Enterprise Intelligence Architecture
How context, evidence, models, governance, authority, and actions should fit together.
Founder · Verimir
Ben Schauerhamer
Enterprise Intelligence Architecture & Engineering
I build systems at the boundary between enterprise architecture, software, infrastructure, operations, and AI. Verimir grew from a recurring problem I encountered across all of them: organizations have enormous amounts of information, but very little durable operating context connecting what is true, why it is true, who has authority, what changed, and what should happen next.
- 01Systems engineering
- 02Enterprise architecture
- 03Operating context
- 04Verimir
Operating evidence returns to the system ↺
The problem I kept finding
The enterprise already has the information. It is missing the connective tissue.
I have spent more than two decades moving across software engineering, systems engineering, quality, enterprise architecture, consulting, infrastructure, delivery, and business systems.
The technologies changed. The organizational failure pattern did not.
A requirement existed in one system. Implementation existed somewhere else. Production had another version of reality. Policy lived in documentation. Authority existed mostly in people’s heads. Evidence was scattered across all of them.
Humans learned to compensate for that fragmentation. AI makes it impossible to ignore.
- 01Systems
- 02Evidence
- 03Context
- 04Authority
- 05Action
- 06Outcome
Operating evidence returns to the system ↺
The architecture
The model should not become the enterprise’s memory.
Models are powerful reasoning engines, but a model context window is not an enterprise operating model.
Organizational understanding needs to survive prompts, sessions, model generations, providers, applications, and infrastructure changes.
That led to the concept of the Operational Warehouse: a durable representation of enterprise entities, relationships, state, evidence, authority, history, actions, and outcomes.
Models are replaceable. Enterprise memory should not be.
The Operational Warehouse preserves durable enterprise context beyond any one prompt, model, provider, or application.
Explore the Operational Warehouse →Engineering intelligence
AI needs an engineering discipline around it.
I do not think consequential enterprise AI should be treated as a sequence of prompts and integrations.
It has to be observed, diagnosed, modeled, engineered, measured, challenged, and improved as a system.
Enterprise Intelligence Engineering observes the operating environment, diagnoses what changed, models the problem, prioritizes work, engineers a response, measures outcomes, learns, and returns to observation.
That is the basis of Enterprise Intelligence Engineering.
Explore Enterprise Intelligence Engineering ↗Sovereign intelligence
The intelligence provider should not become the owner of enterprise understanding.
Models will change. Providers will change. Infrastructure will change.
The durable intelligence of the organization—its operating context, evidence, policies, authority, history, and outcomes—should remain within an explicit enterprise control boundary. Verimir is being built around that principle.
Managed by Verimir. Controlled by you.
Sovereignty keeps enterprise context, evidence, authority, and continuity inside a defined control boundary; rights to platform intellectual property remain agreement-defined.
Explore Sovereign Intelligence →Background
Systems thinking across software, infrastructure, and operations.
My career has crossed software and systems engineering, quality leadership, product engineering, enterprise consulting, delivery operations, architecture, infrastructure, business operating systems, and AI.
Before working in enterprise technology, I served in the U.S. Army working on AH-64D Apache Longbow armament and electrical systems—an early environment where systems integration, reliability, evidence, and consequences were not abstract concepts.
What to talk to me about
Bring me the system that does not quite make sense.
If the problem crosses several systems and nobody has the complete picture, that is usually where I am most interested.
Sovereign AI
How to retain control of organizational intelligence while still using modern models and managed infrastructure.
Delivery Systems
How intent becomes implementation, validation, release, production state, and learning.
Complex Operating Problems
Especially where business process and technical architecture no longer line up.
If the system has outgrown the informal connections keeping it understandable, we can start with the operating context around one useful problem.
Frans brings the business-operations perspective that keeps enterprise intelligence connected to customers, revenue, partnerships, and the systems that support them.
Business operations perspective → Meet Frans