From First Principles to Products

The Challenge

Quantum detectors are limited by efficiency and signal-to-noise, and calibration is often manual and slow.

The Challenge

Single-photon detection needs high fidelity and repeated manual calibration.

The Challenge

Injection-induced electromagnetic side channels can exfiltrate secrets while looking like ordinary RF, which energy detectors cannot separate out.

The Challenge

Every organisation is being told to migrate to post-quantum cryptography, but most cannot say how much protection they hold today, or what to fix first. Severity lists show what looks bad, not how much margin remains.

The Challenge

You cannot protect cryptography you cannot see, and most organisations cannot see all of theirs. It hides in certificates, configuration, live connections and inherited dependencies.

The Challenge

A migration plan is not a migration. Changing cryptography in a live, regulated environment risks breaking systems that depend on it.

The Challenge

API breaches slip past detectors that rely on a single technique, because one method means one blind spot.

The Challenge

Distributed networks are trusted with real value, but there is no standard way to score how they hold up under attack or validator failure.

The Challenge

Signature-based intrusion detection struggles with novel attacks that do not match a known pattern.

The Challenge

Cryptographic security margins are usually asserted from policy, not bounded from first principles.

The Challenge

Organisations increasingly rely on AI models they did not train, with no simple way to prove a model has not been tampered with.

The Challenge

AI agents are being trusted with real actions, but an agent can behave well under test and differently in production, and you cannot tell by asking it.

The Challenge

Almost all AI training relies on empirical heuristics: methods that work in practice but offer no mathematical guarantee about what could fail or when. Loss spikes and collapsing runs remain unsolved for regulated and safety-critical deployments.

The Challenge

Training large models at very low numerical precision is unstable, and precision is a direct driver of training cost.

The Challenge

AI accelerators stall waiting for data; memory and data staging is a persistent bottleneck.

The Challenge

Scaling qubits means managing many signal channels; wiring and demultiplexing is a bottleneck for quantum computers.

The Challenge

Running AI compute at reduced voltage saves energy but raises the risk of correlated faults.

The Challenge

Battery cell performance is hard to predict and optimise, and getting it wrong is costly across electric vehicles and grid storage.

The Challenge

A drone or robot actuator can be commanded to do something irreversible, and there is usually no independent check on the command path itself.