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Accuracy Benchmarks, by Industry

Case-study benchmarks comparing a generic LLM (ChatGPT / GPT-4o) against tuned classical machine learning (XGBoost / Random Forest) — before Sriya.AI's own LNM even enters the picture.

How a generic LLM stacks up against classical ML

These figures compare two non-Sriya baselines; Sriya.AI's own LNM figures for these exact use cases are not yet published on this page.

ChatGPT (GPT-4o)
XGBoost / Random Forest

Benchmark source & methodology: Internal case-study benchmarking cited in Sriya.AI materials, not independently or third-party verified. Baselines compared: ChatGPT (GPT-4o) and tuned XGBoost/Random Forest models. Dataset: Varies by use case — not provided in current source material. Metric: Accuracy (%). Sriya.AI result: Not yet published on this page. Company materials state a claimed 20–40% accuracy lift with 100% precision over standard ML on comparable tasks; per-row LNM figures should be added here once independently verified.

Distinguishing the claims: The chart above is a company-reported internal case-study benchmark, not an independently or third-party-verified result, and not a published research study. Sriya.AI's own precision-learning claim of 20–40% accuracy improvement with 100% precision over standard ML is likewise internally reported and flagged for verification before external publication.