What is an LLNM (Large Language Numerical Model)?
An LLNM pairs front-end and back-end LLM agents with an LNM to turn decision trees into measurable business outcomes, combining language-based interaction with numerical prediction and optimization.
Technology · Large Language Numerical Models
Large Language Numerical Models (LLNMs)
An LLNM pairs front-end and back-end LLM agents with a Large Numerical Model (LNM) to turn decision trees into measurable business outcomes.
What LLNM means
A Large Language Numerical Model combines two layers: a language layer (LLM agents) for conversational interaction, and a numerical layer (an LNM) for prediction and optimization on structured business data. The language layer handles how a person or system asks a question or walks a decision tree; the numerical layer handles the underlying prediction that decision depends on.
How LLM agents and the numerical model relate
Sriya.AI describes LLNMs as pairing front-end and back-end LLM agents with an LNM. In practice, that means a conversational or workflow layer sits in front of (and behind) the numerical engine: it can gather inputs, walk a decision tree, and present the LNM's prediction or optimization result in a usable form, without the numerical core itself being a language model.
Decision-tree interaction
Many business processes are effectively decision trees — qualify a lead, escalate a risk, route a case. LLNMs are designed to sit on top of that structure, using LLM agents to walk the tree conversationally while the LNM supplies the numerical prediction at each decision point.
Numerical prediction, optimization, and AI agents
Underneath the language layer, an LLNM still relies on the same two core capabilities as a standalone LNM: prediction (estimating a number or probability) and optimization (recommending the action that improves an outcome). The AI agents add a layer of interaction and orchestration on top — they don't replace the numerical engine, they operate it.
Business applications
Sriya.AI's LLNM approach underpins its solution areas — see Sales AI, Supply Chain AI, Fintech AI, and Healthcare Predictive Analytics — where a conversational or workflow interface is paired with a numerical prediction underneath.
LLM vs LNM vs LLNM
- LLM — a language model: processes and generates text, general-purpose, GPU-dependent.
- LNM — a numerical model: predicts and optimizes on structured business data, CPU-based, precision-indexed. See Large Numerical Models.
- LLNM — LLM agents paired with an LNM, so language-based interaction drives a numerical decision engine.
Scope note: Beyond the front-end/back-end agent pairing and decision-tree framing described here, Sriya.AI has not published further internal LLNM architecture. This page does not speculate beyond that.
FAQ
Large Language Numerical Models: frequently asked questions
Is an LLNM just an LLM with a database connection?
No. The numerical core is an LNM — a model precision-indexed for structured-data prediction and optimization, not a general-purpose language model retrieving records. The LLM agents sit around that numerical core for interaction.
Where are LLNMs used?
Across Sriya.AI's solution areas — sales, supply chain, fintech, and healthcare — wherever a conversational or workflow interface benefits from being backed by a numerical prediction engine.