for the Enterprise
HyperCore
Enterprises operating at hyperscale face a new reality: intelligence alone is no longer a differentiator—performance is. HyperCore is the GPU-native operating layer for the autonomous enterprise.
Key Benefits
GPU-Native
Performance
NVIDIA full stack.
Cycle Speed
40-60%
Lower dev cycles.
Latency
Ultra-Low
TensorRT optimized.
Reliability
Industrial
Production uptime.
Efficiency
Optimized
Lower compute spend.
Agivant's Advantage
Built on NVIDIA’s stack to transform fragmented AI initiatives into a cohesive core.
1
Build
Accelerated deployment through Agivant’s POD-based execution model.
2
Deploy
Production-grade speed and reliability on NVIDIA’s computing stack.
3
Run
Efficient, high-performance intelligence core for hyperscale agents.
Industry Evolution
Reframing AI as foundational operating infrastructure for machine-speed execution.
Infrastructure Design Principles
Built on NVIDIA’s stack to transform fragmented AI initiatives into a cohesive core.
1
Latency
Designed out
No tuning later.
2
Real-Time
Execution
In-flight reasoning.
3
Optimization
NVIDIA-Native
CUDA first.
4
Governance
Outcomes
Human monitored.
Agile POD-Based Delivery Model
Structuring Teams for High-Performance AI Acceleration
Agivant organizes work into scalable, skill-combination PODs—small, cross-functional autonomous teams (6–12 people) operating on Agile principles. This model enables fast iteration, rapid prototyping in the HRP Lab, and reliable production delivery for enterprise AI systems.

Automation
CoE
Primary Mandate
“Enterprise standards, reusable frameworks, and governance.”Provides standards, reusable frameworks, and governance across the enterprise. Focuses on Agentic AI agents, RPA + AI hybrids, and scalable automation patterns.
Key Roles
- Agentic AI frameworks and guardrails
- RPA + AI hybrid architectures
- Event-driven AI (Kafka, API orchestration)
- DevOps pipelines with GPU support (CI/CD, NVIDIA Docker)

Development
PODs
Primary Mandate
“Rapid build and deployment of production-grade AI systems.”Cross-functional teams building custom LLMs or deploying agentic bots, leveraging NVIDIA hardware to achieve 5–10× faster development cycles.
Key Roles
- Solution Architect – CUDA-optimized architectures
- AI Engineers – LLM fine-tuning with TensorRT-LLM
- Data Engineers – GPU-accelerated ingestion (cuDF, RAPIDS)
- Developers / QA – PyTorch + CUDA testing pipelines

Sustenance
PODs
Primary Mandate
“Long-term performance, reliability, and cost efficiency.”Dedicated teams responsible for ongoing operations, monitoring, and post-go-live enhancements.
Key Roles
- L2 / L3 Support – Model drift management
- Performance tuning of inference servers
- Program management of GPU cluster scale
- Integration and stress testing
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.
Market Validation
Pattern-based outcomes from Fortune-scale deployments.
Enterprise AI
Platforms
Global AI Product Leader
Technology &
Semiconductor
Global Semiconductor Technology Company
Cloud Platforms & AI
Infrastructure
Global Cloud & AI Infrastructure Provider
Client Environment
Global Semiconductor Technology Company
Strategic Partner
Unified Data Platform Transformation
Challenge
Fragmented observability, manual pipeline restarts, and over-provisioned cloud
infrastructure were driving cost overruns and operational inefficiencies.
Agivant Solution
Fragmented observability, manual pipeline restarts, and over-provisioned cloud
infrastructure were driving cost overruns and operational inefficiencies
Minutes vs Hours
Recovery Time
Zero
Overruns
Cloud Spend
AI-Ready
Foundation
Readiness