About NaaIO
Green Power DC
NAAIO ™ is the Execution Control Plane for High-Compute Workloads
Governing cost, placement, compliance, and execution across AI, HPC, simulation, analytics, and sovereign compute.
Target audience: CISOs, CFOs, CTOs, and CIOs seeking a smart, cost-efficient, secure, scalable, and green AI business strategy.
NAAIO offers the strategic blueprint for comprehensive AI transformation, seamlessly integrating cutting-edge neuromorphic orchestration and multi-brand GPU/CPU/NPU clusters with ultra-low-carbon Québec hydro power. Beyond datacenter efficiency, our patent-pending architecture, combined with our robust governance framework (SAFER/CARA), empowers enterprises to intelligently transform business processes. We are your strategic partner, delivering a scalable, efficient, and responsible AI foundation that dramatically reduces your costs and energy consumption and accelerates your journey to AI leadership. NAAIO is the missing governance layer between AI workloads and expensive accelerators.
The Problem with Today's High-Compute Datacenters
Factory-Model Infrastructure Is Failing the Next Era of Compute
Current
The promise of advanced computation—AI, high-performance computing (HPC), large-scale analytics, simulation, and real-time data processing—is constrained by an outdated infrastructure model. Escalating costs, extreme energy demands, and rigid architectures limit access to high-compute capabilities beyond hyperscalers. Small and mid-sized organizations are excluded, while enterprises face growing delays driven by compliance, sovereignty, and operational risk. Despite accelerating demand, high-compute adoption is progressing too slowly, widening the gap between what modern workloads require and what current datacenters can efficiently deliver.
Critical Pain Points
  • Vendor lock-in limits hardware flexibility across CPUs, GPUs, NPUs, accelerators, and emerging architectures.
  • Inefficient, constant power draw drives excessive operating costs and a growing carbon footprint.
  • Idle or stranded capacity cannot dynamically adapt to renewable energy availability or grid conditions.
  • Poor workload-to-hardware matching forces many compute-intensive jobs onto suboptimal, overpowered, or energy-inefficient resources.
Our Mission
NAAIO’s mission is to govern high compute and complex job execution so organizations can run every workload on the most efficient, compliant, and cost-optimized compute—automatically and at scale. We rethink AI for enterprises, because today’s models and infrastructure were never designed for enterprise realities and the current approach is already breaking.
Through new innovations and balanced governance, we give organizations the power, oversight, and control they lack today. Our goal is to make high compute and complex execution truly enterprise-ready: smarter, cost-efficient, secure, and aligned with real operational constraints.
Key Objectives:
Establish High and complex Compute Execution Governance as a new industry standard, ensuring every workload runs on the most efficient and cost-optimized compute.
Deliver structural reductions in compute cost and energy consumption by eliminating GPU waste and optimizing execution across heterogeneous clusters.
Provide sovereign, compliant, and auditable workload execution through deterministic routing, jurisdiction control, and transparent governance.
Transform AI infrastructure into an intelligent, sustainable, and accountable system that serves both operational leaders and financial decision-makers.
In doing so we believe this will increase AI adoption and democratize AI for SMBs.
About NAAIO Datacenters
Built for the Future of Sustainable High Compute
NAAIO was founded on the conviction that AI infrastructure must evolve beyond industrial-era factory models. As workloads diversify—from massive foundation model training to distributed edge inference—and as energy grids transition to variable renewable generation, datacenters need brain-like adaptability, not rigid homogeneity. Our founding team combines expertise in neuromorphic computing, grid integration, and large-scale datacenter operations to deliver infrastructure that thinks.
Located in Québec, Canada, NAAIO leverages one of the world's cleanest energy grids while supporting Canadian and North American AI sovereignty initiatives. We partner with public institutions, research labs, and forward-thinking enterprises to build the next generation of eco-responsible compute. Our roadmap begins with a 20 MW proof-of-concept facility and expands to multi-site neuromorphic campuses, demonstrating that AI can scale without sacrificing the planet.
Neuromorphic Principles
Datacenter architecture inspired by biological neural systems—heterogeneous, event-driven, and energy-aware
Grid Integration
Active participation in renewable energy ecosystems through demand response and frequency regulation
Canadian Sovereignty
Data residency, supply chain independence, and alignment with public sector sustainability mandates
Get Started with NAAIO
Early-stage capacity is limited as we scale our proof-of-concept to multi-site deployment. Partnering now provides priority access, influence on roadmap development, and early-adopter pricing advantages. Whether you need dedicated clusters for foundation model training, shared capacity for inference workloads, or consulting on cloud-to-neuromorphic migration, our team is ready to design your green AI infrastructure.

Limited Initial Capacity: Our first 20 MW facility prioritizes early partners who can help validate neuromorphic orchestration at scale. Reserve your green AI capacity before public availability.
NAAIO's Neuromorphic Solution
Datacenter-Scale Intelligence that thinks like the Brain
NAAIO reimagines AI infrastructure through neuromorphic principles—treating the datacenter itself as an adaptive neural system. Instead of homogeneous GPU farms, we orchestrate heterogeneous "neural populations" of CPUs, GPUs, NPUs, and APUs from multiple vendors, each optimized for specific cognitive functions. Event-driven scheduling inspired by biological energy budgeting routes training jobs to high-power GPUs, inference to efficient NPUs, and preprocessing to CPU clusters—matching intelligence type to silicon architecture.
Our patent-pending orchestration engine continuously reads grid carbon intensity, electricity pricing, and renewable forecasts, then schedules workloads to capture the cleanest, cheapest compute windows. When a model training job can tolerate 2-hour flexibility, we defer it to peak solar or wind generation. When real-time inference demands immediate response, we allocate accordingly. The result: 3–5× energy efficiency improvements compared to traditional monolithic clusters, with vendor flexibility and graceful degradation built into the architecture.
3–5× cost and Energy Efficiency
Heterogeneous hardware and neuromorphic scheduling dramatically reduce power per workload
Multi-Vendor Freedom
Eliminate lock-in with CPU, GPU, NPU, and APU populations from diverse manufacturers
Adaptive Resilience
Graceful degradation when hardware fails or vendors change—no single point of failure
Predictable Performance
Energy-aware routing ensures consistent SLAs while optimizing for carbon and cost
How It Works: Architecture Overview
Four Core Components drive Neuromorphic Intelligence
01
Neuromorphic Orchestration Engine
Patent-pending scheduler that classifies workload urgency and intelligence type, then routes jobs to optimal hardware populations based on minimal common denominator in XPU and energy budget—not just latency. Training a foundation model? Schedule it during overnight hydro surplus. Running real-time inference? Allocate immediately to efficient NPU clusters.
02
Energy Signal Interface
Real-time ingestion of grid carbon intensity, electricity pricing, and renewable generation forecasts. This "sensory" layer tells the datacenter what the energy landscape looks like moment-by-moment, enabling carbon-aware and cost-aware scheduling decisions.
03
Neural Populations
Heterogeneous compute clusters organized by cognitive function: CPU "prefrontal cortex" for control and preprocessing, GPU "motor cortex" for parallel training, NPU "sensory" nodes for efficient inference, edge APUs for distributed intelligence. Each population specializes, reducing wasted general-purpose overhead.
04
Idle-Node Grid Optimization
When the datacenter isn't at full capacity, idle nodes participate in demand response, frequency regulation, and renewable energy absorption—generating revenue while stabilizing the grid. Your unused compute becomes grid infrastructure, not wasted capital.
Customer Benefits at Every Layer
  • Lower cost per inference: Match workload to most efficient silicon, not overprovisioned GPUs
  • Reduced carbon footprint: Capture clean energy windows and avoid fossil peak hours
  • Improved resilience: Multi-vendor populations prevent single-supplier disruption
  • Predictable TCO: Energy-aware scheduling reduces surprise power bills and carbon taxes
  • Grid revenue participation: Monetize idle capacity through demand response programs
Sustainability & Energy Impact
Eco-Responsible AI Hosting powered by Québec Hydro
NAAIO datacenters leverage Québec's 99% hydroelectric grid—one of the cleanest energy sources on the planet. While traditional AI infrastructure in grid-average regions generates 400–500 grams of CO₂ equivalent per kilowatt-hour, our facilities operate at 2–10 gCO₂e/kWh, a 40–250× carbon intensity reduction. This isn't greenwashing through offsets; it's direct, physics-based decarbonization at the point of compute.
Our neuromorphic orchestration amplifies this advantage. By scheduling flexible workloads during renewable surplus periods and deferring non-urgent jobs away from fossil backup generation, we achieve 3–5× energy efficiency improvements over traditional monolithic GPU clusters. The datacenter becomes an active grid participant, absorbing excess renewables, providing frequency regulation, and supporting demand response—turning AI infrastructure into climate infrastructure.
2–10 gCO₂e/kWh
Québec hydro grid vs. 400–500 gCO₂e/kWh fossil-heavy regions
3–5× Efficiency Gain
Neuromorphic scheduling vs. traditional homogeneous GPU fleets
Grid Integration
Demand response and renewable absorption during low-utilization periods
Traditional vs. NAAIO: Carbon and Efficiency Comparison
Use Cases & Ideal Customers
Neuromorphic Infrastructure for Every AI Intelligence Category
Foundation Model Training & Serving
Train and deploy LLMs, multi-modal models, and diffusion networks with 3–5× lower energy costs. Neuromorphic scheduling defers batch training to renewable surplus windows while maintaining real-time inference SLAs. Ideal for AI labs, enterprise platform teams, and model providers seeking "green tier" products.
Sovereign AI Infrastructure
Governments and public institutions building domestic AI capabilities require data residency, vendor independence, and long-term cost predictability. NAAIO's Canadian location, multi-vendor hardware strategy, and energy efficiency align with sovereignty mandates and public procurement sustainability requirements.
Climate & Grid Optimization
Renewable forecasting, power flow analysis, and battery dispatch optimization workloads naturally align with grid-aware compute. NAAIO's energy signal interface and idle-node participation turn your climate modeling infrastructure into active grid support—compute that helps the problem it's studying.
Research Compute for Universities & Labs
Academic institutions face budget constraints and increasing ESG accountability. NAAIO delivers enterprise-grade AI infrastructure at lower TCO through energy efficiency, with transparent carbon reporting that satisfies grant requirements and institutional climate commitments. Priority access for Canadian and Québec-based research partners.
Enterprise AI Platforms
Companies building internal AI platforms for customer service, fraud detection, recommendation systems, and business intelligence need predictable green capacity. NAAIO provides dedicated or shared clusters with SLA guarantees, energy cost predictability, and carbon accounting that rolls directly into Scope 2 and Scope 3 emissions reporting.
Specialized AI Workloads
Healthcare imaging, drug discovery, autonomous systems, and other domain-specific AI applications benefit from heterogeneous hardware populations. Route medical image processing to specialized NPUs, molecular simulations to GPU clusters, and real-time safety systems to edge APUs—all within one neuromorphic datacenter.
Engagement Model & Services
Flexible Pathways from Pilot to Production Scale
NAAIO offers dedicated clusters, shared capacity reservations, and consulting engagements tailored to your AI infrastructure maturity and sustainability goals. Whether you're migrating existing workloads from hyperscale clouds, building sovereign AI capabilities, or launching a new model training initiative, we design custom infrastructure packages that align compute topology, energy budgets, and carbon targets.
Pricing is quote-based, reflecting the heterogeneous nature of neuromorphic architectures and the specific energy optimization opportunities in your workload profile. We don't publish one-size-fits-all pricing because every customer's workload intelligence mix—foundation model training, real-time inference, batch analytics, research compute—demands different neural population configurations and energy scheduling strategies. Our team works directly with your technical and procurement leaders to model TCO, carbon impact, and performance SLAs before commitment.
Service Options
  • Dedicated AI clusters with reserved capacity
  • Shared neuromorphic pools for flexible scaling
  • Green capacity reservations with SLA guarantees
  • Migration consulting for cloud-to-NAAIO transitions
  • Carbon accounting and ESG reporting integration
Assess
Workload profiling and energy opportunity analysis. We map your current AI infrastructure spend, carbon footprint, and workload intelligence types to quantify neuromorphic efficiency gains and TCO improvement.
Pilot
Small-scale deployment in shared or dedicated NAAIO clusters. Run representative workloads for 30–90 days to validate performance, measure energy savings, and refine orchestration policies before full migration.
Scale
Production deployment with committed capacity, SLA guarantees, and ongoing optimization. Continuous energy-aware tuning and hardware population expansion as your AI workloads grow and intelligence categories evolve.
Innovation in AI Governance & Data Flow
NAAIO is constantly innovating introduces groundbreaking technologies (patents pending) enabling responsible and efficient enterprise AI deployments.
AI Compliancy Proxy (ACP)
The ACP is an intelligent intermediary enforcing regulatory compliance, ethical guidelines, and internal policies across your AI workflows. It monitors model behavior, data access, and output, ensuring transparent and accountable AI operations. Critical for mitigating risks in regulated industries, it provides an auditable layer for every AI interaction, ensuring trust and responsible deployment.
Vectorized Data Unit Life Cycle (VDU-LC)
VDU-LC manages the entire data lifecycle within NAAIO's neuromorphic infrastructure, from ingestion and transformation to memory optimization and secure retirement. It ensures data lineage, integrity, and energy-aware processing for vectorized data units. This granular control is vital for maximizing performance and minimizing the environmental footprint of advanced AI applications.
Together, ACP and VDU-LC form a holistic framework, seamlessly integrating compliant AI governance with optimized, energy-efficient data management for the next generation of enterprise AI.

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Enterprise AI Governance:
5 Domains: Sovereignty, Access controls, Fragmentation, Execution, Regulatory
Executive Summary
A secure AI architecture must enforce:
  • Where data can go (sovereignty)
  • Who/what can access models (access governance)
  • How data is protected (fragmentation & privacy)
  • How workloads execute (integrity)
  • How compliance is upheld (regulatory assurance)
Five Core Domains
Sovereignty & Data Boundaries
  • Cross-border data leakage
  • Residency compliance
  • Tenant isolation
Access Control & Model Governance
  • Unauthorized model usage
  • Insider threats
  • Shadow AI
  • Prompt injection & jailbreaks
  • Unauthorized model access
  • Unsafe agent behavior
  • Identity and access traceability throughout pipelines
  • Access preservation and audit trails
  • User action logging and accountability
Fragmentation, Privacy & Minimization
  • PII exposure
  • Data reconstruction
  • Privacy law violations
Execution Integrity & Routing Assurance
  • Unverified compute paths
  • Model poisoning
  • Processing legitimacy
  • Inference tampering
  • Rogue tool calling
  • Policy-violating routing
Regulatory & Audit Compliance
  • GDPR/HIPAA/PCI alignment
  • Audit gaps
  • Continuous compliance
  • Complete lineage and explainability tracking
  • Inference decision path documentation
  • Contractual clause compliance verification
NAAIO's SAFER framework operationalizes all five domains directly inside the compute fabric, ensuring AI adoption is secure, compliant, and auditable from day one.
Pricing Model
NAAIO is a control plane for AI execution. It governs where, how, and on what compute each AI workload runs — before execution — to minimize cost and waste.
What Is an XPU?
1 XPU = 1 high-end accelerator equivalent
NAAIO abstracts:
  • GPUs
  • NPUs
  • AI-optimized CPUs
Customers license execution capacity, not hardware math.
PRICING — PERPETUAL LICENSE (CAPEX)
Includes
  • Static cost-optimized routing
  • Fragment-level execution control
  • Single-cluster deployment
  • Deterministic cost attribution
ANNUAL SUPPORT (REQUIRED)
OPTIONAL MODULES (SUBSCRIPTION)
Example Deployment
100 XPU Enterprise Platform
  • Perpetual License: $700,000
  • Enterprise Support: $140,000 / year
  • Predictive Optimization: $100,000 / year
Year-1 Total: $940,000
Ongoing Annual: $240,000
Why This Works Financially
  • CAPEX license avoids perpetual usage tax
  • OPEX limited to support + optional value
  • No per-token, per-job, or per-hour pricing
  • Scales with infrastructure, not usage spikes
  • Fits procurement, audit, and finance models
Who This Is For
  • Enterprises running internal AI platforms
  • Regulated industries (finance, insurance, government)
  • Hyperscalers and AI OEMs
  • Organizations with >$1M/year AI compute spend
Call to Action
  • Start with XPU-10.
  • Prove savings.
  • Scale only when justified.
SAFER™ Framework: The Complete Governance & Audit Toolkit (CARA ™ ) for AI
What is SAFER™?
SAFER (Secure AI Framework for Enterprise Risk) is a comprehensive, end-to-end control architecture that defines governance, security, compliance, privacy, sovereignty, and eco-responsibility requirements for enterprise AI adoption.
What is CARA™?
CARA (Critical AI Risk Audit Framework) is the formal assurance methodology used to evaluate the readiness, resilience, and compliance posture of enterprise AI pipelines. CARA concentrates on ten critical domains of AI risk.
The Combined Ecosystem
Together, SAFER and CARA create a complete governance and assurance ecosystem:
SAFER defines what must be controlled
CARA defines how those controls must be verified
White Papers & Resources
Enterprise AI Governance, Risk, and Compliance Frameworks
Contractual and Regulatory Risks in Enterprise AI and SaaS-Based AI Services
Covers DPA compliance, EU AI Act obligations, contractual processing rights, and SaaS-specific risks.

Financial Governance Risks in AI Adoption
Addresses cost volatility, budget overruns, development vs production cost misalignment, and financial planning.

GPU or Not GPU? Optimizing Compute Resources for Enterprise AI Workloads
Examines when GPUs are necessary vs inefficient, cost implications, and sustainable compute selection.

Lineage and Explainability in AI Pipelines
Foundational requirements for trust, compliance, and operational integrity in complex AI systems.

NAAIO vs DevOps: Why Design-Time Isolation Fails at Execution Time
Formalizes the gap between conceptual DevOps isolation and execution-time reality, enumerating common misconceptions and introducing NAAIO as an execution-layer governance system.

NAAIO Optimization Layers: L2, L3, and L3+ — Architecture, Cost Benefits, and Limitations
Explores NAAIO's layered optimization model that progressively improves cost efficiency, predictability, and control by acting at different points in the workload lifecycle.

OCR & Vector Databases as Critical Data Leakage Risks
Compliance risks in SaaS-based solutions and balancing accuracy with regulatory requirements.

Tenant Isolation Failures in AI Systems
Contractual, security, and regulatory risks in multi-tenant architectures.

AI Risk in the Enterprise: A Neutral Overview of Systemic Threats
Comprehensive overview of financial, security, data integrity, RAG, vector DB, input/output, IAM, lifecycle, and ecological risks.

SAFER-CARA Framework: Secure AI Framework for Enterprise Risk
Complete governance and assurance ecosystem with 10 domains and audit methodology.
Our History
It all started with a need to build an eco-friendly AI infrastructure which would also democratize AI adoption for SMBs. Project codename @Robinhood (Internal Codename) - An internal engineering initiative focused on democratizing AI for SMBs ended up in an enterprise grade control plane for AI execution. We tasked our team with building a platform that removes the cost, complexity, and governance barriers preventing smaller organizations from adopting AI at scale.
Outcome: A patented, enterprise-grade AI orchestration and governance architecture designed to make AI accessible, affordable, secure, and operationally viable for SMBs — unlocking a massive underserved market segment.
CFO ROI Sidebar — Why NAAIO Pays for Itself
1. Direct GPU Waste Reduction (30–55%)
Most enterprises overprovision GPUs and allow workloads to run monolithically. NAAIO reduces GPU-hour consumption by routing fragments to the lowest-cost XPU that meets SLA.
Typical savings:
  • 30% reduction in GPU hours in month 1
  • Up to 55% reduction after full workload decomposition
For an organization spending $3M/year on GPUs, this yields: $900,000–$1,650,000 annual savings.
2. Faster AI Throughput Without New Hardware (10–25%)
By eliminating thermal bottlenecks and idle cycles, NAAIO increases throughput of existing clusters.
Outcome: delay or avoid major GPU purchases. A single avoided GPU rack refresh typically saves $500,000–$1.2M.
3. CAPEX Instead of OPEX
CFOs prefer AI governance tools that produce one-time capitalizable value, not recurring usage fees.
NAAIO license = capitalizable asset This converts unpredictable AI OPEX into a predictable, auditable cost center.
4. Predictable OPEX for Support Only
Annual support (15–25%) behaves like software maintenance—not usage tax.
This protects budgets from:
  • Runaway training experiments
  • Seasonal AI spikes
  • Shadow IT workloads
5. Full Chargeback Accountability
NAAIO enables tenant, model, and team-level cost attribution, eliminating ungoverned spend.
Organizations typically recover 10–20% of their budget by reallocating misattributed GPU consumption back to internal owners.
6. Simple ROI Calculation
ROI = (Annual Compute Savings – Annual OPEX) / Total Investment
Using a conservative example:
ROI in Year 1: 115% (pays back in ~7–8 months)
ROI Year 2+: 337%+ yearly, because the perpetual license has already been paid.
7. Strategic CFO Benefit
NAAIO creates structural cost advantages:
  • Lowered cost per model served
  • Improved AI margins
  • More predictable long-range planning
  • Compliance-ready compute governance
  • Reduced risk of GPU inflation or vendor lock-in
CFO Summary:
NAAIO reduces AI compute spend by 30–55%, pays for itself within the first year, and permanently improves cost structure across all AI workloads.