Establish what applies
Resolve the organisation’s regulatory perimeter and freeze the complete set of applicable obligations before looking for evidence.
Most AI systems begin with a question and search for plausible context. Regulated work demands a stronger method: establish the complete obligation set, evaluate every row and preserve what the evidence can—and cannot—support.
Explore the architecture →Resolve the organisation’s regulatory perimeter and freeze the complete set of applicable obligations before looking for evidence.
Match each obligation to organisational evidence, source context and governed institutional memory inside the controlled environment.
Use retrieval and local model reasoning to distinguish supported, missing, contradictory and technically incomplete evidence.
Bind the outcome to its source, evidence and event lineage so reviewers can understand what happened and why.
The architecture treats completeness, provenance, privacy and uncertainty as system responsibilities—not instructions added to a prompt.
Expert judgement is built slowly and lost quickly. Aprentiz is engineered to preserve source-linked context, decisions and learning as governed memory—without confusing stored knowledge with verified truth.
Models, infrastructure and performance boundaries.
Organisation, jurisdiction, rules and task authority.
Provenance-bearing knowledge from documents and work.
Aprentiz is designed so an accountable person can inspect the source, applicable obligation, supporting evidence, outcome state and event history behind consequential work.