Hand-building enterprise-ready agentic infrastructure from scratch forces stakeholders to confront the friction between LLM behavior and enterprise constraints.
The story this project is really telling.
End-to-end spec-driven development forces ALL stakeholders to confront the security, compliance, governance, and reliability demands of deploying AI inside a real business.
The source
Two repositories — the presentation, and the system that powers it.
I
Web app presentation
This narrative site.
Astro static site with MDX prose and React islands. Presents my research, the spec-driven approach, the architecture, and the four captured pipeline replays.
github.com/pr1me289/web-app-enterprise-ai II
The system
The engineering repository.
The orchestration layer, retrieval layer, chunking and embedding, storage and indexing, cross-encoder reranking, the deterministic supervisor, and the agent specs that govern every domain agent.
github.com/pr1me289/enterprise_aiGet in touch
I’d welcome the conversation — about this project, the architecture behind it, or related work in the space.