AI Trust & Transparency

Trust is built in with the Frends.

As enterprises increasingly adopt AI-driven automation, Frends has built trust and transparency directly into its integration platform architecture. Rather than treating these as afterthoughts, Frends makes AI governance, explainability, and security foundational elements of every AI-powered integration.

Transparent AI Operations by Design

Frends eliminates some of the "black box" problem through its Intelligent AI Connector, which logs every AI decision with complete reasoning traces. When AI makes a decision within a Frends Process, you can see exactly what prompt was sent, how the AI reasoned through the problem, and what conclusion it reached. This transparency extends beyond simple input/output logging to capture the AI's actual chain-of-thought process.

The platform's BPMN 2.0 visualization makes AI logic part of the visual workflow, so stakeholders can literally see where AI reasoning occurs within the Processes. Instead of hidden AI operations, every AI step appears as a specialized Task shape on the Process diagram, making complex AI-driven workflows transparent to both technical and business users.

Enterprise-Grade Security and Compliance

Frends ensures AI models never retain or learn from customer data. Each tenant runs dedicated AI instances, keeping data processing securely contained within customer-specific configurations. Only metadata and variable references are shared with AI models—actual data values never leave the secure Frends environment.

Every AI interaction creates detailed audit logs that track what data was accessed, what decisions were made, and what actions were taken. Administrators can review all AI sessions and even delete log entries containing sensitive information, ensuring compliance with data protection regulations.

As a European platform, Frends builds GDPR and EU AI Act compliance into its AI features from day one. The platform's reasoning logs and explainable AI decisions align with upcoming EU AI regulation requirements for transparency in automated decision-making.

Implementation in Practice

Frends provides AI monitoring in Frends Control Panel where users can monitor AI agent activities in real-time and intervene when necessary. The platform shows token consumption and projected AI costs during both development and runtime, preventing unexpected expenses while maintaining full visibility into AI resource usage.

Critical Processes can still be built to include human in the loop, where the result of AI operation may diverge the Process to contact human personnel for confirmation. This ensures sensitive operations maintain human oversight while still benefiting from AI acceleration. Users can configure workflows that require human approval for high-stakes decisions or allow full autonomy for routine tasks.

Frends supports on-premises AI models through integration with platforms like Ollama, ensuring organizations can keep sensitive data completely within their own infrastructure. This hybrid approach allows companies to balance AI capabilities with strict data sovereignty requirements.

The Frends Advantage

Unlike platforms that retrofit AI governance onto existing systems, Frends designed its AI capabilities with enterprise trust requirements from the ground up. The platform's semi-deterministic AI orchestration combines the intelligence of AI reasoning with the predictability of structured business processes.

This approach enables organizations to deploy AI in mission-critical environments with the confidence that comes from complete transparency, robust governance, and enterprise-grade security.

Key differentiators include:

• Visual AI orchestration within standard BPMN workflows

• Complete reasoning transparency with logged decision processes

• IT-governed tool access preventing unauthorized AI actions

• Enterprise security with data sovereignty options

• Regulatory compliance built for European standards

By making AI operations as transparent and governable as traditional integrations, Frends enables enterprises to move confidently from AI pilots to production-scale automation. The result is AI that works for the business, not against it—delivering intelligence within the trust frameworks that enterprises require.