Edge AI · MLOps · Computer vision — Athens, Greece
Plaixus takes machine learning from notebook to factory floor, farm, robot and satellite. We design the models, build the pipelines and keep them running at the edge, on hardware you already own.
Who we are
Plaixus is a Greek deep-tech SME founded in 2022. We specialise in industrial-grade machine learning, MLOps and edge AI, with a focus on computer vision and data analytics for industrial and agricultural settings.
Most AI projects stall between a promising prototype and a system operators can trust. That gap is our whole business. We work across the full lifecycle: data ingestion and curation, model training and optimisation, edge deployment, monitoring and explainability.
The name comes from plexus: a network of pathways that carries signals between the centre and the extremities. That is what we build: code and data moving gracefully along the cloud–edge continuum.
Discuss your use caseWhat we build
We combine them as the problem demands. Most engagements start with a vision or analytics question and end with a monitored pipeline running at the edge.
Detection, tracking, counting, anomaly and quality inspection models that run on-device with low latency and no dependency on a stable uplink. Our livestock lameness-detection work under the AGRARIAN programme is a typical example: cameras in the barn, inference at the edge, alerts to the farmer.
Reproducible training, automated deployment and continuous monitoring so a model stays accurate long after launch. Drift alerts, retraining triggers and explainability reports your operators can read.
Train shared models across hospitals, homes or plants without moving raw data. Full compliance with data-protection rules, with the accuracy of a pooled dataset.
We connect models to the systems that matter: DCS, PLC and SCADA on the plant floor, ROS 2 on robots, ESA's NanoSat MO Framework in orbit, with Kubernetes orchestration end to end.
From sensor time series to dashboards and forecasts: predictive maintenance, yield estimation, grid optimisation and the NLP tooling to make reports searchable.
How it fits together
Training, optimisation and orchestration happen in the cloud or your data centre. Inference, buffering and control happen at the edge, next to the camera, the controller or the payload computer. Plaixus is the layer in between that keeps the two in sync: models flow down, telemetry and drift signals flow back up.
Because the layer sits on open frameworks, you are never locked into our tooling, and your team can operate it after handover.
Map this to your setupPlatforms
Each one packages a proven pipeline for a specific environment. We use them to start your project weeks ahead, then tailor the last mile to your equipment and data.
Deploy and monitor edge AI applications on satellites, combining the power of the cloud with the timeliness of on-board inference for Earth observation and payload autonomy.
ESA NanoSat MO Framework, KubeEdge, EdgeX, KubeflowDevelop AI algorithms in the cloud and push complete pipelines automatically to configured robots at the edge, closing the loop between perception models and ROS 2 control.
ROS/ROS 2, KubeEdge, Akraino, Argo, KubeflowBridge industrial IoT with the cloud and your existing DCS, PLC and SCADA. Removes the data silos that block AI applications in plants and factories.
KubeEdge, EdgeX, Fledge, OPC UATrain federated models on smart-home and grid data streams. Extends to distribution system operators for substation and RTU data, optimised grid use and predictive maintenance.
Home Assistant, openHAB, HomeEdgeUse multiple healthcare data repositories to train federated AI models in full compliance with data-privacy regulations, without centralising patient data.
Kubeflow, KubeEdge, TensorFlow Federated, PySyftSectors
Physical environments with real constraints: dust, distance, latency, regulation. That is where edge AI pays for itself.
Visual quality inspection, predictive maintenance on rotating equipment, and IIoT data unification across legacy control systems.
Camera-based animal health monitoring, yield and growth estimation, and offline-capable field devices for farms with weak connectivity.
Perception models integrated with ROS 2, deployed and updated over the air to fleets of mobile or industrial robots.
Federated analytics on grid and building data, predictive maintenance for substations, and forecasting for optimised grid use.
On-board inference for nanosatellites, so useful insight comes down instead of raw imagery that saturates the downlink.
Federated model training across institutions, keeping patient data where it is while improving shared diagnostic models.
Research & funded projects
We participate in European R&D as a technical partner that turns concepts into deployable systems. If you are assembling a consortium and need edge AI, MLOps or integration expertise, we would like to hear from you.
Propose a collaborationComputer vision and edge inference for early lameness detection in livestock, laying the groundwork for our animal-health monitoring capability.
Applying our intelligence-layer approach to industrial and agricultural machinery within the O-CEI Horizon programme.
Client software developed for the SPIRE project and published on our GitHub, reflecting our preference for open, reusable components.
Listed on the AI4Europe platform as a provider of data analysis, ML models and ML operations.
How we work
Fixed scope at each stage, so you decide with evidence rather than optimism.
We hear the operational problem, look at sample data and agree what success would look like in numbers.
45 minutes, freeWe assess data quality, edge constraints and integration points, and return a written go / no-go with an architecture sketch.
About 2 weeksA working model on one line, herd, robot or site, deployed on real hardware and measured against the agreed targets.
8–12 weeksRoll-out across sites with monitoring, retraining and handover documentation, so your team owns the system.
Ongoing, as neededFAQ
A scoped pilot on your own data typically runs 8 to 12 weeks: two weeks for a data and feasibility audit, then iterative model development and a field deployment on one line, herd, robot or site.
Usually not. We act as an intelligence layer on top of the cameras, PLCs, sensors and controllers you already run. When an edge device is needed we recommend commodity hardware such as NVIDIA Jetson or industrial x86 gateways.
Yes. Our pipelines run on your infrastructure or a private cloud, and for multi-site or sensitive data we use federated learning so raw data never leaves its source.
Yes. We have delivered cascade-funded and Horizon Europe work as a technical partner, contributing edge AI, MLOps and integration expertise, and we co-write proposals with partners.
We are based in Athens, Greece and work with clients and consortia across the European Union, on-site when needed and remotely by default.
Contact
Describe the problem in a few lines. An engineer, not a sales rep, replies within one business day with next steps or honest reasons why AI is not the right tool.