If you've used a state's ETPL, you have a good understanding of what the gaps are. Trainingproviderresults.gov is a good wrapper on the ETPLs, but neither state ETPLs or TPR have effectively moved the needle on workers enrolling with training providers.
If you've used one of the many training navigators that are effectively a wrapper on CareerOneStop, you may have noticed some of their limitations. They rely on the same underlying search experience, with light customization to filters or branding. That approach is quick to stand up, but it inherits the same limitations. The relationship between the data and the worker or the data and their place is shallow, and the experience is not ready to adapt as much to a state or partner’s specific context as this is.
This tool is built for good data + good design + flexible customization for a state or sector's needs.
Scalable data
The system is designed to stand up quickly for a state while maintaining a consistent, extensible foundation. It uses a medallion architecture, where raw data (bronze) is preserved, standardized (silver), and then surfaced through clear precedence rules (gold). This allows multiple sources to coexist while maintaining traceability and data hygiene. It also builds a system working backwards from a future of worker voices and provider-submitted information and forwards from validated, but flawed, public datasets.
Programs, providers, occupations, and wages are connected through a matching system that is responsive to the catalog itself. As new records are introduced or existing ones change, relationships are re-evaluated. This allows the system to scale across states and providers and preferences in standardization methodologies.
The data pipelines are reusable and templatized - aka, they work in one shot. New datasets can be ingested into the same structure and participate in the same matching and prioritization logic. This supports alignment with frameworks like Advance CTE, enrichment from sources like the Harvard Workforce Almanac, and future layers like claimed programs or worker reviews, without any restructuring of the system.
Behavioral design
The experience adapts to the specific catalog it is built on. The quiz and discovery flows are dynamic, shaped by the programs, occupations, and data available in a given state instance. The tool puts a high premium on progressive disclosure, careful introduction of vocabulary, and above all - removing a lot of things that distract them from the path they came to walk. It's a short and yellow brick road right now.
The system is designed to help a worker move from exploration to decision. Information is structured so it can be understood and compared without translation. The experience surfaces relevant options early, then allows users to go deeper as needed. The goal is clarity in how to use it.
Built for partnership
The system is portable and designed to be deployed quickly across regions, but it is not fixed. Each instance can be shaped to a state or partner’s priorities, whether that is aligning to a specific framework, highlighting certain industries, or incorporating new data sources.
It is also designed to be used in partnership with stakeholders and workers. The structure allows for ongoing input, including program claims, updates, and feedback tied to specific records. This reduces the distance between the data and the people it represents.
In practice, this is what makes it different. Having a portable, scalable foundation (that can adapt to a state’s context, support different use cases, and improve over time while staying grounded in consistent data and real-world input) may be what's needed to land the adoption and motivation game in increasing worker enrollment in training programs across the U.S.
Dramatic, I know, but this is why I love my job! We've observed millions of workers interact with tools like these and know for certain that the opportunities are there.
