Intelligence, Distilled.
We build autonomous AI agents that run in production on NVIDIA Blackwell. Trained and deployed on owned hardware — from cybersecurity and strategic intelligence to voice pipelines and language preservation.
About PureTensor
PureTensor builds autonomous AI agents powered by NVIDIA Blackwell GPUs. Our agents handle cybersecurity, strategic intelligence, voice pipelines, and language preservation, running 24/7 in production on infrastructure we own and operate.
We also help organisations become AI-ready. Whether that means standing up on-premise inference infrastructure, deploying agentic workloads on AWS, Azure, or GCP, or designing hybrid architectures that bridge both, we start with the problem, not the platform.
Ecosystem
Three verticals, one agentic AI platform. Autonomous cybersecurity, open-source agent orchestration, and strategic intelligence — all powered by on-premise Blackwell inference.
What Our Agents Do
Production AI agents running 24/7 on NVIDIA Blackwell, built for workloads where accuracy, speed, and data control matter.
- Cybersecurity · autonomous offensive security testing and threat intelligence.
- Strategic Intelligence · AI-augmented geopolitical, financial, and technology analysis.
- Voice Pipelines · real-time transcription, voice cloning, and speech-to-knowledge extraction.
- Language Preservation · endangered language documentation and revitalisation tools.
How We Build Them
Every agent runs on infrastructure we own. No cloud dependency for sensitive workloads.
- Local Inference · frontier-scale models running 100% GPU-resident on NVIDIA Blackwell.
- Multi-Modal · text, voice, vision, and tool calling in a single agent.
- Multi-Engine · local models, cloud APIs, and CLI agents orchestrated through one framework.
- Human-in-the-Loop · approve, reject, or override any agent decision before it executes.
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 frontier-scale 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 · frontier-scale models running 100% GPU-resident at production speed
- 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
Heimir Helgason · Founder & CTO
Heimir Helgason is the founder and CTO of PureTensor, and the principal architect of TENSOR//CORE, the flagship Blackwell-class GPU system anchoring PureTensor's compute infrastructure, engineered for high-throughput training, inference, and HPC workloads. He also designed ARK//NEXUS, PureTensor's distributed storage and recovery architecture, unifying compute, storage, and disaster recovery across multiple regions. His work spans the full stack: from bare-metal infrastructure and high-bandwidth fabrics to agentic AI systems, multi-model orchestration, and autonomous workflow design. His technical focus is building sovereign AI platforms where every layer, from silicon to software, is owned, not rented. His background spans finance and technology, with experience in environments 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. Every system we build runs on hardware we own and operate, from 200GbE compute fabrics to petabyte-scale Ceph storage clusters. Our engineers work across the full stack: bare-metal provisioning, container orchestration, model serving, and the agentic software layers that tie it all together. We do not outsource core infrastructure decisions. If it touches production, our team built it, tested it, and monitors it.
Contact
Send a short problem statement. If there's fit, we schedule a technical call.
Or email us directly: ops@puretensor.ai