Modular platform for autonomous AI agents

CX Solutions

AI Solutions that move safely into action: CreaLog combines AI models, company knowledge and existing systems into controllable solutions ready for productive use – in the cloud, on-premises or hybrid.

Discover the architecture

Trusted AI platform

Made in Germany

Cloud / on-prem / hybrid

Operation

CreaLog offers a modular platform for building, integrating and operating AI agents. Prompt-based agents can be connected to company knowledge, MCP tools, backend systems and communication channels – technology-neutral and suited to cloud, on-premises or hybrid scenarios.

Security & compliance

Made in GermanyISO 27001GDPR-compliantEU AI Act

An AI agent that responds makes a good impression. An AI agent that takes action needs a solid foundation

Models, knowledge, tools, processes and operations must work together. Taking a holistic view of the entire process and ensuring a robust architecture takes precedence over technological ideologies.

Our platform combines these building blocks into a robust architecture. This results not in isolated FAQ bots, but in AI agents that utilise information, carry out defined tasks and integrate seamlessly into existing processes.

Platform architecture

The powerful AI-driven Service Delivery Platform connects AI components and backend access. Governance and data sovereignty are the foundation of all solutions for trust and control.

Interaction – voice, chat, digital applications, assistance
Orchestration – prompts, dialogue control, context, routing and workflows
Hybrid AI stack – from rule-based applications to agentic AI
Integration – APIs, tools, MCP, knowledge resources, specialist systems
Governance and operations – roles, logging, monitoring, fallbacks

AI stack

Add models · swap components · adapt operating models

Rules

Stable and auditable

For clear processes with predictable, verifiable behaviour.

RAG

Knowledge-driven

Context-related answers from approved company knowledge.

Agentic AI

Capable of acting

Controlled actions in connected systems via defined interfaces.

MCP-native integration for knowledge, tools and actions

MCP connects AI agents with approved knowledge resources, data sources, tools and backend functions. Defined interfaces create the basis for controllable access and traceable actions.

Provide knowledge · release tools · connect systems · document results

Create agents by prompt. Secure processes by workflow.

The role, behaviour and tasks of an AI agent are defined via prompts. Rules, MCP tools, routing and workflows cover the steps where clear processes, approvals or unambiguous results matter.

The core process

From enquiry to traceable action

Identify
Recognise the enquiry, the process and the systems needed.
Read
Retrieve relevant data and knowledge sources deliberately.
Validate
Check rules, rights and plausibility.
Act
Carry out approved actions safely.
Evidence
Log sources, tool calls and results.

Governance by design

AI agents that can act need clear responsibilities and defined points of intervention. Governance is therefore not added afterwards but anchored in the platform, the integration and the operation.

Roles and approvals

Define who may use or change knowledge, prompts, tools and actions.

Human in the loop

Hand sensitive, complex or ambiguous cases over to staff, deliberately.

Logging and traceability

Document sources, tool calls, decisions and results.

Monitoring and fallbacks

Observe quality and process flows and use defined fallback paths.

Platform, integration and operation from a single source

Connecting AI components, communication and backend systems on one shared basis. Service development, integration and operation interlock.

The modular architecture can be integrated into existing system landscapes and designed for cloud, on-premises or hybrid scenarios.

Own platform · open choice of technology · integration of complex systems · support across the entire lifecycle

Data sovereignty is an architectural decision

Cloud, on-premises or hybrid: data flows, model access and operating models are shaped to suit the deployment scenario.

Depending on requirements, local models, cloud services or combined setups can be used. Rights and roles, approvals, logging, monitoring and fallbacks are built into the platform, the integration and the operation from the outset.

Selectable models · controllable data flows · controlled system access · traceable actions

From MVP to scale