Technology & AI executive · Costa Rica
I get AI out of the demo and into the work.
In one paragraph
Emilio Bogantes is a Costa Rican technology and AI executive with more than 15 years in software engineering and leadership. He co-founded Pangea Holdings and was its Chief Technology Officer from March 2023 to July 2026. Earlier he was Director of Engineering at Soom, a regulated SaaS company serving medical-device manufacturers. He is a Learning Facilitator on MIT Professional Education programs, delivered in collaboration with Global Alumni, and an ad honorem professor at Universidad Nacional. He is based in Heredia, Costa Rica.
Evidence
Four before-and-afters.
Titles say where I sat. These say what changed.
~80%
lower cloud costs
Soom, a regulated SaaS for medical-device manufacturers. A five-month rebuild of the live product, inside FDA and EU MDR constraints.
How it was done4–6 weeks → 1–3 days
to onboard a customer
The same platform. About 80% of the manual onboarding work now runs by itself.
How it was done2 weeks → minutes
for complex forensic reports
A legal team facing 13,000+ scanned files. Specialist AI agents do the reading. People verify the result.
How it was done15 sites in 3–4 days
moved off WordPress
A US insurer, about 35,000 daily users per site. Scripts and AI handled the volume. Humans validated it.
How it was done
01 04
Why I do this
Latin America already supplies the engineers. I want it to supply the leaders too.
I’ve spent my career inside the US and Latin America delivery model. It works, and it has built good careers, mine included. Too often, though, the decisions get made in one place and the work gets done in another.
So I hire for ownership. I teach at Universidad Nacional without pay. I’ve run a community where engineers here learn from each other. The aim is plain: more people from this region in the rooms where technology decisions get made.
01 02
How I think
It’s multiplication.
People × Processes × Data × AI = Business Outcomes
Multiply by zero anywhere and you get zero, however good the model is. So I find the weakest factor first and fix that one.
Selected work
- Regulated SaaS transformationA full platform reconstruction for a regulated SaaS serving medical-device manufacturers, with distributed delivery and disciplined vendor governance.Delivery cycle reduced from four weeks to one
- AI-assisted legal researchA forensic research tool that interprets and searches a large body of legal protocols.Complex forensic reporting reduced from about two weeks to minutes, with human verification
- Applied AI for construction materials and logisticsAn AI operation that captured after-hours demand, extended customer-service capacity, and optimized delivery routing.Avoided hiring approximately three to four additional people
01 03
Writing
- Why Enterprise AI Pilots Fail Before ProductionMost AI pilots do not fail because the model is wrong. They fail because they were never designed to reach production in the first place.
- Cost per Completed Task: The Metric That Changes AI EconomicsMeasuring AI by tokens or seats hides whether it is actually cheaper. Cost per completed task tells you the truth and changes which use cases are worth funding.
- Responsible AI Is an Engineering Discipline, Not a Policy DeckPrinciples on a slide do not change what an AI system does in production. Controls in the system do.
01 03
Next
Three conversations I’m always glad to have.
A senior role
CTO, VP of Engineering, Head of AI. Technology decides the outcome, and the mandate is real.
A hard assessment
An outside read on a platform or an AI program, before you commit more money to it.
Teaching and public work
Universities, fellowships and civic programs working on technology and leadership.
01 03
I read every message myself.