TL;DR

  • System: Sentinel-16 — an AI Knowledge Operating System built on a 106k-word base.
  • Architecture: Prompt → Mod → Pack → Plug → Base → Guardian.
  • Security: The Guardian explicitly protects against prompt injection, keeping proprietary knowledge safe.
  • Lesson: AI is most valuable when designed as modular, secure infrastructure — not just another chatbot.

Context / Challenge

AI tools exploded in popularity, but they shared the same flaw: they were fragile and inconsistent.

  • Prompts had to be remembered, rewritten, and re-tested each time.
  • Results varied depending on wording.
  • Proprietary knowledge couldn’t be safely embedded, because prompt injection could expose it.

I wasn’t interested in building “just a chatbot.” The challenge was bigger:

👉 Could I design an AI Operating System that modularized knowledge, safeguarded it from leaks, and made it reusable across workflows?

The Build

I designed Sentinel-16 as both a system and a business model:

Architecture:

  • Prompt → raw building block.
  • Mod → reusable prompt pattern.
  • Pack → themed collection of Mods for workflows.
  • Plug → integration point into other tools/contexts.
  • Base → the structured knowledge foundation.
  • Guardian → the single front-facing layer, carrying tone + security.

The Guardian is critical:

  • It’s the security layer that explicitly protects against prompt injection so the proprietary Base cannot be compromised.
  • It’s also the interaction layer — giving the system presence, tone, and pacing.

Together, the Guardian + Base form a complete, client-ready OS.

Sentinel 16's Knowledge Base: A 106k-word modular foundation. Structured around Prompts → Mods → Packs → Plugs, it is the engine that powers Sentinel-16. (https://craft.do/)
Sentinel 16's Avatar: A designed identity that makes the Guardian legible. It signals presence, but more importantly, it represents the security layer that protects against prompt injection and manages access to the Base. (https://leonardo.ai/)
Custom GPT: The interactive interface where clients actually engage. This is the front-facing Guardian, redirecting unsafe prompts, facilitating knowledge use, and making the OS usable in daily practice. (Custom GPT)

Validation

I tested the business case with an early survey:

  • Marketing respondent: Used AI for copy + idea generation. Frustrated by inconsistency. Saw Mods as a way to save time in prospecting + client comms.
  • Legal respondent: Used AI for Q&A. Same pain: inconsistency. Validated Mods as valuable for internal workflows.

🔑 Cross-domain pattern: Two very different fields, same problem.

Buying signal: 100% wanted to see Mods in action via demo/workshop.

This confirmed the opportunity: Sentinel-16 solves a universal pain — prompt inconsistency, wasted time, and lack of reuse.

Outcome / Impact

  • Built Sentinel-16 into a working Knowledge OS.
  • Validated demand for Knowledge as a Service (KaaS).
  • Positioned the system for first-client engagements.

Reflection

The biggest impact of building Sentinel-16 is this: it’s not just a system — it’s a business.

By combining a modular Base with a Guardian that explicitly protects against prompt injection, I’ve created a framework that organizations can trust with their proprietary knowledge.

The next milestone: securing the first client, proving the model in practice.

Closing Thought

Sentinel-16 reframes what AI can be:

  • A Guardian that protects knowledge and builds trust.
  • A Base that modularizes and reuses expertise.
  • An OS that turns fragile prompting into scalable, secure workflows.

This isn’t a chatbot.

It’s a secure operating system for knowledge.