Ready to get started?
Deploy sovereign AI on your infrastructure - in weeks, not months.
NVIDIA × Katonic · Elite alliance
Enterprises buy NVIDIA GPUs. Katonic turns them into governed agents that employees actually use - with per-dollar cost attribution back to the GPU.
NAT for agent brains. NeMo for safety. KAI for scheduling. NIM for serving.
Katonic answers all three. The platform takes raw NVIDIA infrastructure and delivers governed, production-ready AI agents to every employee, with per-agent cost attribution back to the GPU.
Not a partnership badge. Architectural foundation. Every layer of the platform integrates NVIDIA technology.
NAT
NVIDIA Agent Toolkit · agents on NAT
Every agent built in Katonic runs on NAT by default. The brain of every AI agent is NVIDIA technology.
Primary runtime. ReAct loop with LLM + MCP tool calling. 5 framework adapters. All 5 build paths produce NAT packages.
NeMo
NeMo Guardrails · safety rails
Every message through every agent passes through guardrails. Dual-layer safety: NAT middleware + API gateway.
NAT middleware (content_safety_guard, pii_defense, output_verifier) with dedicated guard LLM. 11 configurable rails across input, output, and tool categories.
KAI
KAI Scheduler · GPU scheduler
GPU scheduling that prevents resource contention. Prod gets guaranteed allocation, dev gets best-effort.
Open-sourced from Run:ai (Apache 2.0). Hierarchical queues map to org/env structure. Gang scheduling. GPU fractions. Bin-packing.
NIM
Inference Microservice · per-token
Self-hosted models run on NIM. Zero per-token cost. Data never leaves your infrastructure.
AI Gateway routes to NIM endpoints. K8s auto-discovery detects NIM pods. Tier resolver prefers NIM for on-prem. HPA auto-scales.
GPU Op.
NVIDIA GPU Operator · GPU families
GPU infrastructure managed automatically. MIG slicing without manual node configuration.
MIG manager DaemonSet. ClusterPolicy. Supports A100, H100, H200, B200, GH200, GB200, T4, L40S.
vLLM
vLLM on NVIDIA GPUs · fine-tuning
Open-source inference complementing NIM. Fine-tuned models deploy from MLflow with full lineage.
Default serving for open-source models. Tensor parallelism, quantization (GPTQ, AWQ), HPA auto-scaling. LoRA/QLoRA fine-tuning on GPU.
NVIDIA NeMo Agent Toolkit (NAT) is the agent runtime under Katonic. It's the same toolkit Adobe, Salesforce, and Atlassian use to power their AI agents - production-hardened, framework-agnostic, optimized for cost and latency. We integrate NAT into a complete sovereign platform: deployed in your perimeter, governed by Control Room, observed end to end.
You don't have to assemble NAT yourself. Katonic ships it pre-integrated with the gateway, the knowledge engine, the guardrails, and the audit log.
Production hardening
Turn agent demos into systems that pass audits. Three-layer guardrails plus secure runtime. Full execution traces - prompts, tool calls, model outputs, every decision. LLM-as-a-judge evaluators and RAGAS for systematic quality measurement.
Performance optimization
Agent Performance Primitives accelerate graph-based agents through parallel execution, speculative branching, and node-level priority routing. Workflow profiler suggests the right model for each step. Documented 50% query cost reduction with NVIDIA Nemotron.
Framework interoperability
Build with the framework your team knows. NAT is the unifying runtime - agents from LangGraph, CrewAI, Google ADK, or raw Python all run under the same governance, observability, and audit log. Configuration-driven (YAML) so you can reconfigure without rewriting.
Enterprise scale
Specialist agents that collaborate on complex workflows - reasoning, planning, retrieval, action - rather than one giant model trying to do everything. Production traces feed the data flywheel: real usage continuously improves your fine-tuned models, in your environment.
Same foundation, integrated differently. Adobe, Salesforce, and Atlassian use NAT to power their AI products. Katonic uses NAT to power your sovereign AI factory.
See the agent architecture →85% of organizations grew AI allocations in the past 12 months. Every GPU must prove ROI. Katonic tracks cost per agent, per department, per GPU - and attributes business value back to the infrastructure.
Deploy on NIM - zero per-token cost
Self-hosted models on NIM eliminate API spend. AI Gateway routes to NIM first, cloud providers as failover. Your GPUs serve the inference.
KAI Scheduler maximizes utilization
Hierarchical queues prevent GPU waste. Production workloads get guaranteed allocation. Dev and test use spare capacity. Significant utilization improvement through bin-packing and fair scheduling.
Per-agent cost attribution (OpenCost)
Every agent's GPU time, LLM tokens, and infrastructure cost tracked. Know exactly which agents deliver ROI.
NVIDIA DGX / HGX / Cloud GPU
Hardware investment
KAI Scheduler + MIG Slicing
Maximize utilization
NIM + vLLM Model Serving
Zero per-token inference
NAT Agent Runtime
Governed agent execution
NeMo Guardrails
Safety on every message
Katonic Platform (21 services)
Full-stack enterprise AI
Workroom · Studio · Control Room
Use · Build · Govern
NAT middleware (LLM-based) and pattern-based pre-screening run on every message
Not bolted on. Embedded at every layer. Every agent workflow includes NAT guardrail middleware with a dedicated guard LLM for model-based classification. Pattern-based pre-screening at the API gateway adds defense in depth. Per-tenant profiles with RBAC.
NAT Middleware (LLM-based)
Content safety, PII defense, output verification, and grounding checks run inside every agent workflow via a dedicated guard LLM routed through AI Gateway.
API Gateway Pre-screening
Fast pattern-based checks at the API boundary before agents execute. Injection detection, PII scanning, and tool-call validation (SQL injection, path traversal, command injection).
Enterprise Orchestration
Per-tenant guardrail profiles with RBAC. Admin UI for configuration. Activity tracking and stats. Fail-closed enforcement on agent-to-agent traffic.
Estimate GPU utilization improvement, API cost savings, and per-agent cost.
Inputs
Projected outcomes
GPU utilization
52%
20% baseline
Annual API savings
$120,000
from cloud spend
Cost per agent / yr
$500
fully-loaded
Time to first agent
90 days
industry: months
⚠ Estimates based on typical enterprise deployments. Actual results vary by configuration and workload.
GPU
H100
80 GB
1/2, 1/3, 1/4, 1/7
GPU
H200
141 GB
1/2, 1/3, 1/4, 1/7
GPU
A100
40/80 GB
1/2, 1/3, 1/4, 1/7
GPU
B200
192 GB
Full or KAI fractions
GPU
GH200
96 GB
Full or KAI fractions
GPU
GB200
384 GB
Full or KAI fractions
GPU
L40S
48 GB
Full or time-slicing
GPU
T4
16 GB
Full or time-slicing
“We bought DGX systems for AI. For a year they sat at 15% utilization because no one could bridge the gap between GPUs and business value. Katonic got us to production agents in 90 days with measurable ROI per GPU.
A 30-minute assessment: your GPUs, your data, your timeline to production agents.
See NAT, NeMo, KAI, and NIM working together on your infrastructure.
