Tools, schemas, and operating frameworks for evaluating and improving AI readiness in small and midsized enterprises. Designed to help organizations transition from fragmented AI experimentation to coherent, scalable AI infrastructure.
Most organizations are adopting AI, but few are building the underlying infrastructure needed to scale it safely, reliably, and coherently.
This repository highlights selected components of the diagnostic and operating model frameworks used at Nadis Intelligence™ to help leadership teams:
- Identify infrastructure gaps before scaling breaks
- Evaluate AI readiness using structured, repeatable input schemas
- Replace reactive tool sprawl with coherent operating models
- Improve decision velocity and integration reliability
This repo focuses on the infrastructure layer required for sustainable AI adoption—not the applications themselves.
| File/Folder | Description |
|---|---|
docs/ai-operating-model-canvas.md |
Explanation of the AI Operating Model Canvas. |
assets/ai-operating-model-canvas.png |
Static Canvas image used in readiness reviews. |
templates/infrastructure-checklist.json |
Infrastructure readiness checklist. |
templates/signal-input-schema.json |
Diagnostic schema used in scoring and infrastructure assessments. |
docs/framework-3-symbioses.md |
The Three Symbioses™ — core framework for infrastructure coherence. |
docs/diagnostic-dimensions.md |
Definitions of AI maturity and infrastructure readiness dimensions. |
All links and file references preserved.
These frameworks support advisory and infrastructure design work at Nadis Intelligence:
A structured assessment used to measure infrastructure coherence and identify scaling risks before AI adoption accelerates.
Used in consulting engagements to align workflows, governance, tools, and data paths into a coherent operating blueprint.
Templates and schemas are applied during readiness evaluations for small and midsized businesses preparing for AI deployment.
These tools serve as foundational materials in AI readiness bootcamps and leadership workshops focused on operational alignment.
AI isn’t just a model problem — it’s an infrastructure problem.
Organizations that adopt LLMs without addressing fragmented workflows, misaligned systems, and unclear ownership accumulate incoherence debt:
- duplicated workflows
- redundant tooling
- integration failure points
- unpredictable AI behavior
- misaligned data flows
- high operational cost
This repository captures the scaffolding required to move from reactive AI adoption to infrastructure-first AI design.
Nadis Intelligence helps scaling organizations build coherent, adaptive infrastructure that supports sustainable AI adoption.
Our work focuses on:
- AI infrastructure strategy
- Workflow and system architecture
- Readiness diagnostics and scoring
- Operating model design for scale
Learn more → https://nadis.ai Contact → hello@nadis.ai
We welcome collaboration with:
- VCs supporting AI adoption across portfolios
- Innovation teams evaluating operational readiness
- Advisors designing AI operating models
License: MIT for code and templates. Frameworks (e.g., The Three Symbioses™, SCALE Factor™) remain proprietary to Nadis Intelligence.
Ariana Abramson, MSc Founder, Nadis Intelligence AI Infrastructure Architect • Systems Designer • Startup Operator
“AI succeeds when the infrastructure underneath it is coherent. Infrastructure isn’t overhead—it’s leverage.”