AI infrastructure
An evaluation harness that catches the model out
Shipped for An AI reasoning-infrastructure company · led by Parik Ahlawat
01
The problem
Teams could not tell, objectively, which model reasoned and called tools best, or when one was hallucinating.
02
The constraint
Teams needed an objective read on which model reasoned and called tools best, and when one was hallucinating, not a subjective impression.
03
The system
A unified evaluation framework across three public benchmarks (function calling, multi-step reasoning, dynamic real-world tasks), a neurosymbolic reasoning pipeline, and a four-layer verifier catching hallucinations, logical inconsistencies, and invalid tool use.
FIG. 01 · SYSTEM FLOW · 5 NODES · 4 FLOWS
04
The outcome
Data-driven model selection with measurably less hallucinated reasoning and higher tool-call accuracy, with three public harnesses run in parallel.