growth, reorganizations, and platform maturity shifts.
His footprint includes $10M+ cloud spend, 20,000+ B2B customers, 10M+ named users, roughly 1M concurrent users, and sustained transaction volume at enterprise scale, with accountability across reliability, governance, cost, and delivery.
What makes the profile different is the combination of executive operating discipline, deep multi-tenant platform engineering, taxonomy and standards fluency across 25+ standards and 9+ industries, and the ability to turn complexity into decision-ready clarity for boards, founders, and leadership teams.
Executive Footprint
Scale signal
$1.9B
capital raised across organizations and fundraising rounds supported
Scale signal
$10M+
annual cloud spend governed across platform operations
Scale signal
20,000+ / 10M+
B2B customers and named users supported in enterprise SaaS environments
Scale signal
50M+ / hr
transactions sustained across high-scale multi-tenant systems
Scale signal
25+ / 9+
taxonomies, standards, and industry domains navigated
Scale signal
2 weeks -> 1 day
onboarding reduced through software, workflow, and enablement design
Scale signal
0 -> 8
core functions and teams built from zero across engineering, product management, software development, professional services, DevOps, QA, support, and training
What Makes The Background Different
Platform scale with executive accountability
The work is not just architecture on paper. It includes reliability, security, cloud economics, dashboards, audit readiness, and delivery discipline across systems with Fortune 500 consequence.
Operating-model design, not just engineering output
A recurring pattern has been building functions from zero across engineering, product management, software development, professional services, DevOps, QA, support, and training, then reinforcing them with documentation, telemetry, and escalation rhythms so the business can scale decision quality along with software.
Rare data, taxonomy, and standards depth
The background includes configurable metadata-driven platforms, tenant isolation, reference data, and standards work across retail, industrial, healthcare, finance, publishing, travel, and more.
Service, teaching, and communication
Georgia Tech training, USAF service, adjunct teaching, authorship, FCC credentials, and current AI engineering study all reinforce a style that is disciplined, practical, and unusually good at making complexity legible.
Credentials & Context
The parts that do not fit on a simple bio line
Georgia TechUSAF VeteranAdjunct ProfessorAzure DevOps CertifiedQuantic AI Engineering CandidateAuthorFCC LicensedDual U.S./Italian Citizen
The formal background includes Georgia Tech, current graduate study in AI engineering, Microsoft Azure DevOps certification, adjunct teaching, authorship, and a long-running founder's office perspective through Kettle Logic.
It also includes United States Air Force service, Coast Guard merchant mariner credentials, and FCC licensing across AE, MROP, GROL, RR, GMDSS-O/M, and Radar endorsements, which together shaped a very operations-first approach to reliability, communications, and disciplined execution.
Operating Philosophy
How the work is approached
Start with workflow friction
Find the operational bottleneck first, then let architecture and tooling follow the real constraint.
Make economics visible
Cloud, reliability, support burden, and delivery pace should be legible to leadership, not buried in technical noise.
Build trust into the system
Governance, security, auditability, and resilience are operating attributes, not afterthoughts.
Use AI as leverage, not theater
AI is most valuable when it reduces coordination cost, accelerates context assembly, and improves execution quality.
Let's Build
Looking for an operator who can connect platform decisions to growth, trust, and execution?
I work with teams that need strategic technical direction, operating-model clarity, and delivery momentum at the same time.