Technology

Not Just GPT
For Factories

Your manufacturing AI is a tiered system where deterministic quantitative AI does the heavy lifting. The LLM layer exists only to translate—not to decide.

IronTwin AI Data Architecture

The Difference

GPT Wrappers vs. IronTwin AI

Most "AI for manufacturing" products are thin wrappers on generic LLMs. IronTwin is built different.

GPT Wrappers
IronTwin
Architecture
✕LLM-first, prompt engineering
✓4-tier quantitative foundation
Decision Basis
✕Pattern matching on training data
✓Physics-constrained calculations
Hallucination Risk
✕High—LLM generates answers
✓Low—LLM only translates results
Confidence Scoring
✕None or uncalibrated
✓Calibrated uncertainty quantification
Equipment Specificity
✕Generic recommendations
✓Calibrated to your equipment
Auditability
✕Black box
✓Full decision trace

Foundation Layer

Common Data Model Ontology

Your factory's structure encoded as a knowledge graph. Every machine, process, and relationship mapped.

Factory Ontology Diagram
01

Hierarchical Structure

Factory → Lines → Workcells → Machines. Every level connected and queryable.

02

Relationship Mapping

Upstream/downstream dependencies, shared resources, and material flows encoded.

03

Temporal Context

Historical states, shift patterns, and maintenance windows all part of the model.

04

Auto-Discovery

New equipment detected and integrated automatically from data streams.

Intelligence Layer

AI Inference + Uncertainty Quantification

Deterministic AI engines with calibrated confidence. Know what to trust and when to verify.

Physics-Based Models

Not black-box ML. Equations calibrated to your specific manufacturing performance curves.

OEE = f(speed, quality, availability)

Statistical Inference

Bayesian methods that update beliefs with new data. Predictions improve automatically.

P(failure | observations)

Time-Series Forecasting

Production rate predictions, demand forecasting, and trend detection with confidence bands.

Forecast ± 95% CI

Every Output Has a Confidence Score

Not just an answer—you get the probability that the answer is correct. Critical for knowing when human review is needed.

  • ✓Calibrated probabilities (90% confidence means 90% correct)
  • ✓Uncertainty decomposition (data vs. model uncertainty)
  • ✓Automatic escalation when confidence drops below threshold
Recommendation Confidence
Low92%High
High confidence—safe to automate
Edge Case Detection
Low45%High
Low confidence—requires human review

Translation Layer

Reasoning Layer LLM

The LLM doesn't decide. It translates quantitative outputs into human-readable recommendations.

Raw Output
{ "oee_delta": -0.12, "root_cause": "speed_loss", "conf": 0.89 }
→
LLM Translation
Context-aware language generation
→
Human Readable
"Line 3 OEE dropped 12% due to speed losses. Check conveyor belt tension. (89% confidence)"

What the LLM Does

  • Translates numbers to natural language
  • Adds context from factory ontology
  • Formats for different audiences
  • Explains confidence levels

What the LLM Does NOT Do

  • Generate recommendations from scratch
  • Override quantitative calculations
  • Hallucinate equipment states
  • Make autonomous decisions

Architecture Summary

The 4-Tier Intelligence System

Bottom-Up Intelligence

Each layer builds on the one below. Decisions flow up through physics, statistics, and uncertainty before reaching language.

  • ✓Quantitative foundation, not guesswork
  • ✓Confidence scores on every output
  • ✓Full auditability and decision trace
  • →LLM translates, never decides
Tier 1
Common Data Model

Factory ontology, relationships

Tier 2
AI Inference Engines

Physics-based, calibrated models

Tier 3
Uncertainty Quantification

Confidence intervals, risk scoring

Tier 4
Reasoning Layer (LLM)

Natural language translation

See It In Action

Ready to Close the Decision Gap?

Join the manufacturers who are turning data into decisions—not just dashboards.

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