01
Make knowledge machine-interpretable.
A semantic layer, the Knowledge Layer, as the foundation on which agents operate.
Agentic Enterprise
Agents do not fail on the model. They fail on the organisation.
We create the structural foundation so that AI agents can
act autonomously instead of merely simulating it.
Concepts, data and processes are made machine-interpretable, so agents see the same context your best people do.
The decision
Two ways to design an organisation. Only one is future-proof.
Data, information and with them the knowledge about the enterprise are distributed, fragmented and, for artificial intelligence, not usable.
Today
Human-centred organisation
Structures built for human hands.
- Processes bundle activities that are carried out by people.
- Systems are constructed in silos and designed for human interaction.
- Structures, collaboration models and decision paths made for people.
Future
AI-native organisation
Structures for autonomous agents.
- Seamless, autonomous workflows with limited human intervention.
- A semantic knowledge base — a digital twin of the organisation.
- Humans decide on ambiguity, machines on repetition.
The transition
Three things must change simultaneously.
02
Value streams for agents - rethought.
Describe process outcomes, set autonomy levels and leave the execution to the AI agents.
03
The operating model for agents - set up.
Compliance and escalation mechanisms implemented in the technology, not written into the handbook.
Without operating model redesign, AI amplifies an organisation's problems. Used right, AI accelerates how value is created.
The Agentic Enterprise)
What we deliver
Tools don't change an Operating Model.
Whoever seeks a tool finds tools. Whoever wants a use case finds consultants. Whoever needs a roadmap finds templates.
What you won't find elsewhere: a team with method, know-how and experience — from strategy through organisation to technology. So that what emerges in the end is productive value creation with AI.
How we work→The potential
Great potential. So far left untapped.
Projects fail not because of the model, but because the method to actually transform value creation is missing.
- Value potential
- € 4.4 bn
- Value-creation potential of AI agents per year.
McKinsey, 2023
- Cancellation
- 40%
- of agentic AI projects will be cancelled by the end of 2027: unclear value, escalating costs, lack of control.
Gartner, 2025
- Foundation
- 60%
- of AI projects without an AI-ready data and knowledge base will be abandoned in 2026.
Gartner, 2025
- PoC trap
- 88%
- of AI proofs of concept never reach a broad rollout.
IDC, 2025
Scroll to explore
How we work
Introducing AI along your value streams.
We take one process. Break it down along its value creation. Segment it for AI agents. And hand it over with an outcome definition and everything a multi-agent system needs in order to function. Six phases per process, faster each time, because the next one builds on the foundation of the last.
Phase 01
Rebuild the process
We break your process down and reassemble it agent-ready — along the lines of your value creation.
Core steps
- 01Process mapping
- 02Agentic segments
- 03ROI assessment
Phase 02
Extend the Knowledge Layer
An agent can only decide what it understands. This is where we give it meaning: data, ontologies and permissions, in machine-interpretable form.
Core steps
- 01Ontology extension
- 02Data integration
- 03Access & guardrails
Phase 03
Configure the Agent Mesh
Which agent does what, with which tools, within which boundaries. Topology first, implementation second.
Core steps
- 01Agent topology
- 02Tool binding
- 03Orchestration
Phase 04
Test & validate
Before anything goes live: systematically test against real scenarios — functional, ethical, regulatory.
Core steps
- 01Functional testing
- 02Red teaming
- 03Compliance checks
Phase 05
Prepare for your operations
A clean handover into your operations — with runbooks, escalation paths and observability you can operate yourself.
Core steps
- 01Runbooks
- 02Monitoring
- 03Incident response
Phase 06
Go-live & optimisation
A controlled go-live, measurable in the P&L. Then back to step 1 — the next process moves faster.
Core steps
- 01Staged rollout
- 02KPI tuning
- 03Portfolio review
Goal
Agentic AI adoption
We take one process. Break it down along its value creation. Segment it for AI agents. And hand it over with an outcome definition and everything a multi-agent system needs in order to function. Six phases per process, faster each time, because the next one builds on the foundation of the last.
How we work
Introducing AI along your value streams.
We take one process. Break it down along its value creation. Segment it for AI agents. And hand it over with an outcome definition and everything a multi-agent system needs in order to function. Six phases per process, faster each time, because the next one builds on the foundation of the last.
01
Phase 01
Rebuild the process
We break your process down and reassemble it agent-ready — along the lines of your value creation.
Core steps
- 01Process mapping
- 02Agentic segments
- 03ROI assessment
02
Phase 02
Extend the Knowledge Layer
An agent can only decide what it understands. This is where we give it meaning: data, ontologies and permissions, in machine-interpretable form.
Core steps
- 01Ontology extension
- 02Data integration
- 03Access & guardrails
03
Phase 03
Configure the Agent Mesh
Which agent does what, with which tools, within which boundaries. Topology first, implementation second.
Core steps
- 01Agent topology
- 02Tool binding
- 03Orchestration
04
Phase 04
Test & validate
Before anything goes live: systematically test against real scenarios — functional, ethical, regulatory.
Core steps
- 01Functional testing
- 02Red teaming
- 03Compliance checks
05
Phase 05
Prepare for your operations
A clean handover into your operations — with runbooks, escalation paths and observability you can operate yourself.
Core steps
- 01Runbooks
- 02Monitoring
- 03Incident response
06
Phase 06
Go-live & optimisation
A controlled go-live, measurable in the P&L. Then back to step 1 — the next process moves faster.
Core steps
- 01Staged rollout
- 02KPI tuning
- 03Portfolio review
PHASE 01Engineer
Phase 01
Process engineering
We take a value-creation process apart, identify the transitions agents can handle and put a number on the ROI — before any technology is selected.
- 01Process mappingEnd-to-end mapping of the current state. Decision points, data flows, manual interventions — made visible.
- 02Use-case screeningWhich steps are suited to agents, and which stay with humans? Assessment by frequency, complexity and risk.
- 03ROI projectionRobust business cases with assumptions, sensitivities and cut-off criteria — no slideware magic.
Outcome
A prioritised backlog of agent-ready use cases with effort, benefit and risk — ready for decision.
Phase 02
Knowledge Layer expansion
Agents need more than data — they need meaning, context and permissions. The Knowledge Layer becomes the foundation every single agent operates on.
- 01Ontology extensionExisting concept models are extended with agent-relevant entities, relations and rules — domain-specific and versioned.
- 02Data integrationSource systems are connected against the Knowledge Layer. Mapping, quality, freshness — automated and observable.
- 03Access & guardrailsWhich agent may see and do what? Identities, roles, audit trails — from day one, not bolted on afterwards.
Outcome
A semantic layer that all subsequent agents build on without re-design — swappable underneath, stable towards the top.
Phase 03
Agent Mesh configuration
Which agent handles which task, which tools is it allowed to use, where does it hand off to a human or to another agent? Topology first, implementation second.
- 01Agent topologyWhich agents exist, what are their tasks, how do they communicate with each other? Architecture before code.
- 02Tool bindingWhich tools, APIs and data each agent is allowed to use — strictly bound, not open-ended.
- 03OrchestrationWho decides which agent takes the next turn? Central orchestration with clearly defined escalation paths.
Outcome
An agent topology you understand and can operate — one in which responsibility for every step is clearly settled.
Phase 04
Testing & validation
Before anything touches a customer, employee or partner, we test systematically against real scenarios — functionally, ethically, in regulatory terms.
- 01Functional TestingDoes the agent do what it should — in standard and edge cases? Reproducible, automatable, measured.
- 02Red-TeamingSystematic attacks against your own agents — prompt injection, data leaks, ethical boundary cases.
- 03Compliance checksAI Act, GDPR, sector-specific regulation — demonstrably met, with an audit trail.
Outcome
A validation report you can present to your board and your regulator — no black box.
Phase 05
The operations handover
The transition from programme to steady-state operations is the phase where most AI initiatives fail. We build that transition in from day one.
- 01RunbooksWhat do operations do when the agent shows new behaviour? Clear instructions for action instead of gut feeling.
- 02MonitoringLatency, cost, quality, drift — visible live, with alerts, traceable over their history.
- 03Incident ResponseEscalation paths, rollback mechanisms, post-mortem routine — operations know what to do.
Outcome
An operating model your own team carries — we leave the programme without it collapsing behind us.
Phase 06
Go-live & continuous improvement
A controlled roll-out in waves, with clear abort criteria. After that: measurable iteration in the P&L — and back to step 1 for the next wave.
- 01Staged RolloutStep-by-step roll-out by user group or geography. Every stage with clear go/no-go criteria.
- 02KPI tuningWhich metrics rise, which fall, which stay flat? Hypothesis-driven optimisation instead of belief.
- 03Portfolio reviewWhich use case scales, which one gets stopped? Portfolio decisions based on real numbers, not wishful thinking.
Outcome
Impact in the P&L — and a programme that keeps evolving on its own instead of freezing up.
The principles for scaling AI
Four things we want you to take away.
The principles that decide whether introducing AI agents succeeds.
Client examples
A selection of our engagements.
Strategy, organisation and technology from one team, through to go-live. A selection of current engagements.
Enterprise-wide data & AI transformation.
Defining, implementing and continuously evolving the enterprise-wide data & AI strategy of a global OEM — from the board down to the individual divisions, with a significant annual value contribution firmly anchored in the P&L.
Learn moreEnterprise IT Transformation.
Individual agents run into their limits — too many tasks, too little interplay. We built a multi-agent architecture with specialised agents, central orchestration and clear guardrails. Content-agnostic, working across domains, in production.
Learn moreERP strategy & rollout.
Vehicle data scattered across departments, with no shared model. We created a unified ontology for all vehicle types, configurations and attributes and integrated the existing data through automated pipelines — a consistent foundation for AI applications.
Learn moreHow we help across the board
We help where the pressure is greatest.
Nobody builds the Agentic Enterprise in one go. Depending on where you stand, a different entry point is right.
When the supervisory board demands an AI strategy with P&L impact
Strategy & TransformationWhen “What does an AI-native organisation look like?” is still an unanswered question
Organisation & Operating ModelWhen pilot projects stall at 85% and do not make it into production
Technology & ArchitectureWhen millions have gone into data, but agents still do not answer reliably
Knowledge Graph & Ontology
Next step
Give us one process. We will reveal its potential to you.
On a real process from your organisation, with an honest assessment of whether it is agent-ready.