GPU infrastructure that powers your models from training to production
We partner with AI teams to build production-grade GPU infrastructure—from single clusters to multi-region training fabrics. Your models deserve infrastructure that just works.
Built for AI teams who need infrastructure they can trust
We partner with you from initial design through production deployment—handling the complexity so your team can focus on building breakthrough models.
AI POD Build-Out
Rack planning, power distribution, cooling design, and GPU node provisioning. We've deployed everything from 8-GPU dev clusters to 512-GPU training installations.
Network Fabrics
Your GPUs are only as fast as the network connecting them. We design high-bandwidth interconnects—RoCE, InfiniBand, or hybrid—that keep your training runs at >95% utilization.
Orchestration Layer
Whether you need Kubernetes for flexibility, Slurm for batch jobs, or Ray for inference—we configure the orchestration that matches how your team actually works.
Validation & Testing
Burn-in tests, performance benchmarks, and acceptance criteria before handoff. We prove your infrastructure performs before you start training.
Observability
Know exactly what's happening in your training runs. We set up real-time dashboards showing GPU utilization, network health, and token throughput—so you can spot issues before they cost hours of compute.
Security & Compliance
Network isolation, access controls, audit logs, and compliance frameworks for regulated environments. Built-in from day one, not bolted on later.
From design to deployment
We work alongside your team—whether you need a complete build-out or help with specific infrastructure challenges.
Architecture Design
Reference architectures, capacity planning, and vendor selection based on your workload, budget, and timeline.
Build & Integration
Physical installation, network configuration, orchestration setup, and automation with full documentation.
Validation Testing
Performance benchmarks, failure testing, and acceptance criteria that prove production readiness.
Ongoing Operations
Runbooks, monitoring setup, incident response guidance, and continuous optimization for cost and performance.
Domain-specific models built for production
We don't just build infrastructure—we also fine-tune and deploy custom models for finance, healthcare, and industrial applications.
Financial Services
Custom models for fraud detection, risk scoring, trading signals, and regulatory compliance. Trained on your data, deployed on secure infrastructure.
- Real-time fraud detection with sub-100ms latency
- Credit risk models with explainable outputs
- Market sentiment from news and social feeds
- Regulatory document extraction (10-K, 10-Q)
- Customer churn prediction
Healthcare & Medical
HIPAA-compliant models for clinical decision support, medical imaging, and patient data processing. Privacy-preserving techniques built in from the start.
- Medical image analysis (X-ray, CT, MRI)
- Clinical notes extraction and coding
- Drug-drug interaction predictions
- Patient readmission risk stratification
- Automated medical billing and coding
Industrial & Manufacturing
Predictive maintenance, quality control, and supply chain optimization models trained on sensor data and operational logs.
- Equipment failure prediction and anomaly detection
- Visual quality inspection with computer vision
- Demand forecasting across supply chains
- Process parameter optimization
- Sensor data analysis and root cause detection
Fine-Tuning & Adaptation
We use parameter-efficient techniques (LoRA, QLoRA, prefix tuning) to adapt foundation models to your domain without full retraining.
Domain Adaptation
Continued pre-training on industry-specific text to build domain knowledge
Task-Specific Tuning
Supervised fine-tuning on labeled data for classification, extraction, and generation
RLHF & Alignment
Human feedback and DPO to align model outputs with business requirements
RAG Architectures
Retrieval-augmented generation pipelines that ground model outputs in your proprietary data—reducing hallucinations for knowledge-intensive tasks.
Vector Search
Pinecone, Weaviate, or Milvus with semantic search and hybrid retrieval strategies
Multi-Modal RAG
Combine text, images, tables, and structured data for comprehensive context
Real-Time Updates
Streaming ingestion for continuous knowledge base updates with low latency
Build AI Agents That Actually Work
We help you build AI agents that can think through problems, use tools, and get real work done. Whether you're automating customer support, research tasks, or complex business processes—our agents handle the reasoning, planning, and execution so your team can focus on what matters.
Real visibility into your AI workloads
From ingress to inference, we instrument every layer of your stack. Token counts, TTFT, GPU utilization—all in real-time dashboards.
GPU Telemetry
Per-GPU utilization, memory, temp, power draw, and throttling events with anomaly detection and alerting.
Network Health
Fabric throughput, latency histograms, packet loss, and congestion signals for distributed training debugging.
Model Metrics
Inference latency, throughput, batch efficiency, and per-request costs across all serving endpoints.
Distributed Tracing
Request flows from ingress through orchestration to GPU workers with span timing for bottleneck analysis.
Modern observability stack
Prometheus, Grafana, Jaeger, and ELK integrated with GPU exporters (DCGM, NVML) and custom dashboards for LLM workloads.
A microscope for viewing inside LLMs
Inspect model internals, identify learned features, and debug failure modes. Tools for attention analysis, activation inspection, and circuit discovery.
Attention Patterns
Visualize attention across layers and heads to understand information flow and reasoning paths through the network
Activation Analysis
Monitor neuron activations to understand feature detection, representation learning, and concept formation
Circuit Discovery
Identify computational sub-circuits responsible for specific behaviors and model capabilities at scale
Production Tools
Deploy interpretability monitoring in production to catch model degradation and unexpected behaviors
From GPU pods to production workloads
A production AI platform is more than just GPUs. It's compute, networking, orchestration, observability, and validation—all working together as a system.
AI POD Infrastructure
Physical build-out with rack planning, power/cooling, GPU provisioning, and repeatable deployment patterns for horizontal scaling
High-Bandwidth Fabrics
Front-end service networks and back-end training fabrics (RoCE/IB) tuned for distributed workloads and minimal latency
Testing & Validation
Performance benchmarks, burn-in tests, and acceptance criteria that prove throughput, stability, and production readiness
Talk to an engineer
Tell us about your GPU count, workload type, facility constraints, and timeline. We'll respond with an architecture proposal and delivery plan.
Or email us directly: contact@ariseai.net
- Target scale (8/32/128/512 GPUs)
- Workload type (training, inference, both)
- Network requirements (Ethernet/RoCE/IB)
- Environment (on-prem, colo, hybrid) and timeline
We'll respond within 24 hours