WHY NOT LLM
ChatGPT explains.
XimTier proves.
LLMs, regression and generic ML answer only part of a decision. XimTier solves the math of 'how much to change to hit the target.'
- Not a tool you prompt.
- Not a regression report.
- Not a black-box model.
- It's a decision engine, with the math attached.
MATRIX
A 4-row comparison
| Tool | Does it explain *how* variables matter? | *How much* to change to hit a target? | Math-backed proof auto-attached | 100% on-prem |
|---|---|---|---|---|
| ChatGPT · Claude · Gemini | △ Hallucination risk | ✗ | ✗ | ✗ |
| BI (Tableau · PowerBI) | ✓ Regression | ✗ | ✗ | ✓ |
| Generic ML (DataRobot · H2O) | ✓ | △ Limited | ✗ Black box | △ |
| XimTier | ✓ | ✓ Reverse What-If | ✓ SHAP auto | ✓ |
DATA
3 of 4 enterprise LLM POCs never reach production.
Hallucination, audit-unfit, and missing numerical validation — the quantitative reason behind 'why didn't ours work?'
VS
Same input, different output — ChatGPT explains plausibly, XimTier proves numerically.
ChatGPT scores partially only on explainability. XimTier hits production-ready on all four axes.
ENEMIES
Two enemies we fight
// ENEMY 01
Enemy #1 — Gut feel and spreadsheets
Senior-analyst intuition, manual Excel work, and BI dashboards that only show retrospective analysis. None can answer 'how much to move to hit the target?'
// ENEMY 02
Enemy #2 — LLM hallucination
ChatGPT/Claude *generate* plausible explanations but not data-grounded mathematical *proof*. They cannot survive an audit.
MOAT
Why this matters
Reverse What-If
Beyond regression — reverse-solve each variable's optimum to hit a target in 0.18 seconds.
SHAP auto-attached
Every decision ships with per-variable contribution and mathematical justification. EU AI Act Article 13/14 evidence pack.
100% on-prem
Zero external LLM calls. Passes finance, defense, and medical security policy as-is. Sovereign AI operation.
Three regs at once
EU AI Act + Korea AI Framework + KR MFDS medical-device guidance — one auto-generated document set.