An LNM (Large Numerical Model) is to structured numerical data what an LLM is to unstructured text — a model built to be highly accurate and non-hallucinating on business data.
A Large Numerical Model is trained directly on structured, numerical business data rather than on text. Where a large language model learns the statistical patterns of language, an LNM is precision-indexed against the patterns in your ERP, transaction, or process data, and is built to be light enough to run on standard CPUs instead of GPU or TPU clusters.
Structured business data — rows of orders, claims, transactions, sensor readings — has a different shape than language: it's tabular, feature-based, and the "right answer" is often a specific number or class rather than a plausible sentence. Sriya.AI built LNMs specifically for this shape of data and for prediction/optimization tasks, rather than adapting a language model to a job it wasn't built for.
| Dimension | Generic LLM | Sriya.AI LNM |
|---|---|---|
| Built for | Language — process & generate text | Numbers — structured, business-critical data |
| Scale of use | Personal — enhances individual creativity | Enterprise — runs core operating decisions |
| ROI | Uncertain — qualitative, hard to quantify | Predictable — quantified improvement in outcomes |
| Failure mode | Probability — can hallucinate with confidence | Certainty — precision-indexed, does not hallucinate |
| Fit | General-purpose | Industry-specific — tuned to your process data |
| Compute | Costly — requires GPUs | Efficient — runs on standard CPUs |
Sriya.AI's proprietary precision-learning indexing algorithms are stated to deliver 20–40% accuracy improvement with 100% precision over standard machine learning approaches. (Internal benchmark claim — verify before external publication.)
LNMs are applied to two kinds of problems: prediction — estimating a number or probability, like a conversion likelihood or a readmission risk — and optimization — recommending the action that improves a business metric, such as an inventory reorder point or a pricing adjustment.
Sriya.AI states that as few as 150+ records can support 97%+ accuracy on small datasets, while datasets of 51 million records and 100+ features are reported to hold around 98% accuracy. This is intended to make LNMs usable by SMEs with modest data volumes as well as larger enterprises with high-volume, high-dimensional data. (Company-reported figures — not independently verified.)
A generic LLM is built for language, at a personal, general-purpose scale, with hard-to-quantify ROI, and can hallucinate with confidence. An LNM is built for numbers, runs core enterprise decisions on structured, industry-specific data, targets predictable, quantified ROI, and is precision-indexed to avoid hallucination on standard CPUs rather than costly GPUs.
No. Sriya.AI's LNMs are designed to run on standard CPUs rather than requiring GPU or TPU infrastructure.
An LLNM pairs front-end and back-end LLM agents with an LNM, so a conversational interface can drive the LNM's numerical prediction and optimization. See Large Language Numerical Models.