Polarix brings together documents, sensor data and video and builds AI on top that supports decisions, in your organisation or in your own product. Humans decide.
Polarix is active in the maritime sector, horticulture and security: sectors that each have their own data and expertise.
Knowledge of vessels, AIS data and project history, translated into AI that supports your team with quotes, planning and commercial choices.
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Greenhouse sensors, cultivation data and past seasons, combined into AI that forecasts production and planning and helps steer the crop.
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Camera footage, sensors and alerts, connected to AI that shows in real time what is happening and what needs attention.
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Domain knowledge, documents, sensors and images from existing systems are unlocked. Data from all systems becomes one coherent picture.
AI recognises patterns, flags anomalies and predicts opportunities and risks. Following your organisation's playbook: your way of working, rules and priorities.
Action follows: an alert to the right person, a report, an update in a system or advice in your own product. Within the limits you set, with the reasoning attached.
Every outcome is tracked: was it right and what did it deliver? Quality is tested on your own data and measured continuously, so the system keeps improving.
Your data stays yours. Everything can run locally or on-premise, fully GDPR-compliant, and the code can be handed over to you.
An approach developed and tested in earlier projects, with a go/no-go after every step.
A conversation about your organisation, your data and the AI opportunities available to you. Those opportunities come from cases within your sector and from new developments in AI, such as models that understand camera footage and agents that carry out tasks on their own. This makes clear what is feasible today and what is not yet.
A developed and tested framework to reach an organisation-wide AI roadmap in one week. Together with your team, workflows, data and bottlenecks are mapped per department and opportunities are prioritised. The week ends with the roadmap and a go/no-go for the first application.
Eight weeks to production, on the existing Polarix engine. Every agent gets a fixed role, and the agents work from a shared context with memory across conversations and tasks. Every outcome is verifiable: tested against a reference set of real questions from practice and traceable to its source.
Our multi-agent systems operate as AI teams, each assistant has a role, memory, and task. They collaborate like colleagues to analyze, decide, and act across your organization, reducing manual work and accelerating results. We always keep humans in the loop, ensuring you maintain full control over critical decisions while AI handles routine tasks.
Polarix AI agents assist within the tools your people already use. Whether it's documents, emails, or internal workflows, our agents extract insight, link knowledge, and support decisions, without forcing anyone to change the way they work.
Your data stays in your hands. Our AI runs locally or on-premise, with full support for Dutch language and legal standards. No cloud lock-in. No vendor dependency. 100% control over your models, data, and compliance.
Cases on implementation or our solutions
AIVA brings camera surveillance back from thousands of detections to a handful of well-founded situations: in 24 hours of footage from 10 cameras, 10 remained that the operator needed to see.
Building an effective multi-agent system with large language models (LLMs) requires thoughtful planning of how the agents will think, act, and collaborate. This guide walks through key design choices, from decision-making patterns and core components to architectural styles and human oversight, to help you develop a modern multi-agent system. We’ll cover: * Agent Decision-Making Patterns: Choosing between ReAct loops, plan-then-act, reflection feedback, or tree-of-thought exploration. * Core
OVD models can detect objects specified by arbitrary human language inputs, such as category names or descriptive phrases, provided at inference time without any need for model retraining.
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