Talent Translation Infrastructure (TTI)

Executive Summary

The United States workforce ecosystem contains vast quantities of valuable information across educational institutions, workforce agencies, employers, and training providers. However, these systems operate largely in isolation.

The result is a fragmented landscape where workers struggle to signal their skills, employers struggle to identify qualified talent, and policymakers lack reliable real-time visibility into workforce dynamics.

This proposal introduces the Talent Translation Infrastructure (TTI), a distributed architecture designed to connect existing workforce systems through a standardized translation layer rather than replacing them with another centralized platform.

The Core Problem

Across the United States, multiple institutions maintain workforce-related data:

• Educational institutions track credentials and coursework
• Training providers track skill development programs
• Employers track job requirements and hiring pipelines
• State workforce agencies track employment services
• Licensing boards track professional certifications

These datasets rarely interoperate.

The challenge is not the absence of data, but the absence of systems capable of translating information between institutions in a trustworthy and scalable manner.

Proposed Solution

The Talent Translation Infrastructure provides a national interoperability layer that connects existing systems through standardized translators and validation protocols.

Architecture Overview

Instead of centralizing all workforce data into a single database, the system introduces translation adapters that convert institutional data into a shared schema.

This architecture allows each participating institution to maintain control of its data while enabling real-time interoperability across the workforce ecosystem.

Implementation Strategy

Phase 1 — Pilot Deployment

A pilot implementation will integrate selected educational institutions and employers within a participating state. The objective is to demonstrate measurable improvements in workforce placement efficiency.

Phase 2 — Multi-State Expansion

Following successful validation, the infrastructure will expand to additional states while maintaining interoperability through the common schema and translation layer.

Phase 3 — National Network

The long-term vision is a nationwide workforce data interoperability network enabling real-time alignment between training, credentials, and employment opportunities.

Leadership and Technical Background

The initiative is led by Joaquin Soto, a systems architect whose career has focused on integrating complex institutional technologies across telecommunications, healthcare, real estate analytics, and enterprise software platforms.

His experience includes leadership and technical contributions within organizations such as:

• Telcordia Technologies
• SAIC (Science Applications International Corporation)
• Patient Care
• Waterfront Media / Everyday Health
• redIQ
• Berkadia

His work has centered on transforming fragmented datasets into reliable operational intelligence systems capable of supporting large-scale institutional decision making.

Expected Impact

• Improved visibility of workforce skills and credentials
• Faster alignment between training programs and employer needs
• Reduced friction in hiring pipelines
• Transparent workforce analytics for policymakers

By focusing on translation and interoperability rather than replacement, the Talent Translation Infrastructure offers a practical and scalable path toward a more connected workforce ecosystem.