PureTensor · Intelligence, Distilled

Might makes Wright.Introducing pureWRIGHT, the complete agentic system for the enterprise.

pureBOT does the work, pureMIND remembers everything, and pureMEND repairs what breaks. Hand it research, reporting and operations, inside your boundary, under approval gates you set. We run our own company on it.

Inside your boundary · Approval gates you set · A record you own

The Film · 3 min 23 s

Three parts, one job each: pureBOT does the work, pureMIND remembers everything, and pureMEND repairs what breaks. Every frame was generated by code and rendered on our own GPUs; the figures on screen come from each part's own records, dated where they appear.

Under Control

It asks before it acts, wherever you tell it to

You decide which actions it may take alone, which wait for a named person, and which it may never take. Every action lands in a ledger your auditors can read.

Approval gates

Every class of action is allowed, approved, or denied by a policy you set. Anything in the approve class waits for a named person, with a time limit, and the decision is logged whichever way it goes.

Hold states

When the system cannot cure something, it parks the problem, stops trying, and says so. It does not retry its way into a worse state, and it looks again only when the hold expires.

Attributable actions

Every action is written to the record with what prompted it, what was done, and what was verified afterwards. An auditor reads the same ledger the system does.

How it is deployed and governed
The Proving Ground

Proven on a fleet we own and run

The system is proven on infrastructure PureTensor owns: NVIDIA Blackwell compute, a 400G RDMA fabric, petascale distributed storage, and Kubernetes orchestration. pureMEND keeps it running, and the lab's research is measured on it. The fleet is where the system is proven; it is not for rent. Where a part calls a hosted model, its record names the model.

Compute

Current-generation NVIDIA Blackwell on AMD Zen 5 platforms with terabytes of DDR5 system memory.

NVIDIA Blackwell compute
  • GPU node
  • GPU node
  • GPU node
Fabric coreRDMA switching400G RDMA fabric400G spine
Petascale Ceph storage
Petascale Ceph storage
  • Ceph pool
  • Ceph pool
  • Ceph pool
  • Ceph pool

Storage & Orchestration

Highly available erasure-coded storage pools with a dedicated storage fabric, and Kubernetes across the full stack.

AMD Zen 5·NVIDIA Blackwell·NVIDIA Mellanox·400G RDMA·400G Spine·PCIe Gen5 NVMe·Ceph·Kubernetes

Compute operated in the United Kingdom. Disaster recovery in Iceland.

Research & Writing

From the Lab

The newest paper leads. Below it, every programme in the lab feeds one part of pureWRIGHT, with the papers behind each part, measured on our own fleet and published with the failures.

Latest paper · Oct 25, 2026Research

Learning From Its Own Answers: 27 Fine-Tunes of Small Open Models, and Why the Data's Source Mattered More Than the Adapter

We fine-tuned small open models 27 times on one task, GSM8K mathematics, to see which choices move a model. Supervised fine-tuning on GSM8K's reference solutions lowered Qwen2.5-0.5B-Instruct's accuracy from 46.9% to between 22.2% and 39.5% under every adapter setting we tried, from rank 2 to rank 64, DoRA, target modules, learning rate and full fine-tuning. It also cut the model's compliance with an unrelated answer-format instruction from 70.2% to as little as 3.1%. Training on the model's own correct samples was the only method that lost no task accuracy, and it cost no knowledge or compliance, but it gained nothing significant either. A short GRPO run collapsed onto its format reward before recovering. Three measurement problems, a generation cap, a first-letter parser and evaluation nondeterminism, would each have changed a conclusion.

Read
Sep 26, 2026pureBOT

Click Accuracy Picks the Wrong Browser Agent: Choosing a Self-Hosted Computer-Use Model on 39 Live Tasks

We ran five self-hosted browser-agent setups through 39 live multi-step tasks on public websites, each in a fresh headless Chrome, scored only by verified end states with no model judge. A DOM agent on DeepSeek-V4.1-Flash scored 39/39 but needs four GPUs; Qwen3.8-27B in a lean screenshot loop scored 38/39 at a median 12.7 seconds per task on a model we already serve, and the same weights inside a heavier harness were three times slower. Holo3.1-35B-A3B hit 93.1% of targets on a single-click test yet finished last on live tasks at 33/39, so click accuracy alone would have picked the wrong model. The top four setups sit within two tasks of each other and are not separable, every screenshot-only agent failed the one autocomplete form, and a headless viewport that is 713 pixels tall instead of 800 cost one model six tasks.

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Aug 27, 2026pureMIND

A Memory Layer With External Anchors: LongMemEval and LoCoMo on a Sovereign Retrieval Stack

Until this week our agent-memory substrate had no score on any public long-term-memory benchmark, so nothing it did could be compared with anything else. We ingested LongMemEval-S (100,548 chunks) and LoCoMo (1,540 questions) and report every configuration on all three axes that matter, accuracy, context tokens, and retrieval latency, with the reader model named. Two zero-cost changes, ordering retrieved chunks by session date and telling the reader that later excerpts supersede earlier ones, lifted a fully on-premises 27B configuration from 67.4% to 72.8%. With a gpt-5.4 reader and the official judge, a top-25 window reaches 90.6%, edging past Zep's published 90.2% at 2.7 times their context budget; at their budget we sit 2.4 points behind. On LoCoMo a distilled fact layer helps as a supplement (+3.7 points) and hurts on its own (−4.9), which is the structural risk in extraction-only memory.

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Jun 19, 2026pureMEND

An Immune System for a GPU Cluster: Building a Reflex Arc for Autonomous Self-Healing

We set out to design a self-healing v2 and found the opposite of a greenfield: four generations of self-healing stacked in sediment, maximum architectural sophistication, near-zero closed-loop coverage. The fix was not a fifth system: it was naming the missing shape, and then discovering the real blocker was signal quality, not architecture.

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About

Built from first principles

We built pureWRIGHT to run our own company on: a system that does the work, remembers everything, and repairs itself, on infrastructure we own and understand completely. Every part is measured before it is claimed, and the failures are published with the fixes. What we deploy for others is what we run ourselves.

What We Believe

Own the stack

From NVIDIA silicon to Ceph storage to Kubernetes orchestration, we operate every layer. No black boxes.

Research in the open

Our findings, benchmarks, and post-mortems are published, including the failures. Science requires scrutiny.

Build what matters

We don’t chase benchmarks. We build systems that solve real problems for real organisations.

Heimir Helgason

Heimir HelgasonLinkedIn ↗

Founder & Chief Architect

Designed and built PureTensor's AI platform from bare metal. 400G RDMA fabric, petascale distributed storage, NVIDIA Blackwell inference. Background in algorithmic trading, cross-border capital markets, and entrepreneurship. Deep expertise in autonomous agent systems, sovereign infrastructure design, and large-scale model deployment.

Ahmed W. Khalil

Ahmed W. KhalilLinkedIn ↗

Strategic Advisor

CFA charterholder with a career spanning top-tier international law, institutional capital allocation, and cross-border deal execution across EMEA. Advises on capital strategy, investor relations, and international market expansion.

Alan B. Apter

Alan B. Apter

Advisory Board Member

Investment banker with over 40 years advising multinationals and corporate boards. Morgan Stanley, Merrill Lynch, Renaissance Capital, Eaglestone Group. Led the first NYSE-listed IPO from post-Soviet Russia. Columbia Law School. Founder of Bretalon Ltd.

We are growing. Reach out.

Contact

Talk to our engineers

Tell us about the work you want to hand over and the boundary it has to stay inside. Research, investment, press, and hiring enquiries are welcome too.

Location

Santa Clara, California · London, United Kingdom

Every message is read by the team.