What powers your practice
Behind every cubelet is a 5-stage AI production pipeline — from domain signal to quality-gated knowledge atom. Here's what your practice is built on.
5-Stage AI Production Pipeline
Gather context, regulations, and domain expertise from authoritative sources
Generate six-face cubelet content with structured learning outcomes
Refine coherence, accuracy, and pedagogical clarity across all faces
Validate against 42/60 quality gate — no face below threshold
Write to Neo4j knowledge graph with embeddings and taxonomic links
The Knowledge Atom
A Cubelet is a knowledge atom with six faces of understanding — WHAT, WHY, HOW, WHERE, WHEN, and APPLY — explored across five levels of mastery depth.
Each face is scored during production. The quality gate requires a minimum 42/60 aggregate score with no face below threshold. This ensures every cubelet delivers complete, multi-dimensional understanding.
MCP-Native Delivery
Every Cubelet product ships as an MCP server — a standard protocol that lets AI assistants use specialized tools. Your compliance training runs inside Claude, ChatGPT, or any MCP-compatible client.
In Claude Desktop
16 interactive tools — practice listing, simulation, coaching, gap analysis — all accessible through natural conversation.
In ChatGPT
The same CMMC Mock Assessment Simulator running as a ChatGPT integration. Same intelligence, different surface.
In Your Browser
A dedicated web application at cmmc-app.cubelet.ai for teams that prefer a traditional interface.
// for organizations
Build with cubelets
Organizations can assemble tabletop exercises, custom simulations, and partner tools using the same cubelet foundation that powers cubelet.ai. The full platform story — including CubeletCore air-gapped deployment, the platform substrate, and partner tooling — lives at GRID42.ai.
What organizations build
- ✓ Tabletop exercises from live cubelet content
- ✓ Custom compliance simulations for clients
- ✓ Training and consulting partner tools
- ✓ Air-gapped knowledge substrates (CubeletCore)