Full Lifecycle, Cross-Industry Vectorized Quality Management Model and Traceability System.
Full Lifecycle, Cross-Industry Vectorized Quality Management Model and Traceability System.
Unlike other AI tools, AIQMS™ does not hallucinate because it is not a generative or probabilistic model, it is NOT a bot nor AI agent—it is a deterministic, compliance-driven system architecture powered by four interdependent AI engines: SHIVA (Systematized Hierarchical Inference and Validation Architecture), TARA (Traceability Alignment and Regression Analysis), ANIA (Analytical Narrative for Insight and Alerts), and RIMA (Risk-Informed Model Adjustment). Unlike large language models such as ChatGPT or Grok, which predict outputs based on statistical token distributions across unstructured corpora, AIQMS relies on structured, schema-constrained input derived from authenticated project documents.
Each output in AIQMS whether a compliance alert, traceability matrix, or status report is grounded in pre-ingested, validated data structures. No output is permitted unless fully anchored in verifiable records, eliminating the statistical inference that causes hallucinations in conventional LLMs. This ensures AIQMS outputs are deterministic, audit-ready, and regulation-compliant.