Build it. Train it.
Run it on your hardware.
We provision GPU servers, engineer custom deep learning models, deploy self-hosted LLMs, and operate production AI infrastructure for enterprises. No cloud lock-in. No per-token pricing.
From bare metal to production AI
Six core service lines — pick what you need. We've shipped them all.
GPU & CUDA Server Provisioning
End-to-end build, hardening and tuning of NVIDIA GPU servers for production AI workloads. Driver stack, CUDA toolkit, cuDNN, NCCL — done right the first time.
- NVIDIA driver + CUDA + cuDNN + NCCL install
- Multi-GPU NVLink / PCIe topology validation
- Container runtime (Docker + nvidia-container-toolkit)
- Performance benchmarking & power tuning
Deep Learning Engineering
Custom model design, training pipelines, and fine-tuning. From computer vision to NLP to multimodal systems — built for your data, your domain, your accuracy targets.
- Custom CV / NLP / multimodal model design
- PyTorch + TensorFlow training pipelines
- Distributed training (DDP, FSDP, DeepSpeed)
- Quantization, pruning, ONNX/TensorRT optimization
LLM Deployment & Hosting
Self-host open-source LLMs (Llama, Mistral, Qwen, DeepSeek) on your own hardware. vLLM, llama.cpp, SGLang — production-grade inference at a fraction of API costs.
- vLLM / SGLang / llama.cpp deployment
- Tensor parallel + speculative decoding setup
- OpenAI-compatible API gateway
- Token throughput & latency SLAs
AI Strategy & Solutioning
We work with your team to find the highest-leverage AI applications inside your business — and build a 30/60/90 plan to ship them.
- AI opportunity audit & ROI modeling
- Build vs buy vs hybrid recommendations
- Privacy, compliance & risk assessment
- Roadmap with measurable milestones
AI Inference Infrastructure
Production inference clusters with auto-scaling, queueing, A/B model routing, observability, and zero-downtime deploys. Built on Kubernetes or bare metal.
- Kubernetes / KServe / Triton deployment
- Auto-scaling and request batching
- Model registry & A/B routing
- Prometheus + Grafana observability
MLOps & Pipelines
Versioned datasets, reproducible training runs, automated evaluation, model registries, and CI/CD for ML. Get your team out of notebook hell and into production.
- Data versioning (DVC / LakeFS)
- Experiment tracking (MLflow / W&B)
- Model registry + automated promotion
- CI/CD for retraining + canary deploys
Battle-tested tools we deploy daily
A clear path from idea to production
Discover
Free consultation to understand your goals, data, and constraints.
Design
Architecture proposal with timeline, hardware specs, and budget.
Build
We engineer, train, and deploy on your infrastructure.
Launch
Production rollout with monitoring, runbooks, and team training.
Talk to an AI engineer
Free 30-minute consultation. We'll discuss your use case, hardware needs, and the fastest path to a working prototype.