D8DataM8
Defence Model Readiness

Put operational judgement behind every model decision.

DataM8 helps Defence, sovereign industry and research teams test, improve and compare new AI models through authorised scenarios, calibrated practitioners and traceable evidence.

Scope a controlled pilot Explore Defence expertise

READINESS EVIDENCE

A repeatable range for changing models

  • Scenario-level decision and communication scores
  • Critical-failure and unsafe-authority classifications
  • Model-version regression comparisons
  • Expert disagreement and adjudication records
  • Evidence-backed trial and release recommendations

THE READINESS RANGE

From practitioner judgement to model evidence

Conversational realism is not operational readiness. The range makes the intended behaviour, permitted evidence and human authority explicit, then preserves the resulting cases for every future model.

  1. 01

    Define

    Set the authorised scenario, user, decision and failure consequences.

  2. 02

    Capture

    Translate practitioner judgement into observable criteria and evidence rules.

  3. 03

    Calibrate

    Align qualified reviewers on shared cases before evaluating production runs.

  4. 04

    Exercise

    Run blinded model and human interactions in a controlled environment.

  5. 05

    Adjudicate

    Resolve material disagreement and record the reason behind each decision.

  6. 06

    Improve

    Turn verified failures into approved corrections and held-out test cases.

  7. 07

    Regress

    Rerun the suite whenever the model, prompt, retrieval or tools change.

SPECIALIST CAPABILITIES

Built for operational evaluation—not generic labelling

AI role-player fidelity evaluation

Evaluate whether AI role players maintain realistic intent, communication, tempo and decision patterns across authorised synthetic training scenarios without claiming operational equivalence from conversational realism alone.

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Command-and-control decision evaluation

Test how AI systems prioritise information, preserve commander intent, communicate uncertainty and recommend escalation in synthetic command-and-control scenarios designed with authorised practitioners.

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Intelligence analysis evaluation

Evaluate whether AI-supported analysis distinguishes observations from inference, weighs competing explanations and communicates confidence without fabricating sources or collapsing uncertainty into an unjustified conclusion.

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Simulation scenario and red-team design

Design synthetic exercises that vary ambiguity, tempo, sensor reliability and coordination demands so model weaknesses can be observed, reproduced and corrected before any higher-trust trial.

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Human-AI teaming evaluation

Evaluate workload, trust calibration, intervention timing and decision outcomes when people work with AI assistance, including whether operators can understand, challenge and safely override model recommendations.

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Mission-system regression testing

Rerun controlled scenario suites after model, prompt, retrieval or tool changes to identify new failures in reasoning, communication, evidence use, latency and safe escalation before a release decision.

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Security boundary first

Public demonstrations use wholly synthetic, fictional and unclassified scenarios. Customer work is scoped to the approved environment, handling rules and participant authority before any material is collected.

  • No request for classified knowledge
  • No implied Defence endorsement or operational access
  • Data minimisation and project-specific access
  • Human authority, stop conditions and escalation
  • No readiness claim from realism scores alone

Start with an unclassified pilot

Select one operator role, one bounded synthetic scenario and two model versions. DataM8 turns the exercise into a reusable evidence contract, calibrated review and regression report.

Create a pilot workspace