A structured methodology that teaches and applies the discipline of producing AI-augmented research that is valid, defensible, and authoritative within a specific domain. Built on a three-component core framework and operationalised through a scored instrument set.
Identifying the hierarchy of validating institutions, standards bodies, regulatory frameworks, and cultural arbiters that determine whether a finding is credible in a given domain.
Encoding the domain-specific patterns, speech acts, temporal contexts, cultural variables, and structural signals that trained practitioners recognise and that AI systems miss without explicit instruction.
Building a structured, repeatable sequence of checks that every AI-generated output must pass before it is considered credible, citable, or actionable.
DPI isn't one workshop product - it's the same three-component discipline instantiated four ways, depending on whether you want to learn it, buy it pre-calibrated, have it applied on your behalf, or embed it as a standing layer over your own AI use.
The three-component framework and its supporting instruments, taught domain-agnostically.
Pre-calibrated domain instantiations - fixed authority maps, pattern libraries, and instrument weights for a named vertical, or bespoke-calibrated to your internal governance standard.
The SENTIUS Research Suite viewed through the DPI lens - DPI as the internal validation discipline behind a delivered analytical product, rather than a separately sold methodology.
An embedded validation and compliance layer over your own AI pipeline, mapped against named external standards. Jump to DPI Assure.
DPI doesn't ask a model to hold an entire methodology in its attention at once. Each analytical stage is a discrete, reviewable gate - the output of one gate constrains what the next gate is permitted to assert.
Scores the gap between what's claimed or reported and what the observable record actually shows.
Tests a declared or recorded identity against observable characterisation across six standard dimensions.
Scores how open or closed an actor's formation environment is to correction under contradicting evidence.
Tests whether a stated justification's cause-and-effect sequence is upright, indeterminate, or inverted.
Aggregates independent evidential streams into a confidence level - repetition from one source chain doesn't count, regardless of volume.
Detects when multiple instruments simultaneously produce adverse findings and escalates the evidentiary bar accordingly.
Scores the gap between an actor's declared authority and its actual, observable operational capacity.
Catches mismatched or drifting terminology before it's allowed to quietly shift what's being analysed mid-session.
Tests whether a source is genuinely independent or only presents that way - the specific gap a coordinated content campaign is built to exploit.
Tests claimed institutional incapacity or coordination against an actor's demonstrated behaviour elsewhere, rather than assuming either by default.