By taking frontier models where their own builders never conceived — pointed at problems that were never supposed to yield, in genomic medicine, defence, and beyond. The model is the instrument. The edge is the mind working it.
Ask why a problem has stayed unsolved for thirty years. It is rarely because nobody tried — it is because everyone who tried was trained the same way. Same textbooks, same methods, same instincts about what cannot be done. Expertise is how a field advances, and also how it gets stuck: the people closest to a problem are the least free to see it differently. Prisoners of their own training.
Frontier models are not bound by any one field's frame. Used politely — as autocomplete for the status quo — they change nothing. We use them at their edge, in configurations their own builders never conceived, against problems that were never supposed to yield. Working a model at that edge is a human craft — a mind in tight iteration with the machine, testing what it can be made to do and what it will only pretend to do. Experts inherit boundary conditions with their training. The pairing of an unbounded mind and a frontier model inherits none. That pairing is a rare capability.
And we say what every clinician and every commander already knows: things go wrong. That is how they eventually go right. So we build to see it early, measure it honestly, and keep going until it works — stable, useful, and life-changing.
We are deliberately narrow about how we work and deliberately broad about where we apply it. Every practice below is held to the same bar: results you can use — practically applicable, consistently delivered, and built to hold in an increasingly hostile world.
We have solved the last mile. Genomics has been ready for the clinic for a decade — what was missing was the delivery: getting a sequenced genome into a decision a clinician can act on, in the time they have, with the provenance to stand behind it. Our system closes that gap. Genomic medicine finally reaches the bedside, and medicine changes for good.
Decision advantage in contested, degraded and actively hostile environments. Multi-sensor fusion, mission autonomy and command tooling engineered to hold their integrity precisely when everything around them is designed to break it.
The problems that don't come with a playbook. We take frontier models into scientific and industrial domains where off-the-shelf tooling quietly stops working. Anyone can get an AI demo to work once — we build the test rigs that prove it works every time, before you bet on it.
Agents that do the work, not just describe it — planning over days rather than sentences, and actually using your tools. Before software acts in your name, you need to see what it is doing and be able to stop it. We build agents we would let loose on our own business, and nothing we couldn't show our working on.
Studio-grade creative at machine scale. Generative pipelines fused with live-action craft, producing campaign work for brands and agencies that moves at the speed of AI without ever looking like it was made by one.
Consumer software held to the same engineering standard as everything else we build. Our current focus is financial education for women — closing a confidence and participation gap the industry has been content to leave open, with products designed to teach rather than to extract.
The next attack surface is already forming: agents holding credentials, acting at machine speed, against systems designed for human users. We build identity for non-human actors — IDs, keys and least-privilege for agents — and defences for the day the attacker is an agent too. It is the standard we hold our own systems to before we sell it to anyone.
Your agents will hallucinate. Not might — will: any calibrated model must sometimes assert what it cannot know, and that is a theorem, not a pessimist's opinion. Every promise of zero hallucination is a claim mathematics forbids. The honest questions are the ones an underwriter would ask: how often, where, and at what cost.
Anchor exists to answer them — error contained where it matters most, measured everywhere else. Two layers, because each covers for the other.
On the paths where a wrong answer is unacceptable, the agent is not permitted to generate freely. It may only assemble output from attested sources, and a machine-checked postcondition rejects any token that doesn't trace to a citation. Fabrication isn't detected and filtered — it's never generable.
“The trial met its primary endpoint at 24 weeks.”
“Mortality was reduced by roughly 40% across all cohorts.”
Guarantee — zero unsourced claims. Cost — coverage: it refuses more than it answers. Affordable only where a refusal beats a guess.
Layer 01 is too expensive to put on all your traffic. So the rest is measured rather than gated: known-answer probes run continuously against your live agents, from the outside, the way a user would reach them — on contracted, auditable networks in the regions your users are actually in. No coverage cost, because nothing is blocked.
Guarantee — a regression of 1.6 points is detected, with the sample size published. Cost — it tells you the fleet moved, never which answer was wrong.
Both layers ship with their numbers — coverage, false-positive rate, the smallest regression detectable at your probe budget — and every measurement comes from a vantage point you can name in a security review. In a regulated room, a number you can check beats a promise you can't.
See your own numbers We'll measure your agents and show you exactly what we find. Request a measurementThree commitments that decide what we take on, how we staff it, and what we are willing to ship.
You do not prompt your way to reliability. It is designed in at the architecture level, enforced by verification, and proven with evidence a sceptic would accept.
Our teams read the papers, the protocols and the doctrine — not to inherit a field's assumptions, but to know precisely which ones to break. You cannot free a problem from its frame without knowing the frame cold.
Degraded inputs, hostile actors and the long tail of edge cases are the operating environment, not the exception. We design for the bad day, because the bad day is the point — and increasingly the bad day arrives at machine speed, driven by an adversary that is itself an agent.
We work with a small number of partners at a time, across research, industry and government. If the cost of being wrong is what keeps you up, we should talk.