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Home / AI Solutions / Agentic AI
Agents that act

Agentic AI, in production.

Multi-agent systems wired into core workflows — governed, eval-driven and human-in-the-loop — modelled as part of your Mendix application.

What we do

Agentic AI on Mendix.

Production-grade agentic ai delivered by a Mendix Platinum Partner, backed by Siemens Xcelerator.

Workflows

Multi-agent

Agents that plan, call tools and complete real tasks.

Tools

Function calling

Agents act on your systems through governed tools.

Grounding

RAG

Knowledge-graph and RAG grounding for accurate answers.

Oversight

Human-in-the-loop

Approvals and escalation keep humans in control.

Safety

Guardrails & evals

Content filters, evals and monitoring at every step.

Platform

Agents in Mendix

Agents modelled in-app, under version control.

How we deliver

Discover. Build. Govern.

The same 21-day rhythm across every engagement — discovery in five days, a production proof in twenty-one.

Step 01

Discover

Pin the use case and the ROI hypothesis in a 5-day discovery.

Step 02

Build

A production PoC on Mendix in 21 days — real data, real users.

Step 03

Govern

Security, audit and model risk handled from day one.

Step 04

Scale

Grow on the same platform to 100K users — no rebuild.

Ready to put agentic ai to work?

Tell us the outcome you're after. We'll bring the platform, the AI and the engineers who've done it before.

FAQ

Questions, answered.

What is agentic AI?

Systems where AI agents plan and take multi-step actions across tools and workflows — beyond a single chatbot response.

How do you keep agents safe?

Guardrails, evaluation pipelines, human-in-the-loop approvals and full observability.

Where do agents run?

Modelled inside your Mendix application with version control and governance.

Can agents use our systems?

Yes, through governed tools and connectors to your data and applications.

What is agentic AI?

AI systems where autonomous agents plan and take multi-step actions across tools and workflows — beyond a single chatbot reply.

What is retrieval-augmented generation (RAG)?

A technique that grounds an LLM in your own data at query time, improving accuracy and reducing hallucinations.

How is generative AI different from traditional AI?

Traditional AI predicts or classifies; generative AI creates content and powers copilots and agents.

How much does an AI proof of concept cost?

We deliver a fixed-fee, fixed-scope 21-day production PoC; the exact figure is set after a 5-day discovery.

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