This paper documents the methodological contradiction between researching the autonomy of older adults and using generic language models that inherit clinical surveillance biases pre-existing in the academic corpus. To address this friction, we present sustrato.ai, an Al-assisted systematic review platform that encodes interpretive traceability through a structured three-iteration dialogue between a human researcher and Al, backed by an immutable append-only registry with SHA-256 cryptographic verification. The platform was deployed in an empirical pilot that processed 257 scientific articles on artificial intelligence and older adults, identifying a primary subcorpus of 192 documents. Structured extraction reveals that 62.5% of these articles restrict their evaluation to clinical or technical performance metrics, while only 9 studies address leisure granting protagonist agency to the older adult. Simultaneously, the project demonstrates that the barrier to conducting this level of scrutiny is not budgetary: against a literature where 77.6% of funding is endogenous academic patronage, the total automated API processing cost was less than one US dollar. The convergence of these empirical and architectural results allows the following interpretation: the infrastructure used to review the literature conditions what becomes visible within it. Sustrato.ai proposes a reproducible model for methodologically documenting the friction between human judgment and algorithmic classification.