Solution

LLMs and XimTier do different jobs — together they're complete

If ChatGPT explains, XimTier proves. LLM language + XimTier computation = real decision AI.

"ChatGPT presents in the meeting. XimTier produces the answer before the meeting."

COMPARISON

LLM (language) + XimTier (computation) = real decision AI

LLM LLM (language interface) ChatGPT · Claude · Gemini	XimTier XimTier (computation engine) Decision Intelligence · Reverse What-If
✗Language understanding / natural conversation	✓Actual numeric computation — regression, time-series, classification
✗General knowledge retrieval / text generation	✓Reverse What-If — input a goal, derive the optimal variables
✗Real statistical computation (regression, SHAP) — not possible	✓SHAP-based mathematical XAI — passes regulation
✗Reverse What-If — structurally impossible	✓On-prem / private — data never leaves your environment
✗No access to internal enterprise data	✓Industry-vertical depth — manufacturing, hospital, public
✗Cannot satisfy regulated XAI duty	✓Same input → same output — reproducible consistency

Prescription, not diagnosis

Most AI reports stop at "we hit 0.9 accuracy"

What the operations director wants to know is what to change on Monday.

The top-ranked driver is not an action item

In an analysis of 480 convenience stores, the

"Location matters" is a correct finding but an unusable one. A report that skips this distinction leaves the reader asking, "So what am I supposed to do?"

How XimTier goes one step further

STEP 1

Keep only what can be changed

Each variable is judged for controllability. Location and competitor count are excluded; cleanliness scores, promo execution rate, and order accuracy become action candidates.

STEP 2

Strip out variables that already contain the outcome

Post-hoc fields like monthly settlement and revenue rank are removed from training. Including them pushes accuracy to 1.000 while telling you nothing.

STEP 3

Quantify the gap

Stores in the top third of promo execution show a +23.1%p higher rate of being top performers than the bottom third — 1.8× the odds.

STEP 4

Turn the unchangeable into targeting

The 180 stores with 3+ nearby competitors perform at one third the rate of zero-competitor stores. Concentrating support there beats spreading it evenly.

When we don't know, we say so

If the predictive basis is too weak or a range has no data, the output is "cannot compute." We do not fill blanks with averages or defaults. Excluded variables are shown along with the reason they were excluded.

5-STEP WORKFLOW

5-Step One-Stop Workflow

STEP / 01

Data Exploration

WhatDataAI

Non-experts grasp data structure in minutes

STEP / 02

Statistical Analysis

Regression · Classification

Regression, time-series, and classification — auto-executed

STEP / 03

Reverse What-If

★ Core

Input the goal → derive the optimal variables

STEP / 04

Auto Report

One-click Report

Decision-ready executive report, generated instantly

STEP / 05

AI Q&A

Conversational

Ask questions about results in natural language