Do you need SLAM or autonomous navigation for your robots?

EXPERANOS builds ROS 2 SLAM and navigation stacks (LiDAR and visual SLAM, Nav2) for autonomous mobile robots operating in GPS-denied environments such as warehouses, factories and tunnels, validated in simulation before deployment and delivered with fleet monitoring.

Is manual quality inspection too slow or your defect escape rate too high?

EXPERANOS deploys edge vision quality-inspection systems: camera and lighting design, defect-detection models trained with limited labelled data, TensorRT-optimised inference on NVIDIA Jetson at the line, and MES integration so operators act in real time.

Do you need to train AI on data that cannot leave its owner?

EXPERANOS implements cross-institution federated learning with differential privacy and, where required, homomorphic encryption or zero-knowledge proofs, so hospitals, banks and factories obtain a joint model while every record stays where it is, GDPR-compliant by construction.

Is your team drowning in documents and institutional knowledge?

EXPERANOS builds sovereign retrieval-augmented generation (RAG) assistants over your documents: open-weight LLMs fine-tuned for your domain and language, deployed on-premises with citation-grounded answers, audit logging and zero data egress.

Are energy costs unpredictable across your buildings or plants?

EXPERANOS delivers energy forecasting and decision-support systems: consumption and generation forecasts, anomaly detection, load-shifting recommendations and what-if simulation, integrated with your metering and building-management systems.

Do you want robotic automation without integration risk?

EXPERANOS designs robotic cells simulation-first: the complete cell is built and stress-tested in Gazebo or Isaac Sim, vision-guided picking and placement are validated virtually, and only then is hardware procured and commissioned, cutting risk and downtime.

Is your AI model too slow or too large for your device?

EXPERANOS compresses models for the edge: quantisation-aware training, structured pruning and distillation, with measured accuracy/latency/energy trade-off curves, from Jetson-class devices down to microcontrollers (TinyML).

Challenges & Solutions | SLAM, Quality Inspection, Federated Learning, Digital Heritage, RAG

Q: Do you need to comply with the EU AI Act?

EXPERANOS provides AI Act compliance engineering: risk classification, technical documentation, model cards, robustness and bias testing, human-oversight design, logging and post-market monitoring, delivered as an auditable evidence package.

Q: Is unplanned downtime disrupting your operations?

EXPERANOS builds predictive-maintenance systems and digital twins that fuse PLC/SCADA, IoT and vision data to forecast failures with uncertainty estimates, prioritise interventions and cut unplanned downtime.

Q: Do you need to digitise cultural heritage in 3D?

EXPERANOS digitises artefacts, monuments and sites into research-grade 3D models using photogrammetry and neural reconstruction (NeRF / Gaussian splatting), delivers open-standard archives (glTF, E57, Europeana-ready metadata), and builds AR/VR experiences and interactive web viewers on top.

Q: Do you need early warning for wildfires or environmental hazards?

EXPERANOS builds multi-source early-warning systems fusing camera towers, aerial patrols and satellite feeds with edge AI detection, tuned for Mediterranean conditions, with alert fusion and civil-protection dashboards.

Robotics & autonomy

Machines that find their own way

LIVE SLAM · LIDAR MAPPING · RENDERED IN YOUR BROWSER

Challenge 01

Do you need SLAM or autonomous navigation?

Your robots, AGVs or inspection platforms must localise and navigate where GPS doesn't reach: warehouses, factories, basements, tunnels, ships. Off-the-shelf autonomy rarely survives contact with your actual floor.

LiDAR and visual SLAM tuned to your environment (ROS 2, Nav2)
Navigation with dynamic-obstacle avoidance around people and forklifts
Multi-robot fleet coordination and traffic management
Validated in simulation on a digital model of your site before day one
Delivered with fleet monitoring and a maintenance handbook

ROS 2SLAMNav2GPS-denied

REAL FOOTAGE · HUGGING FACE LEROBOT Real robot arms performing learned manipulation tasks

Challenge 02

Do you want robot automation without integration risk?

You know a robotic cell would pay off, but you've heard the horror stories: months of commissioning, production stoppages, integrators who disappear.

The entire cell built and stress-tested in simulation first (Gazebo / Isaac Sim)
Vision-guided picking, placement and machine tending validated virtually
Hardware procured only after the simulated cell hits your cycle-time target
Sim2Real transfer with measured performance deltas, not promises
Operator training on the digital twin before go-live

Simulation-firstMoveIt 2Vision-guided picking

Vision & inspection

Eyes that never blink

REAL FOOTAGE · ULTRALYTICS YOLO · CONVEYOR COUNTING Real footage: packages detected and counted on a conveyor belt

Challenge 03

Is manual quality inspection too slow, or defects escaping?

Human inspectors tire, drift and can't keep line speed. Every escaped defect costs rework, recalls or reputation.

Camera and lighting design for your specific surfaces and defects
Detection models trained even with limited labelled examples
TensorRT-optimised edge inference at line speed (NVIDIA Jetson)
MES/ERP integration: findings become actions, not screenshots
KPIs contractually framed: precision, recall, false-reject rate, latency

Defect detectionEdge AIZero-defect

MEDITERRANEAN WILDFIRE CONDITIONS Wildfire burning across a hillside at night

Challenge 04

Do you need early warning for wildfires or environmental hazards?

Minutes decide whether a wildfire is an incident or a catastrophe. Patrols and emergency calls alone find fires too late.

Camera towers, aerial patrols and satellite feeds fused into one picture
Edge AI smoke and fire detection tuned for Mediterranean conditions
Alert fusion that suppresses false alarms instead of drowning operators
Civil-protection dashboards with dispatch-ready information
Designed and tested with the conditions we know first-hand from Greece

Early warningEarth observationDisaster resilience

Data, privacy & trust

Intelligence without exposure

FEDERATED TOPOLOGY · DATA NEVER MOVES

Challenge 05

Do you need to train AI on data that cannot move?

Hospitals, banks, factories: the joint model would be valuable, but legal, competitive or regulatory walls keep each dataset locked in place.

Cross-institution federated learning: the model travels, data never does
Differential privacy on updates, with a written DPIA answer for every DPO
Homomorphic encryption or ZK proofs where verifiability is demanded
Per-site evaluation so no partner is quietly worse off
GDPR-compliant by construction, not by disclaimer

Federated learningFHE / ZKMLGDPR by design

COMPLIANCE PIPELINE · EVIDENCE BY DESIGN

Challenge 06

Do you need to comply with the EU AI Act?

Your AI system may be "high-risk" under Annex III, and the obligations are engineering work, not paperwork: logging, oversight, robustness, documentation.

Risk classification and gap analysis against the Act's requirements
Technical documentation, model cards and datasheets that survive audit
Bias, robustness and adversarial testing with reproducible reports
Human-oversight design and event logging built into the system
Post-market monitoring plan wired to real drift detection

EU AI ActModel cardsXAI

Free download: AI Act readiness checklist (PDF)

REAL FOOTAGE · HEAVY SEGMENTATION MODELS, MADE EDGE-SIZED Real footage: instance segmentation masks over moving objects

Challenge 07

Is your model too slow or too large for your device?

The model works in the cloud, but your product needs it on a device with a fraction of the compute, power and memory.

Quantisation-aware training (INT8 and below) with accuracy recovery
Structured pruning and knowledge distillation
Measured accuracy / latency / energy trade-off curves, not guesses
Targets from NVIDIA Jetson down to microcontrollers (TinyML)

QuantisationTinyMLTensorRT

GENERATIVE STACK · SIMULATION + DIFFUSION Illustration of the EXPERANOS generative AI stack producing labelled data

Challenge 08

Do you lack enough labelled data to train a reliable model?

The failure cases you care about are exactly the ones you have three photos of. Collecting and labelling more is slow, expensive, sometimes impossible.

Synthetic data from simulation: domain-randomised, auto-labelled renders
Generative augmentation (diffusion) for rare classes and edge cases
Active-learning loops so humans label only what matters
Honest validation on real held-out data, always

Synthetic dataDomain randomisationActive learning

Knowledge & operations

Decisions at the speed of your data

SOVEREIGN RAG · ANSWERS WITH CITATIONS

Challenge 09

Is your team drowning in documents and tribal knowledge?

Contracts, manuals, case files, regulations: the answer exists somewhere in your archive, and finding it costs your experts hours every day.

Sovereign RAG assistant over your corpus, answers with citations
Open-weight LLMs fine-tuned for your domain and language (incl. Greek)
On-premises or EU-cloud deployment: zero data egress
Audit logging and role-based access from day one
Evaluation harness proving groundedness before rollout

Sovereign RAGFine-tuned LLMOn-prem

ASSET HEALTH · PREDICT BEFORE IT BREAKS Power transmission infrastructure at sunset

Challenge 10

Is unplanned downtime disrupting your operations?

Every surprise failure costs production, penalties and weekend call-outs, while planned maintenance replaces parts that were still fine.

Sensor fusion from PLC/SCADA, IoT and vision into one asset picture
Failure forecasting with honest uncertainty estimates
Digital twin for what-if planning of interventions
Prioritised work orders into your CMMS, not another dashboard to ignore
KPI: unplanned-downtime reduction, measured against your baseline

Predictive maintenanceDigital twinCMMS

FORECAST · OPTIMISE · SHIFT LOAD Solar panel installation under a bright sky

Challenge 11

Are energy costs unpredictable across your sites?

Volatile prices meet volatile consumption, and nobody can say which building, line or hour is burning the budget.

Consumption and generation forecasting per site, line and hour
Anomaly detection that catches waste as it starts
Load-shifting recommendations against tariff windows
Decision-support dashboards your operations team actually uses
Integration with metering and building-management systems

ForecastingDSSEnergy optimisation

Something else?

Your problem isn't on this page?

Most challenges we're brought are variations of these twelve, but not all. Describe yours in a paragraph; we'll answer within two working days with an honest assessment: what's feasible, what it takes, and whether we're the right partner. If we're not, we'll say so.

Describe your challenge

Digital heritage · Cluster 2

Do you need to digitise cultural heritage in 3D?

Museums, ephorates, archives and municipalities sit on collections the world should see and scholarship needs preserved. We turn artefacts, monuments and sites into research-grade digital twins, and into experiences people can visit from anywhere.

Photogrammetry and neural 3D reconstruction (NeRF, Gaussian splatting) of artefacts, buildings and excavation sites
Research-grade archives in open standards: glTF, E57, IIIF, Europeana-ready metadata (EDM)
Interactive web viewers, like the one on the right, that run in any browser, no app required
AR on-site storytelling and VR reconstructions of lost or inaccessible spaces
AI enrichment: automatic classification, damage mapping and condition monitoring over time
Fit for Horizon Europe Cluster 2, Creative Europe and national digitisation programmes

PhotogrammetryGaussian splatting glTF / E57 / IIIFEuropeana AR / VR

Based in Athens, we work a stone's throw from some of the world's most demanding heritage, and treat every collection with the same respect.

Start from your problem. We'll bring the system.