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.
DataPulse reads your scheduler, your cooling, your grid, and your history — and tells you exactly what to change, why, and what it will cost you if you don't.
Your GPU cluster bleeds power while cooling plays catch-up. Pitstop reads SLURM scheduler intent at T−45s — before any sensor fires — pre-warming CDUs, capping wattage, and auto-enrolling demand response revenue against ERCOT, CAISO, and PJM grids.
reduction
feed-forward
per cluster/yr
w/ optics
Ecosystem & target markets
Company names shown are independent trademarks of their respective owners. No partnership, affiliation, or endorsement is implied or claimed.
Accepting 3 design partners
for Q3 2026
90-day paid pilot. We deploy DataPulse at your cluster, you measure the PUE improvement and grid savings. No measurable results — no charge.
Typically responds within 48 hours
We could degrade our performance, reduce our power consumption and provide for a slightly longer latency response when somebody asks for an answer.
CEO, NVIDIA
Public remarks — Lex Fridman Podcast #494
(Quoted as general industry context. NVIDIA has no affiliation with AriseAI.)
Be a founding design partner
We are accepting 3 design partners for Q3 2026. 90-day paid pilot. Your name goes here — with your permission — once you've seen the results.
⚡ Apply for the pilotWhat changes the day PitstopPUE goes live
256-GPU H100 cluster · mixed training + inference · 90-day pilot baseline vs. Pitstop operational period.
DR + cooling + demand charges · ERCOT & CAISO
SLURM scheduler-intent signal
Docker deploy · zero config
Software-only · BACnet/Modbus
3 DESIGN PARTNER SLOTS REMAINING · Q3 2026
The infrastructure that Pitstop runs on
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
Talk to an engineer
Tell us your cluster size, workload type, and timeline — we respond within 24 hours.