Phase 0: Infrastructure Foundation
Foundational registries and staging infrastructure that enable model training, evaluation, and progressive capability building
The Problem
AI projects fail when teams can't track what worked, reproduce results, or build on previous experiments. Without systematic infrastructure, learning stays local and effort gets duplicated. Every training run becomes an isolated event rather than part of cumulative organizational knowledge.
The Solution
Three lightweight registries (models, data, experiments) that make every training run measurable and every phase capable of learning from previous work. File-based storage with structured logging, no database dependencies, no complex infrastructure. All AI experimentation happens in isolated staging environments that cannot impact production systems.
The Value
Zero-cost foundation enabling systematic learning across all AI development, with complete isolation from production systems. Divisions maintain self-service control over their data and experiments while building shared infrastructure that compounds as teams learn what works. Staged data infrastructure mirroring each division's workflows, model registries tracking all trained models across phases, experiment tracking capturing every training run, and standardized evaluation frameworks—all established in approximately two hours with no ongoing costs.
Next: Phase 1 - Shared Embedding Space
With Phase 0 infrastructure in place—data staged, registries configured, experiment tracking enabled—you're ready to begin building AI capabilities. Phase 1 creates the Unified Embedding Space that enables semantic search and cross-divisional pattern discovery without centralized orchestration.
Phase 1 trains custom embedding and reranker models on your staged data, creating a shared semantic infrastructure where all three divisions can find relevant information across silos—the foundation for all subsequent AI capabilities.
Continue to Phase 1 →