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Code + presentation
Connected company operations
AI Back Office
A connected back office bringing Google Workspace, Gemini, department workflows, and an executive dashboard into one operating view.
Joseff Betancourt • AI Platform Architect
I build software platforms at 10× speed. My governed AI production system has delivered 30–50× faster than traditional forecasts in observed runs—without trading away production quality.
I turn complex architecture into production software using governed AI, developer platforms, and durable infrastructure.
Architecture is only useful when the system runs.
Featured work
What I'm building.
I like systems with clear edges, useful records, and fewer places to hide a bad decision.
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Company & platform
Good work, better connected
Fernain
Connected software for membership organizations and modern teams, with shared identity, permissions, and governance across focused products.
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iPhone app · in development
Your life. Remembered.
Memoriosa
A private, local-first home for memories, connecting people, moments, and stories so you can return to them with their context intact.
Writing & methods
Methods for AI-assisted delivery that hold up.
Practical frameworks for estimating, governing, and delivering software with AI.
Connected company operations
I Built an AI Back Office
A connected back office with one operating view.
A presentation on connecting Google Workspace, Gemini, department workflows, and an executive dashboard.
Download the slides
9-page PDF
Fernain build guide
Building an AI back office with Google Workspace
A practical guide from the Fernain build.
A practical guide covering the build order, records, instructions, and checks used to assemble an internal AI back office.
Download the case study
16-page PDF
AI-assisted delivery management
AI-Assisted Software Delivery Management
Manage AI work without pretending agent activity equals delivery.
A research note testing that premise through a practical AI Delivery Execution Profile layered on established delivery methods, with human ownership, attempt lineage, evidence-bound acceptance, separate clocks, and cost per accepted outcome.
Read the research note
57-page PDF
Provider-agnostic AI orchestration
Provider-Agnostic AI Orchestration
Route work by role, evidence, and cost - not provider loyalty.
A provider-agnostic candidate framework for assigning stable roles and contracts across cloud and local execution, while preserving human authority and requiring evidence at acceptance gates. It is not a provider benchmark.
Read the research note
26-page PDF
Applied methodology
AI-Assisted Estimation Methodology
Estimate delivery without confusing AI speed with human capacity or elapsed schedule.
A practical methodology that separates baseline effort, operator load, AI execution, validation, and elapsed schedule so teams can test assumptions before making delivery commitments.
Read the methodology
20-page PDF
About
Architecture is only useful when the system runs.
I built my career as a senior software engineer in New York City. Now I’m helping define what AI platform architecture looks like in practice: drawing the boundaries, building the system, and staying with it until real people can use it.
Years of product and project work taught me to weigh the business case with the technical one. Good architecture should earn its keep.
How I build
A few rules I keep close.
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Start with the problem.
Know the boundary and acceptance criteria before building.
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Engineer the work, not just the code.
AI can execute at enormous speed. I still own architecture, direction, and result.
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Keep the system legible.
Fast delivery is useless if the next engineer can’t understand it.
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Prove what ships.
Test-first where practical, then human QA and acceptance. Working code still needs evidence.
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Build for the Tuesday after launch.
The system has to survive production, maintenance, and change—not just the demo.