Phase-by-Phase Implementation
How three siloed divisions became an autonomous multi-agent intelligence system in 6 progressive phases
Click any phase to explore the implementation details
Infrastructure Foundation
Data pipeline and staging infrastructure
Foundational registries that track models, datasets, and experiments—enabling systematic learning across all phases. Zero infrastructure cost with file-based storage.
Unified Embedding Space
Shared semantic infrastructure across all divisions
Search all organizational data with AI embeddings so employees find relevant documents across any division, country, or language (English, French, Spanish, Arabic) more easily.
Task-Specific SLMs
Fine-tuned small language models for division tasks
AI learns the organization's team-specific tasks giving employees intelligent assistance for the work they prioritize—15 specialized models trained on the organization's workflows.
MoE Division Agents
Mixture-of-Experts models from merged SLMs
Each division gets expert AI combining their specialized assistants into one agent that handles many tasks to the organization's standards.
Agentic Discovery
A2A protocol for autonomous collaboration
Agents learn how to use information across the organization's divisions giving access to task capabilities to enhance other divisions' work.
Orchestrated System
SLM orchestrator trained from discovery data
Employees across the organization engage through a single AI window that can leverage data and capabilities across all the enterprise tailored to the organization's knowledge and culture.
Next: Results
See the complete investment analysis, strategic value delivered, and what this case study proves about enterprise AI deployment.
View ResultsThis is a portfolio demonstration project showcasing the complete design and implementation of an enterprise AI system. The technical implementation is actual and deployment-ready. Business context (the $1.3B international organization) provides realistic constraints and requirements. Direct investment figures are based on actual infrastructure costs and industry-standard training program estimates.
© 2025 Daniel Dimick. Licensed under CC BY-NC 4.0 for educational use.