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Agivant NVIDIA
HyperCore

The accelerated intelligence core for the enterprise
Enterprises running AI at hyperscale find that performance decides what reaches production. HyperCore is the GPU-native operating layer for the autonomous enterprise, built on the NVIDIA stack.

Key Benefits

GPU-Native

Full stack

Built across the NVIDIA stack
GPU-Native Performance
Cycle Speed

40 to 60%

Shorter development cycles
Cycle Speed 40–60%
Latency

Low latency

Inference tuned with TensorRT
Ultra-Low Latency
Reliability

Industrial

Uptime held in production
Industrial Reliability
Efficiency

Optimized

Lower compute spend.
Optimized Efficiency

Agivant's advantage

Agivant builds on the NVIDIA stack, so scattered AI work runs as one core.

Build

Agivant deploys through a POD-based execution model.

Deploy

Production-grade speed and reliability on the NVIDIA computing stack.

Run

An efficient, high-performance intelligence core for hyperscale agents.

Where the stack is heading

AI runs as operating infrastructure, at machine speed.

✕Where most enterprises are

  • AI as Software
    An application layer managed as a silo
  • Experimentation
    Prototype projects that stop short of industrial scale
  • Human Bottlenecks
    Cycles that wait on manual steps

✓Where HyperCore runs

  • AI as Core Infrastructure
    GPUs as first-class citizens in the stack
  • Industrialized Intelligence
    Production-grade throughput, with uptime measured
  • Autonomous Execution
    Machine speed with enterprise-grade trust
Industry Evolution decorative ribbon

Infrastructure design principles

Four choices made before the first line of the build.
Latency

Designed in

Performance settled up front
Real-Time

Execution

Reasoning in flight
Optimization

NVIDIA-native

CUDA first
Governance

Outcomes

People stay in the loop

Agile POD-based delivery model

How Agivant structures teams for GPU work

Agivant organizes work into PODs, small cross-functional teams of six to twelve people working on Agile principles. Each POD iterates fast, prototypes in the HRP Lab, and takes enterprise AI systems into production.
Mandate
Enterprise standards and reusable frameworks
Sets standards and reusable frameworks across the enterprise, covering agentic AI, RPA and AI hybrids, and scalable automation patterns

Key skills

• Agentic AI frameworks and guardrails
• RPA + AI hybrid architectures
• Event-driven AI (Kafka, API orchestration)
• DevOps pipelines with GPU support (CI/CD, NVIDIA Docker)
CUDA-native development, multi-GPU training, and TensorRT-optimized inference pipelines enabling rapid experimentation and production readiness.
Horizontal DevOps, MLOps, and Platform Engineering teams manage NVIDIA-powered environments to ensure enterprise-wide scalability and operational consistency.

Capability portfolio

Production-ready capabilities for generative and agentic systems.
Accelerated LLM Training & Fine-Tuning

Accelerated LLM Training & Fine-Tuning

Custom domain models
Agivant builds and fine-tunes domain-specific LLMs that carry your business context.
CUDA Optimization: PyTorch/TensorFlow optimized for A100/H100 GPUs, bringing training time from weeks to hours .
Rapid Iteration: Validated in our Hyperintelligent Rapid Prototype Lab (HRP Lab).
High-Performance Inference for Agentic AI

High-Performance Inference for Agentic AI

Real -tim reasoning and autonomous action
Agivant builds agentic AI systems that respond fast enough to hold a conversation
Production Latency: Using TensorRT and Triton Inference Server for multi-step agents.
Scalable: Optimized for Salesforce Agentforce integrations and omnichannel bots.
GPU-Accelerated Data Processing

GPU-Accelerated Data Processing

Next-gen platforms for real-time insights
GPU processing for enterprise data that moves faster than CPUs can handle
RAPIDS :cuDF accelerates ETL pipelines and feature engineering.
Industry Impact: Processing terabytes of data in near real time for BFSI and eCommerce.
Computer Vision & Multimodal AI

Computer Vision & Multimodal AI

Beyond text, into visual and multimodal understanding
Extending enterprise intelligence beyond text into visual and multimodal understanding.
Visual Workloads: High-speed OCR, visual search, and video analysis using cuDNN.
Multimodal GenAI: text, image and audio workflows in one pass.
Infrastructure Optimization

Infrastructure Optimization

Performance and cost across GPU clouds
Ensuring optimal performance and cost efficiency across GPU-powered cloud environments.
Cloud agnostic: workloads tuned on Azure AI and AWS SageMaker GPU instances
MLOps: Dynamic scaling to prevent waste while guaranteeing CUDA performance.
Advanced Model Distillation & Efficiency

Model distillation and efficiency

Key NVIDIA tools and frameworks
Creating compact, high-performance models optimized for enterprise-scale deployment.
NVIDIA NeMo & Aligner: Enables Knowledge Distillation (KD) via TensorRT ModelOpt and supports KD during Supervised Fine-Tuning (SFT).
TAO & Minitron: Pruning and distilling larger models (e.g., Llama-3.1 8B → 4B) using Logits-Distillation (KL-Divergence).

Where this runs today

Patterns proven at Fortune-scale.
Enterprise AI Platforms

Enterprise AI Platforms

Global AI
Product Leader
Technology & Semiconductor

Technology & Semiconductor

Global Semiconductor
Technology Company
Cloud Platforms & AI Infrastructure

Cloud Platforms & AI
Infrastructure

Global Cloud & AI
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