Why IW Partners Team Request Demo

Why AI Agent Projects Fail

You're building agents directly on fragmented context. That's like building dashboards on raw CSV files.

No Shared Ontology

Every team defines "revenue," "margin," and "demand" differently. Agents inherit these inconsistencies and amplify them.

Tribal Knowledge

Critical decision rules live in people's heads. When they leave, institutional knowledge walks out the door.

No Decision Layer

Data warehouses store facts. But nobody structured the logic that drives actual operations.

PRE-AI ERA
Fragmented Data
Data Warehouse
Dashboards & Reports
Human Interprets → Decides → Acts
PARADIGM SHIFT
AI ERA
Data Warehouses Still Exist
Intelligence Warehouse
Outcome Agents
Autonomous Action + Human Oversight

Data warehouses let you ask "what happened?" reliably.
Intelligence Warehouse lets agents ask "what should I do?" reliably.

What's Being Warehoused

Not data. Understanding of the business — structured, queryable, executable.

DATA WAREHOUSE
Facts
Transactions
Dimensions
The "what"
vs
INTELLIGENCE WAREHOUSE
Ontology what things mean, how they relate
Metrics how we measure, with calculation logic
Decisions how we decide, with rules & thresholds
The "why" and the "how"

From Tribal Knowledge
to Autonomous Decisions

Three steps to go from "it's all in people's heads" to AI agents that make decisions like your best operators.

1

Extract the Decision Logic

MORRIE talks to your domain experts — supply chain managers, pricing analysts, sales leaders — and extracts the rules, thresholds, and edge cases they use to make decisions every day.

Example
“When lead time exceeds 14 days and demand variance is above 20%, we bump safety stock by 1.65× the forecast error.”
Captured as a structured decision blueprint
2

Build the Intelligence Graph

Blueprints are structured into a unified knowledge graph that connects ontology (what things mean), metrics (how you measure), and decisions (how you act) — across every function.

Ontology
Metrics
Decisions
Every concept, metric, and rule — connected and traversable
3

Agents Traverse & Execute

AI agents don't guess — they follow the same decision paths your best people do. They traverse the graph, pull the right rules, and execute autonomously — with full traceability.

90%
Autonomous execution
10%
Human-in-the-loop with full decision traces
100%
Explainable & auditable

They're Adding AI to Data.
We Built Intelligence from Scratch.

OTHERS

Data warehouses with conversation layers bolted on. Adding AI to data. No ontology. No decision nodes.

Data → trying to make it intelligent
INTELLIGENCE WAREHOUSE

Purpose-built intelligence infrastructure. Ontology, metrics, and decisions as first-class traversable nodes.

Intelligence → built as the foundation
You need an intelligence warehouse before AI agents work. Building agents without one is like building dashboards on raw CSV files.

Build the Foundation
Your AI Agents Need

Stop building agents on fragmented context. Start with the infrastructure layer that makes AI actually work.