Numerical AI is Sriya.AI's term for AI systems built primarily to work with structured, numerical business data — ERP records, transactions, sensor and process data — rather than natural language.
Most well-known AI systems today are language models: they read and generate text. Numerical AI is a different category. It is built around structured numerical business data — rows and columns in an ERP system, transaction logs, sensor readings, claims records — rather than sentences and paragraphs.
Sriya.AI's Numerical AI systems are built for two jobs: numerical prediction (estimating a number or a probability, like the chance a lead converts or a patient is readmitted) and optimization (finding the action — a price, a reorder quantity, a staffing level — that moves a business outcome in the right direction).
A general-purpose language model is trained to sound plausible across almost any topic. It can be a poor fit for business-critical numerical decisions because it was never optimized to be precise on structured data, and it can produce confident-sounding but wrong answers — a hallucination. Sriya.AI's Numerical AI models are precision-indexed directly against your structured business data, aiming for measurable accuracy rather than fluency, and are light enough to run on standard CPUs instead of GPU or TPU clusters.
Sriya.AI's models are designed to work from the structured data companies' ERP and process systems are already generating — including SMEs with small but valuable datasets, not only organizations with massive data volumes.
Numerical AI is the category; Large Numerical Models (LNMs) and Large Language Numerical Models (LLNMs) are the two model types Sriya.AI builds within it. An LNM is a pure numerical prediction/optimization engine. An LLNM pairs front- and back-end LLM agents with an LNM so a conversational interface can drive the underlying numerical decision engine.
Numerical AI is Sriya.AI's umbrella term for its numerical-data-focused model family (LNMs and LLNMs). It is applied to the same broad class of problems — prediction and optimization on structured data — that classical machine learning addresses, using Sriya.AI's own precision-indexed approach.
No. Language models and Numerical AI solve different problems. Sriya.AI's LLNMs actually combine the two: an LLM agent for interaction, an LNM for the underlying numerical decision.
Structured business data your systems already produce — ERP, transaction, sales, or process records. Sriya.AI states 150+ records can support 97%+ accuracy on small datasets, scaling to 98% accuracy on datasets of 51 million records and 100+ features.