Structured Data Extraction With AI That “Can’t Hallucinate”
Author(s): Umair Ali Khan, Ph.D.
Originally published on Towards AI.
How AI decision models offer a fast and cost-effective approach to turning unstructured text into decisions
Most of the organizational data is unstructured, such as incident reports, support tickets, maintenance logs, call-center transcripts, and customer reviews.
The article explains why large language models (LLMs) are often a poor fit for “structured extraction” when the downstream need is a single bounded decision (e.g., classification, routing, scoring, or filtering). It argues that LLMs are heavyweight for these tasks and can suffer from higher latency, higher cost, and unreliable uncertainty estimates. As an alternative, it introduces TypeSafe AI’s decision model (Jev) and describes how decision models are designed to return typed answers from predefined option spaces with calibrated probabilities. The piece outlines Jev’s core components (state and questions) and its supported output types (Choice, Score, and Noul), then walks through a practical manufacturing incident example showing how structured probabilistic outputs can drive automation with human review for uncertain cases. Finally, it clarifies where decision models fit in an AI stack—useful for bounded judgments, escalation/guardrails, and large-scale processing—while noting their limitations (no arbitrary value extraction, and reliance on text-only state at the time) and emphasizing the value of specialization alongside LLMs rather than replacement.
Read the full blog for free on Medium.
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