Modern manufacturers are under relentless pressure to increase throughput, reduce waste, and maintain stringent quality standards - all while contending with ageing equipment and a shrinking skilled workforce. Legacy systems that once served production well are now data silos, unable to communicate with newer line controllers, ERP platforms, or cloud analytics layers.

At the same time, regulatory requirements around energy consumption, workplace safety, and traceability are tightening across DACH and the broader EU. Manufacturers who fail to digitise risk falling behind competitors who can detect defects in real time, predict machine failures before they cause downtime, and optimise energy use across entire plants.

The path to Industry 4.0 is not a single technology purchase - it is a disciplined engineering programme that connects sensors, machines, and enterprise systems into one coherent intelligence layer. That is exactly what Tavlo builds.

Global supply chains have grown so complex that a single disruption - a delayed container, a mis-scanned pallet, a regulatory hold at customs - can cascade into days of lost revenue. Logistics operators are expected to deliver faster, cheaper, and with full traceability, yet many still rely on fragmented IT systems that cannot provide end-to-end visibility.

Warehouse operations face their own pressures: labour shortages, rising e-commerce volumes, and the need for error-free fulfilment at scale. Manual processes for inventory counting, route planning, and compliance documentation are no longer sustainable when throughput targets climb every quarter.

What the logistics sector needs is not another dashboard layered on top of disconnected data sources - it needs an intelligent integration backbone that fuses sensor data, enterprise systems, and AI models into a single operational picture. Tavlo provides exactly that.

Industrial robotics is moving beyond repetitive pick-and-place tasks into domains that demand real perception - recognising defects on a surface, picking irregularly shaped parts from a bin, or navigating safely alongside human workers. These capabilities require tight integration between vision systems, edge computing, and robot controllers, yet most robotics OEMs still ship with rigid, pre-programmed workflows.

Collaborative robots (cobots) add another layer of complexity: safety standards like ISO 15066 demand real-time awareness of the human environment, not just hard-coded exclusion zones. As robot fleets scale from a single cell to an entire facility, operators also need fleet-level health monitoring and predictive maintenance to avoid costly unplanned downtime.

Tavlo bridges the gap between robotic hardware and intelligent perception. We deliver the vision, ML, and edge-computing layers that transform industrial robots from blind actuators into context-aware autonomous systems.

The energy sector is undergoing the most significant transformation in a century. The shift from centralised fossil-fuel generation to distributed renewables, battery storage, and smart grids introduces operational complexity that legacy SCADA and metering systems were never designed to handle. Grid operators, utilities, and industrial energy consumers all face the same problem: too much data, too little intelligence.

At the same time, EU Green Deal reporting obligations demand auditable, real-time energy and emissions data across every facility. Organisations that cannot demonstrate measurable progress on energy efficiency and carbon reduction risk regulatory penalties and reputational damage.

Tavlo addresses both sides of this challenge - the operational and the regulatory. We connect legacy metering and control systems to modern analytics, deploy ML models for predictive maintenance on wind turbines and solar arrays, and build the reporting pipelines that turn raw energy data into boardroom-ready sustainability metrics.

Healthcare and MedTech organisations operate at the intersection of clinical urgency and regulatory rigour. Every process - from device manufacture to patient-facing diagnostics - must be traceable, validated, and audit-ready. Yet many health systems still depend on paper-based workflows, siloed clinical databases, and manual quality checks that cannot scale with growing patient volumes or tightening compliance requirements.

Medical device manufacturers face a parallel challenge: EU MDR and IVDR regulations demand comprehensive technical documentation, post-market surveillance, and validated production processes. The cost of non-compliance is not just financial - it can mean delayed product launches, market withdrawal, or patient harm.

Tavlo brings disciplined engineering to healthcare AI. We build solutions that respect the regulatory landscape - GxP, CE Marking, ISO 13485, HIPAA, GDPR - while delivering measurable improvements in clinical operations, documentation pipelines, and quality assurance.

Modern AI is only as valuable as its integration into the operational systems it's meant to improve. System Integration is our discipline of connecting ML models, computer vision pipelines, and IoT telemetry into MES, ERP, SCADA, and PLC infrastructure - turning insights into closed-loop action.

Without robust integration, even the most accurate AI model remains a science experiment. We bridge the gap between prediction and production, ensuring that every model output triggers the right downstream process - whether that's a quality hold in SAP, a PLC recipe change on the line, or a real-time dashboard update for the shift lead.

Our integration architecture is protocol-agnostic, event-driven, and built for the realities of brownfield industrial environments where decades-old equipment must coexist with modern cloud services.

From predictive maintenance on rotating machinery to demand forecasting in supply chains, Machine Learning and Anomaly Detection let you spot problems before they cost you money - and opportunities before competitors do. We build, train, and deploy production-grade models tuned to your industrial context.

Our ML practice covers the full lifecycle: data acquisition and labelling, feature engineering, model training and validation, deployment to cloud or edge, and continuous monitoring for model drift. Every model we ship comes with explainability tooling and performance baselines.

Anomaly detection is not just about flagging outliers - it is about understanding the normal operating envelope of your process and surfacing deviations that matter, filtering out the noise that does not.

Our Computer Vision pipelines take raw image and video input from industrial cameras and deliver production-grade outputs - defect detection, dimensional inspection, object tracking, and change detection - integrated directly with your MES or robotics systems.

We handle the full stack: camera selection and integration, lighting design, image acquisition, model training on your specific defect taxonomy, and deployment to edge or cloud - with closed-loop feedback to production control systems.

Whether you are inspecting automotive body panels at 60 frames per second or monitoring warehouse inventory from ceiling-mounted cameras, our vision systems are built for the throughput, accuracy, and reliability that industrial production demands.

Not every intelligent system can afford the latency of a round-trip to the cloud. IoT & Edge Computing is how we run inference at the point of action - retrofitting legacy equipment with modern sensors, running AI models on industrial gateways, and bridging protocols across decades of plant-floor technology.

We design edge architectures that are resilient, secure, and maintainable at scale - from a single pilot machine to a fleet of thousands across multiple sites. Every deployment includes OTA update capability, remote monitoring, and graceful degradation when connectivity drops.

Our edge practice is built on the reality that most industrial environments are brownfield: the equipment is old, the protocols are mixed, and the network is unreliable. We design for that world, not the greenfield fantasy.

Data Analytics & BI is the visualisation and reporting layer on top of our Core Platform - turning raw signals into executive-ready dashboards, KPIs, and reports. From shop-floor OEE tracking to energy consumption monitoring, we deliver analytics tuned to the audience.

We believe in analytics that drive action, not just awareness. Every dashboard we build is designed with a specific decision-maker in mind - shift leads need real-time OEE, plant managers need trend analysis, and executives need cross-site benchmarking. The same underlying data, surfaced differently for each role.

Our analytics layer integrates directly with the Core Platform pillars, consuming data from ML models, vision systems, IoT telemetry, and integration pipelines - so your dashboards always reflect what is actually happening, not what was happening an hour ago.

Our Shopfloor UI and HMI work is grounded in one principle: the person using the interface is wearing work gloves, dealing with noise, and has about two seconds to make a decision. We design for that reality - touch-first, high-contrast, mobile-ready, and tested on the factory floor.

We build operator interfaces that respect the context they are used in. That means large touch targets, minimal cognitive load, clear colour-coded status indicators, and offline capability for when the network drops - because on the shop floor, it always does eventually.

From traditional HMI panels on Ignition SCADA to progressive web apps on ruggedised tablets to AR overlays on HoloLens, we match the interface technology to the operator's environment and task.

Every industrial organisation sits on decades of technical documentation, process manuals, incident reports, and tribal knowledge. Our NLP and RAG (Retrieval-Augmented Generation) solutions make that knowledge searchable, queryable, and actionable - while preserving the compliance and audit trails European regulation demands.

We build AI knowledge systems that understand the specific terminology, acronyms, and context of your industry. A maintenance manual for a Siemens S7 PLC is not the same as a user guide for a consumer product - and our retrieval systems know the difference.

Every solution includes provenance tracking, citation of source documents, role-based access control, and full audit logging - because in regulated industries, knowing where an answer came from is as important as the answer itself.