Secure enterprise AI for knowledge-intensive organisations

Turn scattered company knowledge into reliable answers, faster decisions and controlled AI.

We open up your existing documents, systems and permissions into an AI knowledge base. Management intelligence and governed workflows grow on top of it – stage by stage.

No rip-and-replace Source-cited answers Existing permissions preserved Processing matched to data sensitivity

No mega-project, no all-or-nothing.

Knowledge before autonomy. Rules before agents.

The Problem

AI rarely fails because of the model. It fails on business reality.

Knowledge is scattered, numbers arrive late, and every new tool creates another island. What's missing is a shared basis for work and decisions.

Knowledge sits in inboxes, folders and individual heads.
Reliable answers in seconds – with source and permissions.
Decisions wait for Excel, month-end close and manual reports.
Operational status and financial impact in one clear steering view.
Every new AI use case becomes the next one-off solution.
Your existing systems become the shared basis for data, knowledge and governance.
With sensitive data, it stays unclear which tool is even permissible.
Protection needs determine the model, permissions and processing environment.

Scattered knowledge does not merely waste search time. It delays decisions, increases dependence on individual employees, duplicates work – and lets uncontrolled AI use spread outside any governance.

What it costs today
What replaces it
Experienced staff answer the same questions over and over.
Verified answers are available on demand.
Management waits for manually produced reporting.
Decisions rest on current operational data.
Knowledge leaves the company with the people.
Institutional knowledge stays available and transferable.
Teams use AI tools outside any governance.
Models, permissions and data environments are governed.

What it looks like

The idea does not convince. The result on screen does.

Two excerpts from working interfaces: a sourced answer and a steering view.

Demonstration with synthetic sample data – no client data

AI Knowledge Base

An answer that brings its source along.

The question goes to your own files, not to a foreign model. Every statement carries the passage it came from – verifiable instead of believed.

  • Case references and memos as cited sources
  • Processing entirely local, no cloud call
  • An answer in seconds instead of a file review
AI knowledge base interface: a sample query about comparable dismissal cases; the answer cites three sources and states that processing is entirely local.
Demonstration with synthetic sample data. Case references, cases and names are invented.

Operating Intelligence

The picture before the month is closed.

Margin waterfall and net liquidity straight from the booked figures – not from an estimate and not from an overnight spreadsheet session.

  • From revenue to earnings, every stage visible on its own
  • Net liquidity as one condensed figure
  • Actual, budget and prior year in the same view
Financial dashboard: metric tiles for revenue, EBITDA margin, net liquidity and DSO, with a profit and loss waterfall from revenue down to earnings before tax.
Demonstration with synthetic sample data. All figures are invented.

The Foundation

Structured knowledge is the foundation for reliable answers, meaningful analysis and controlled AI.

Your systems provide the sources – we turn them into a shared basis of data, documents and permissions. The result is not a concept but a product: the AI Knowledge Base.

AI Knowledge Base

Scattered knowledge becomes a reliable corporate memory.

Existing documents, filing systems and applications are opened up in a structured way – role-based, traceable and with solid sources.

Stage 1 · The Foundation

Company knowledge that answers – instead of being searched for.

Stop searching: contracts, policies and the knowledge of your most experienced people – available in seconds, with source and permissions. Productive from day one. And the foundation for everything that comes next.

01Answers with sources

Every statement stays traceable and verifiable.

02Existing permissions

Users only see content they are authorized for.

03Knowledge secured

Experience stays in the company and transfers faster.

04Systems connected

Existing sources are opened up, not replaced by another island.

The Build-Out Stages

On the knowledge foundation, the Agentic Operating System grows stage by stage.

No mega-project, no all-or-nothing: once the foundation is in place, every stage can be implemented on its own – and pays off on its own.

02

Operating Intelligence

Stage 2 · Numbers that lead

See operational status and financial impact in one view – and decide before month-end close arrives.

03

Business Rules & Governance

Stage 3 · AI without losing control

Permissions, approvals and accountability govern every use of AI – before it happens, not after.

04

AI Agents & Workflows

Stage 4 · Time back for your core business

AI agents take over clearly bounded routine tasks – on your data and knowledge base.

The Target

Agentic Operating System

Knowledge, numbers, rules and digital roles work on one platform. You set the pace.

Who it's for

Built for companies whose knowledge is their most valuable asset.

You get answers, numbers and AI you can rely on – without giving up control of your data.

Confidential knowledge work

Regulated professions

Finally put your case files and expertise to work with AI – without compromising professional secrecy. Law firms, tax advisors and notaries find any answer in seconds, role-based and fully traceable.

  • Make confidential documents safely usable
  • Unlock knowledge role by role
  • Process specially protected data with full sovereignty

Operational steering

Mid-sized companies & management

Secure the knowledge of your best people, see your numbers before the month is over – and make decisions while they still matter.

  • Reduce dependence on individual know-how
  • Connect operations and financial impact
  • Integrate AI into existing workflows – under control

How your solution grows

An architecture that grows with the value.

Each stage builds on the previous one. Nothing is built twice, nothing is thrown away.

Agentic Operating System
Level 5

Controlled AI agents

Level 4

Business rules & governance

Level 3

Knowledge & intelligence

Level 2

Structured company data

Level 1

Your existing systems

We build on what already works: systems, data, documents and responsibilities.

Data Sovereignty & Model Control

The right question is not: on-premises or cloud.

What matters is which data may be processed for which purpose, by which model, in which environment.

A

Non-critical data

Process flexibly

Public content, research and clearly bounded assistance can make efficient use of modern cloud models.

B

Internal data

Controlled environment

Confidential information belongs in enterprise environments secured contractually, technically and organizationally.

C

Specially protected data

Process with sovereignty

Professional secrets and sensitive HR, client or contract data can be processed locally or in isolated environments.

How We Work

Five stages, each with its own purpose.

Every stage stands on its own. After each one you decide again whether and how to continue.

0

AI Readiness Score

Five questions on this page. Free, instant, no sign-up.

1

Diagnostic call

30 minutes: put your result in context and identify the strongest lever.

2

AI Readiness Assessment

Assess data, knowledge, systems and protection needs – with a roadmap and target architecture.

3

Pilot

Prove one clearly bounded use case in production.

4

Implementation & operation

Integrate into systems, permissions and workflows, and assure quality over time.

The next step costs you 30 minutes – nothing more. Book a diagnostic call →

Who stands behind it

Four perspectives from one partner that are usually bought separately.

Methodology alone is not the differentiator. What matters is that technology, governance, financial impact and target architecture are designed together – and that one person is accountable for it.

Julius Oppel

Founder, Agentic Corporate

Focus
Governance, audit and compliance; ERP transformation and business intelligence
Sectors
Real estate, facility management and construction; automotive and suppliers
Region
German-speaking Europe and the Netherlands

Fifteen years in governance, audit and compliance, then ERP transformation and process work: quality, business intelligence, export control and customs, accountability as a compliance officer. That order is no accident – anyone who has first examined controls and then rebuilt systems knows both sides of the table.

This is where the working method behind this offer comes from: a translator between IT and the business, from the process to the tool. AI initiatives rarely fail because of the model. They fail because nobody holds the process landscape, the permission situation, the financial impact and the target architecture in view at the same time.

AI

AI & Automation

Models, knowledge systems and workflows are aligned to concrete operational value.

Governance

Control by design

Roles, approvals, data protection and traceability are part of the solution – not an afterthought.

Finance

The finance lens

We invest where it pays: margin, cash flow and enterprise value set the priorities.

Architecture

Enterprise architecture

What you build today you won't throw away tomorrow: every stage fits into the same target architecture.

Frequent questions

What decision-makers want to know first.

The questions that come up in the first call anyway – answered here up front and without detours.

Which systems can be connected?

The ones you already work with. On the data side typically exports from your leading system – DATEV, Lexware, SAP, bank data, Excel. On the document side filing systems, case files, contracts, expert opinions, policies and internal memos.

Your systems stay where they are. They are opened up and connected, not replaced by yet another island.

Does our data leave the company?

You decide that per data class; we do not decide it for you in the abstract. For specially protected data – professional secrets, sensitive HR, client or contract data – the answer is no: processing runs locally or in an isolated environment, with no cloud model in the processing path.

For uncritical content such as research or general assistance, modern cloud models can be used if you want them.

Which models are supported, local and cloud?

Both, and the assignment follows the protection need: local models on your own hardware, contractually and technically secured enterprise environments, and cloud models for clearly uncritical purposes.

The route is not a matter of belief but an assignment that is documented and verifiable.

Are our existing permissions preserved?

Yes. That is a precondition, not an option. Whoever may not see a document today will not get it through the knowledge base either – neither as an answer nor as a cited source.

For larger rollouts a role and access concept per practice area is added.

How long until the first result?

A pilot stays deliberately small: one bounded use case, proven in production rather than in a presentation. A foundation project is scoped at three months, a full rollout at six.

Between commitment and start there are usually four to five weeks of lead time.

What exactly do we get out of the assessment?

An evaluation of your data, document and system situation, the classification of protection needs, prioritised use cases and a roadmap with a target architecture.

The result is a document you could continue working with even without us. The effort is credited against a subsequent project.

How do you avoid vendor lock-in?

After the project ends you receive full access to the infrastructure that was built. No closed operation, no forced subscription, no black box only we can open.

That is also the reason for the staged architecture: what you build today you will not have to throw away tomorrow.

Who owns the data and the resulting workflows?

You do. The content in any case, and equally the knowledge base that is built, the analysis logic and the workflows set up. We build them in your environment, not in ours.

What investment should we expect?

That depends on scope – on the number of sources, on the protection need, and on whether one practice area or the whole organisation is opened up. It cannot be answered seriously before the diagnostic call, and that is exactly what the call is for.

What we can say up front: a paid discovery is credited in full against a subsequent project. You do not pay for the groundwork twice.

Stage 0 · AI Readiness Score

Five questions. Your position across five dimensions.

Where do data, steering, AI adoption, governance and ownership stand today – and which of them holds you back first? You see your biggest weakness, the recommended first step and three immediate priorities.

1How clean and accessible are your data and documents today?
2How quickly can you get reliable numbers when a decision is due?
3How far along are you in actually using AI day to day?
4How confident are you that sensitive data never ends up in a non-compliant AI tool?
5Is there a clear owner for AI in your organization?
5 questions to go

Answer all five questions to see your assessment.

Your assessment as a one-page roadmap

We send you your result together with the priority order as a single page you can pass on – to management, to IT, or to whoever else you need to convince.

You see your result instantly. No email required. Your details are only sent when you submit the form.

The Next Step

Others are still testing tools.
You are laying the foundation.

In a 30-minute diagnostic call we clarify where you stand and where to start – concrete, no strings attached, no slides.