Intelligence that never leaves your control
Argus turns open sources into verified written intelligence on the companies and events you track, produced end to end by autonomous agents. Built by a research lab that owns and operates its NVIDIA Blackwell fleet, and publishes what it measures.
NVIDIA Blackwell · HQ Mountain View, CA · Compute in the UK · DR in Iceland
Argus: verified written intelligence from open sources
Argus reads funding announcements, filings, hiring signals, and public reporting continuously, then writes assessed intelligence briefs on the companies and events you track. It is built for organisations whose questions cannot go to a third-party cloud: every stage of collection, assessment, and writing runs inside a boundary we control. In production, delivering briefs to clients in the AI infrastructure sector since June 2026.
Assessed, Not Aggregated
A multi-model assessment council scores every document in parallel for novelty, impact, and analytical depth. Only verified material reaches a brief.
Entity & Relationship Mapping
Named entity recognition feeds a live knowledge graph. Companies, people, and events resolve into networks rather than headlines.
Full Provenance
Every claim in a brief traces to its sources. Collection, enrichment, and publication run as one auditable pipeline.
Built and run on our own stack
Argus runs on infrastructure PureTensor owns: NVIDIA Blackwell compute, 200G RDMA fabric, petascale distributed storage, and Kubernetes orchestration, operated as one system with no third-party cloud in the path. Sentinel, our autonomic operations layer, keeps it alive: continuous triage across every signal, remediation with graded outcomes, and a versioned memory that turns each fix into a permanent immunity. We run our own company on this stack every day.
Compute & Inference
Current-generation NVIDIA Blackwell inference and training on AMD Zen 5 platforms with terabytes of DDR5 system memory.
Platform & Storage
Highly available erasure-coded storage pools with dedicated storage fabric. Kubernetes orchestration across the full stack.
Compute operated in the United Kingdom. Disaster recovery in Iceland. No third-party cloud in the path.
From the Lab
Everything below was measured on the fleet described above, including the failures. We publish what we run.
The Flat Line: Why a Single Throughput Number Cannot Tell Batching From Queueing
A production inference engine reported the same aggregate throughput at one concurrent request as at sixty-four. It was not batching at all: every request past the first was queueing, and the single benchmark point we had been quoting could not see the difference. Sweeping the concurrency ladder corrected a stored throughput figure by 3.2× and moved a model comparison from a reported ~7× to 17.0×, or 33.9× per GPU, since NVIDIA's Nemotron 3.5 Lightning sustained 5,613 tokens per second across 64 concurrent requests on a single Blackwell card, at sub-three-second p95, and had not saturated when our ladder ran out. We give the diagnostic signature (flat aggregate, linearly climbing p95), the four-configuration sweep that exposed it, a quality result that survived repetition and one that did not, and why a recorded performance number belongs to a configuration rather than to a model.
Make It Go Red: The False-Green Failure Class in Production Monitoring
For five months a nightly backup reported Complete over a zero-byte archive. An alert rule had been structurally incapable of firing for its entire life. A dashboard rendered missing telemetry as health, and 37 of 49 deployment scripts could silently ship stale monitoring state. We name the failure class, the false green, map its mechanisms in four families from a 24-day production audit, show that verification tooling itself reproduces the class it hunts, and describe the falsification harness now grading all 279 of our alerting controls continuously: registry, structural liveness, mutation-proven firing tests, and end-to-end canaries. Monitoring correctness is a reachability property, not a syntax property.
The Day an Agent Ate the Workstation: OOM Forensics of a 178 GiB CLI Memory Leak
An agentic coding CLI ballooned to 178.5 GiB of resident memory during a routine fleet upgrade and froze a production workstation. The kernel killed fourteen innocent processes before touching the culprit. A forensic walk through the OOM killer's victim ordering, why swapless machines turn leaks into freezes, the four-layer guard we deployed the same afternoon, and the upstream report we filed instead of posting screenshots.
One lab, three programmes
Everything we build serves one thesis: intelligence your organisation can own outright. The work is organised into three research programmes.
Sovereign Intelligence
Intelligence systems for organisations whose questions cannot leave a boundary they control. Argus, our flagship, lives here.
Explore the programmeVoice & Language
Speech and language systems for the accents, dialects, and languages the large labs skip, running on-device or on-premises.
Explore the programmeAutonomous Infrastructure
The operating layer that runs our fleet: autonomic triage, remediation, and memory, published as research as it hardens.
Explore the programmeBuilt From First Principles
What happens when you stop renting intelligence and start building it? We design and operate our own AI infrastructure, from the network fabric to the inference stack, because serious research requires systems you understand completely. Not abstractions on top of abstractions, but hardware you can touch, models you can inspect, and pipelines you control end to end.
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 HelgasonLinkedIn ↗
Founder & Chief Architect
Designed and built PureTensor's AI platform from bare metal. 200G 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. 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.
We are growing. Reach out.
Get in Touch
Interested in collaborating, investing, or just talking about AI infrastructure? We'd like to hear from you.
Mountain View, California · London, United Kingdom
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