Independent AI systems laboratoryEngland / 2026

Intelligence,armoured.

We develop software and formal methods for invention analysis, inference selection and verifiable AI governance.

BAYES' RULEp(θ | D) = p(D | θ)p(θ) / p(D)
The Scorpion Labs mark
INVENTION · INFERENCE · PROOF

AI capability is becoming cheaper.

Reliable selection, governance and verification are not.

Four research programmes.

Product work spanning invention analysis, model selection, verifiable governance and agent orchestration.

01
Patent StudioToward private beta

From invention record to patent work product.

A workspace for invention records, prior-art research, claim development, drawings and counsel review. Outputs retain their source evidence and review history.

Prior art · claims · evidence
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02
OpenScorpionActive research

Selecting models under real constraints.

Research into model selection across capability, cost, latency, policy, provenance and uncertainty.

Decision theory · Bayesian inference · optimisation
Discuss the research
03
APCGActive development

Verifiable evidence for governed AI actions.

Research into machine-checkable evidence for governed AI actions and independent verification.

Formal reasoning · verification · provenance
Explore the thesis
04
AriadneActive research

Traceable agentic work.

Research into traceable agent workflows, explicit human approval points and reproducible outcomes.

Orchestration · evidence · human review
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Decisions under uncertainty.

Optimisation, Bayesian updating, information theory and graphs give uncertain decisions a form that can be inspected.

α / 01

Choice under constraint

x* = arg max f(x) s.t. gⱼ(x) ≤ 0

Optimisation under policy, risk, budget, capability and latency constraints.

β / 02

Belief after evidence

p(θ | D) ∝ p(D | θ) · p(θ)

Bayesian updating makes the relationship between prior belief, observed evidence and revised belief explicit.

γ / 03

The value of uncertainty

H(X) = − Σ p(x) log p(x)

Entropy quantifies uncertainty and helps estimate the value of further evidence.

δ / 04

Reasoning has shape

G = (V, E)

Graphs represent claims, evidence, support and conflict without collapsing the reasoning into a single score.

ε / 05

Commitment before revelation

c = H(m)

Hash commitments support later verification without publishing the underlying material at commitment time.

ζ / 06

No single optimum

x ∈ Pareto(F)

Pareto frontiers expose trade-offs between quality, cost, speed, control and trust.

Relevant scale.

Published indicators for patents, generative AI spending and the projected AI market.

What we are working on.

Research questions connecting mathematical ideas to practical systems.

I

Invention intelligence

How can software help an inventor search an idea space while preserving the record of human contribution?

II

Inference economies

How should systems select models when capability, price, latency, policy and provenance conflict?

III

Proof-carrying governance

What evidence must accompany an AI action for an independent party to verify its governance?

IV

Navigable autonomy

How can long-running agent workflows remain traceable, reviewable and interruptible?

Discuss a research problem.

We welcome conversations about invention systems, governed AI infrastructure and applied research.

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