The world's first LNM & LLNM

AI that's measured in decimal points, not confidence intervals.

LLMs generate words. Our Large Numerical Models generate numbers. Our Large Language Numerical Models generate results — 95–99% precision on business-critical predictions, with no hallucinations and no GPU cluster required.

LLM → words/ LNM → numbers/ LLNM → decisions
Large Numerical Models SXI++ Engine AI Agents LLNMs Lead 2 Cash Supply Chain Fintech Healthcare
Reference · Generic LLM
Language model
~62%
Fluent, probabilistic, prone to hallucination on structured business data.
VS
Sriya.AI · SXI++
Numerical model
97.4%
Precision-indexed on your structured ERP & process data. Runs on CPU.
Who we are

AI isn't a future advantage — it's a present differentiator. Companies that delay adoption watch competitors pull ahead with sharper customer insight, lower operating cost, and faster decisions. Once those advantages compound, catching up gets exponentially harder.

Sriya.AI is a deep-tech AI company built around Large & Small Language-Numerical Models (LLNMs / SLNMs) — engineered for accuracy and improvement in data-driven industries like finance, supply chain, industrials, and healthcare. Our proprietary SXI++ engine pushes past where general-purpose AI falls short.

LNMs are to structured numerical data what LLMs are to unstructured text — highly accurate, non-hallucinating, and light enough to run on CPUs instead of GPU or TPU clusters. LLNMs pair front- and back-end LLM agents with LNMs to turn decision trees into measurable business outcomes.

Highly curated LLNMs unlock 20–200% improvement in business outcomes from data your ERP is already generating — including for SMEs with small but valuable datasets. Why should scale be a prerequisite for AI value?

95–99%
precision on business-critical predictions
20–200%
improvement range in business outcomes
7
US provisional patents filed on LLNM, LNM & SXI++
0
GPUs required — models run on standard CPUs
150+
records needed for 97%+ accuracy on small datasets
51M
record / 100+ feature datasets holding at 98% accuracy
Choosing the right engine

LNMs and LLMs solve different problems. Don't ask one to do the other's job.

Language models are built to sound right. Numerical models are built to be right. Here's the honest breakdown of where each one belongs.

DimensionGeneric LLMSriya.AI LNM
Built forLanguage — process & generate textNumbers — structured, business-critical data
Scale of usePersonal — enhances individual creativityEnterprise — runs core operating decisions
ROIUncertain — qualitative, hard to quantifyPredictable — quantified improvement in outcomes
Failure modeProbability — can hallucinate with confidenceCertainty — precision-indexed, does not hallucinate
FitGeneral-purposeIndustry-specific — tuned to your process data
ComputeCostly — requires GPUsEfficient — runs on standard CPUs

Sriya.AI's proprietary precision-learning indexing algorithms deliver 20–40% accuracy improvement with 100% precision over standard machine learning approaches.

Verticals & solutions

Deployed where the data is structured and the stakes are real.

Lead 2 Cash

  • Propensity-to-buy scoring (DXI)
  • Funnel & conversion optimization
  • Pricing & promotion efficiency

Supply chain

  • Backorder reduction
  • Inventory optimization
  • Logistics optimization

Fintech

  • Fraud detection
  • Payments & default risk
  • Loan & repayment analysis

Healthcare

  • Unplanned readmission risk (HURRA)
  • Sepsis onset detection (HOSRA)
Prediction accuracy, by industry

How a generic LLM stacks up against classical ML — before Sriya's LNM even enters the picture.

Independent case-study benchmarks across industries, comparing ChatGPT (GPT-4o) against tuned XGBoost / Random Forest models.

ChatGPT (GPT-4o)
XGBoost / Random Forest
Source: internal case-study benchmarking cited in Sriya.AI materials. The Sriya.AI LNM column was cropped in the source document this page was generated from — company materials state a 20–40% accuracy lift with 100% precision over standard ML on comparable tasks; plug in your verified per-row LNM figures here once available.
LLNM AI

Start by scoring your data. Then compound its value in three directions.

Benchmark the use-case worthiness of your data with an AI Score, then uncover hidden value across generation, prediction, and improvement.

01 / GENERATE

Fill the gaps in your data

  • Large synthetic datasets for faster rollout, 95%+ accuracy and precision
  • Missing data, missing features, additional features, heterogeneous data mapping, and more
02 / PREDICT

Precision at any scale

  • 97%+ accuracy and precision even on very small datasets (150 records)
  • 98% accuracy and precision on very large datasets (51M records, 100+ features)
03 / IMPROVE

Move the metrics that matter

  • 25% reduction in R&D time and number of experiments (DOE)
  • 25% improvement even on abstract KPIs — employee churn, resource scheduling, recruiting
7
US provisional patents on LLNM, LNM & SXI++ technology
CPU
only — no GPU or TPU infrastructure required
0%
hallucination rate on structured numerical prediction
20–200%
improvement range across deployed use cases
In their words
"
As a medical advisor for Sriya.ai, I am continually impressed by its potential to transform patient care. Sriya.ai precisely predicts which patients are at the highest risk of hospital readmission within 30 days and identifies the specific vulnerabilities contributing to that risk — empowering healthcare providers to implement proactive, targeted interventions.
Caesar Gonzales — Medical Advisor, Sriya.AI

Bring 95–99% precision to your next business-critical decision.

Talk to Sriya.AI →