Intelligence, Distilled.
Autonomous AI agents, powered by Blackwell-class GPU systems. Run 235B-parameter models locally at production speed. Multi-engine orchestration across local inference, cloud APIs, and CLI agents — unified by a single framework.
About PureTensor
We build autonomous AI agents that run on your hardware, not someone else's cloud. PureTensor develops agentic systems powered by large language models running locally on NVIDIA Blackwell GPUs — from email automation and voice intelligence to security testing and strategic analysis. Our agents operate 24/7 in production, with human-in-the-loop controls and zero cloud dependency for sensitive workloads.
Ecosystem
Three verticals, one agentic AI platform. Autonomous cybersecurity, open-source agent orchestration, and strategic intelligence — all powered by on-premise Blackwell inference.
Capabilities
PureTensor builds AI agents that reason, act, and learn autonomously. Cloud-positive yet self-contained by design, our agents run large language models locally with full tool calling, voice transcription, and multi-modal perception.
- Multi-engine agent orchestration — 7 LLM backends, swappable with one environment variable.
- Local inference at scale — 235B-parameter models on NVIDIA Blackwell at 70 tokens/sec.
- Multi-modal pipelines — voice (STT/TTS with cloning), vision (OCR), and text generation.
Agent Products
- PureClaw — open-source multi-engine agentic framework (924+ tests, production 24/7).
- Voice KB — speech-to-knowledge pipeline (1,200+ topics, 1,000+ entities indexed).
- Kalima — multilingual code-switching dictation for Arabic speakers.
Tensor // Core
The Blackwell-class GPU system powering PureTensor's autonomous agents. Multiple RTX PRO 6000 Blackwell Workstation Edition GPUs with hundreds of gigabytes of unified VRAM run 235B-parameter models at production speed — enabling agentic reasoning, tool calling, voice synthesis, and multi-modal perception on owned hardware.
- Blackwell Architecture— multiple RTX PRO 6000 workstation GPUs powering local AI agents
- Production Inference— 235B-parameter MoE models at 70 tokens/sec, 100% GPU-resident
- Multi-Model Serving— LLM, Whisper STT, XTTS voice cloning, and OCR running concurrently
- Portable Artifacts— agents developed locally, deployable elastically to the cloud
- Hybrid Integration— aligned with Ark // Nexus for unified compute + data fabric
Infrastructure at a Glance
- GPU Memory— hundreds of gigabytes of unified GPU memory across multiple RTX PRO 6000 Blackwell workstations
- System Memory— terabytes of ECC system memory
- Storage— petascale erasure-coded distributed storage
- Compute Fabric— 200GbE linking inference, orchestration, and storage tiers
- Spine Switch— 400G with sub-microsecond switching latency
Owned infrastructure. Full operational control. Complete data sovereignty.
Ark // Nexus
The decentralized data plane bridging cloud object stores with PureTensor's Blackwell-class reference lab and edge inference. It enforces scheduled replication windows, versioned datasets, and low-latency inference paths for sensitive workloads — the backbone of PureTensor's distributed intelligence.
- Cloud object store alignment (S3/Blob/GCS).
- Scheduled replication windows, versioned datasets.
- Low-latency inference paths for sensitive workloads.
Deployment
- Elastic Cloud— scale-out training, managed MLOps, global delivery.
- Hybrid On-Prem— Blackwell inference on sensitive paths, cloud for scale.
- Edge Immediate— distilled, quantized models where milliseconds matter.
Security & Governance
Security is not an afterthought but a design principle. Per-developer isolation, lineage-tracked datasets, and strict minimization of data movement define PureTensor's operational standards.
- Per-dev isolation (fixed VRAM slices) and least-privilege by default.
- Dataset versioning, lineage, and retention policies.
- Minimize data movement: sensitive loops local; derived artifacts promoted to cloud.
The Team
H. Helgas — Founder & CTO
H. Helgas is the founder and CTO of PureTensor, and the principal architect of Tensor // Core, the flagship Blackwell-class GPU system anchoring PureTensor's compute fleet, engineered for next-generation training, inference, and HPC workloads at scale. He also designed Ark // Nexus, the decentralized data plane that unifies compute, storage, and recovery into a single operational fabric. With a background in distributed kernel engineering and CUDA-level performance tuning, his work focuses on low-latency architectures where bare-metal efficiency converges with cloud-native elasticity. His background spans finance and technology, including algorithmic trading systems where latency discipline and deterministic execution were non-negotiable — principles now embedded in PureTensor's infrastructure design.
Our Development Team
PureTensor operates with a distributed team model, drawing on engineering talent across time zones. Our focus: MLOps, distributed systems, and GPU-accelerated workloads. We scale capacity to match engagement requirements — lean on infrastructure, rigorous on delivery.
Contact
Send a short problem statement. If there's fit, we schedule a technical call.
Or email us directly: ops@puretensor.ai