Your factory runs itself. Your machines get smarter with every shift. From the first sensor signal to full factory autonomy — in days, not months.
Your machines already generate the data. GET Impact turns it into decisions — predicting failures, trimming energy, flagging quality issues the moment they appear.
Every machine you've shipped holds untapped value. GET Config packages your engineering knowledge into deployable AI — recurring revenue from every unit, past and future.
From first sensor signal to full factory autonomy. In days, not months. No rip-and-replace. No production disruption. Start where you are.
5-question interactive quiz that recommends your right starting product — GET Connect, Impact, or Config.
Interactive walkthrough showing how LETHE, ARGOS, TRACEMEM, ATHENA, KAIROS, HERMES, and VRE work together.
Imagine a factory where machines predict their own failures, energy costs optimise around your production schedule, and quality issues surface before a single defective part leaves the line.
GET Connect alongside your existing infrastructure. No production stops, no vendor lock-in.
GET Impact detects hidden process states, predicts failures, and intervenes before you need to.
Autonomous agents execute, adjust, order, and verify — 24 hours a day, without human intervention.
Connects every sensor, PLC, and energy meter on your floor — regardless of vendor or age — into a single, clean, real-time data feed. The foundation for everything that follows.
Turns your data into decisions. Predictive asset health, live OEE, autonomous energy optimisation, full traceability, and AI agents that act on your behalf — around the clock.
Imagine every machine you've ever shipped becoming smarter with every production cycle. Your engineering knowledge — accumulated over decades — deployed at scale, generating measurable value for customers you installed years ago.
Real-time visibility across every customer installation. Failure patterns appear across the fleet before any individual customer notices.
Your engineering knowledge becomes a predictive service your customers subscribe to. Cold start solved from day one.
Package your expertise as autonomous AI agents deployable across your entire customer base simultaneously. One configuration. Infinite scale.
Bridges every machine in your installed base to the intelligence layer — regardless of protocol, vendor, or vintage. One unified data stream across your entire fleet.
Packages your engineering knowledge into deployable AI services. Your expertise, delivered as a product your customers subscribe to.
The digital service your customers experience — predictive maintenance, energy insight, full traceability — powered by your knowledge and running under your brand.
Every product, every module, every framework — explored in full detail.
Every signal from every machine — captured, standardised, and delivered before anything else can happen. Before AI can think, it needs to hear.
Factory floors are a Tower of Babel. Dozens of vendors, protocols, and generations of equipment — all speaking different languages. Every digitalisation project hits the same wall: getting clean, reliable data out of existing assets.
GET Connect speaks every industrial dialect natively. It connects to any PLC, sensor, energy meter, then normalises all signals into a unified data model — at the edge, before anything leaves the facility.
| Capability | What it means in practice |
|---|---|
| Universal protocol support | OPC-UA, Modbus TCP/RTU, PROFINET, EtherNet/IP, MQTT, REST — any vendor, any generation. |
| Hardware-agnostic deployment | Runs containerised on any industrial gateway or edge server. Your existing infrastructure works. |
| Asset-level energy metering | Real power consumption per machine, per cycle — the foundation for product-level carbon calculation. |
| ERP integration | Bidirectional work order and production data exchange with SAP, Oracle, and custom systems. |
| Offline resilience | Buffers locally during connectivity interruptions and transmits queued records on reconnect. |
| Multi-factory management | One deployment covers multiple sites — consistent data, intelligence, and control. |
Traditional PLCs are frozen in time. They cannot consume AI outputs, cannot be reconfigured without hardware changes, and create a hard wall between cloud intelligence and physical action.
PLCaaS virtualises control logic in software. AI decisions made in the cloud return to the edge as executable control signals — closing the loop without new hardware.
| Capability | What it means in practice |
|---|---|
| Software PLC | Control logic on standard edge hardware. Reconfigurable by software — no engineer on-site required. |
| AI feedback loops | Cloud AI outputs return to the machine as real control signals. The loop is closed. |
| Automatic recipe push | Production recipes pushed to machines automatically on work order triggers. |
| Authorised remote control | State and parameter changes executed remotely within pre-defined safety boundaries. |
| Closed-loop AI control | AI inference in the cloud, actuation at the edge, within the same control cycle. |
Where data becomes intelligence — and intelligence becomes action. Six tightly integrated modules cover every dimension of factory performance.
Standard monitoring watches what sensors can measure directly. But the variables that actually determine machine health — tool wear, bearing degradation — are unseen. Factories replace tooling too early, or catch failures too late.
ARGOS reconstructs hidden machine states from the signals you can measure — separating genuine degradation from batch changes, ambient shifts, and operator variation. Quantified health scores, not binary alarms.
| Capability | What it means in practice |
|---|---|
| Remaining Useful Life | Time-to-failure estimate with 95% confidence interval, aligned to planned maintenance windows. |
| Smart anomaly detection | Fires on genuine degradation, not batch changes or sensor noise. Fewer false alarms. |
| Confound isolation | Switches from statistical estimation to exact regression when material batch data is available. |
| Cycle quality scoring | Real-time sensor curve comparison against reference patterns. Stop/Continue decision per cycle. |
| Named failure signatures | Alerts reference known historical patterns — not generic statistical warnings. |
| KAIROS integration | Health scores feed directly into maintenance and energy scheduling — aligned to planned downtime. |
Production data lives in silos — SCADA exports, MES records, spreadsheets, and paper logs that never talk in real time. OEE is calculated at end of shift. Work orders appear in spreadsheets, not on operator screens.
A single unified operations layer. Work orders from ERP automatically. Operators report from any browser. OEE calculates live. Every stop, defect, and quality event captured in real time.
| Capability | What it means in practice |
|---|---|
| Work order management | Create manually, upload via Excel, or receive from ERP via API. All methods work simultaneously. |
| Operator MES screen | Touch-optimised, browser-based. Active work order, targets, cycle count, QC status at a glance. |
| Live OEE | Availability × Performance × Quality in real time — at machine, line, and factory level. |
| Andon display | Shop floor boards show cycle rate vs. target, counts vs. goal, and remaining time. |
| Defect tracking | Configurable defect library. First-pass yield and QC rate tracked per work order. |
| Asset digital twin | 3D asset view with live IoT data, ARGOS health scores, and full maintenance history. |
| Automated reporting | Production, stop, breakdown, waste, defect reports. Configurable and auto-delivered. |
Maintenance teams plan without energy data. Energy managers plan without maintenance schedules. The perfect alignment of low tariff, free technician, and planned stop is invisible — every week.
KAIROS sees everything at once: ARGOS health scores, live energy prices, production schedules, technician availability — combined into a single optimised plan using PDDL-based planning.
| Capability | What it means in practice |
|---|---|
| Real-time energy monitoring | Simultaneous consumption across all assets — by machine, line, and factory — always live. |
| Cycle-level carbon tracking | Exact kWh per cycle × live grid carbon intensity. Auditor-grade, not modelled. |
| Intelligent task scheduling | Maintenance, energy-intensive jobs, and production windows optimised together in one cost function. |
| Energy anomaly detection | AI flags abnormal consumption patterns before they become bills. |
| Peak shaving | Energy-intensive jobs rescheduled away from peak tariff periods automatically. |
| Renewable integration | Production scheduling maximises self-consumption from on-site solar or storage. |
| CSRD-ready reports | Scope 1 and 2 emissions per production unit, work order, and factory. Compliance as a by-product. |
A warranty claim arrives. The root cause lives in a supplier's process data from three weeks ago. Finding it takes days — often inconclusive. Regulators are demanding product-level carbon footprints nobody has measured.
TRACEMEM generates an immutable Digital Thread at the moment of production. Every unit linked to its exact process conditions, material batch, tooling health, energy consumed. Root cause in under 60 seconds.
| Capability | What it means in practice |
|---|---|
| Digital Thread | Four layers: upstream material lineage, internal logistics, process physics, field evidence — unified per unit. |
| Causal Spine | PELT change-point detection labels shifts with batch IDs. Root cause in seconds, not days. |
| Quality Birth Certificate | Predictive quality score during production (Phase 1), then cryptographically verified proof on completion (Phase 2). |
| Digital Product Passport | EU ESPR-aligned records generated automatically at point of production. |
| Cycle-level carbon footprint | Measurement-based kWh per cycle × real-time grid carbon intensity. Not estimated — measured. |
| Cross-boundary federation | Supplier records queryable against manufacturer warranty data — full data sovereignty preserved. |
| Knowledge Atoms | Successful operator adjustments captured as structured insight. Every correction makes ARGOS smarter. |
Every action is digitally signed at the edge using Ed25519, creating a tamper-proof audit trail from sensor to verified result. When outcomes are confirmed, VRE mints ERC-20 tokens on the Polygon blockchain — aligning financial incentives directly with operational excellence.
Better AI creates a new bottleneck: someone still has to decide what to do, coordinate the response, verify the outcome, and log what happened. Smarter alerts, same operational bottleneck.
HERMES translates a plain-language operational goal into coordinated AI agent deployments. Detection → decision → action → verified outcome — without a human coordinator for routine interventions.
| Capability | What it means in practice |
|---|---|
| Natural language interface | Talk to your factory in plain language. Ask questions, set goals, get structured answers. |
| Documentation Q&A | Any question answered from OEM manuals via semantic search. Answers in seconds. |
| Live telemetry reports | Descriptive statistics and visualisations from live operational data — on demand. |
| Voice reporting | Operators report events by speaking — structured data captured without stopping work. |
| OR-01 Cortex | Autonomous agent runtime: goal → context assembly → execution → recommendation → outcome logging. |
| OR-02 Delivery Gateway | Multi-channel: MES, Mobile, Andon, Email, SCADA, HMI. Three-tier urgency-based approval workflow. |
| BYOA Marketplace | OEMs publish specialist agents to a governed registry. Factories subscribe and deploy instantly. |
Most industrial AI is frozen on the day it goes live. The factory changes — new materials, new operators, new conditions — but the models do not. Intelligence that quietly degrades.
ATHENA is a self-improving knowledge graph that updates continuously — but only from VRE-verified outcomes. Not predictions. Not assumptions. Only what actually happened, cryptographically confirmed.
Raw observations become verified patterns, which become semantic rules — but only when VRE confirms the underlying outcome actually occurred. Nothing untested reaches the top tier.
Your engineering knowledge, packaged as AI. Recurring revenue from every machine ever shipped. A living platform that knows your machines as well as your best engineer.
Your best engineers know things no manual has ever captured. When a customer's machine fails at 2am, that knowledge is inaccessible. When experienced engineers retire, decades of expertise walk out the door.
GET Config structures your engineering knowledge into a governed, searchable, reasoning-capable knowledge base. Your expertise becomes a platform. Your customers get OEM-grade answers instantly, 24/7.
| Capability | What it means in practice |
|---|---|
| Fast document ingestion | Upload any PDF — immediately available for conversational queries via semantic search. |
| Controlled knowledge extraction | LLM-assisted extraction with human review. Every fact carries full provenance. |
| Domain instantiation | 8-question KP-02 process converts engineer input into a scoped, versioned knowledge object. |
| Knowledge hierarchy | Mode → Asset → Line → Factory → OEM baseline. Most specific knowledge always wins. |
| VRE learning loop | Every verified field outcome triggers knowledge re-validation. The manual updates when field evidence contradicts it. |
| Cross-fleet learning | Health models validated across all deployments. Fleet-wide intelligence feeding every individual machine. |
Conversational AI backed by your complete engineering knowledge base, including voice interface. Customer operators ask maintenance questions in plain language and receive OEM-grade answers instantly. Your brand. Your knowledge. Always on.
OEMs find out about field failures when customers call. Service visits run on fixed intervals, not machine condition. Systematic failure patterns across your installed base are invisible.
Real-time health visibility across every customer installation. ARGOS runs continuously on every connected machine. Failure patterns emerge at fleet level before any individual customer notices.
| Capability | What it means in practice |
|---|---|
| Fleet health dashboard | Real-time asset health, production status, and alerts across all installations — one screen. |
| Predictive field service | Know which machines need attention within a defined horizon — reach out before failure. |
| Fleet performance analytics | Systematic failure patterns visible at fleet scale. Issues invisible from a single machine. |
| Remote maintenance support | Shared sessions with live data, ARGOS outputs, and documentation. No site visit needed. |
| Servitisation management | SLA tracking, service records, customer comms — the backbone for subscription-based service. |
| White-label deployment | Every screen and notification carries your brand. The GET engine is invisible to your customer. |
Purpose-built AI for industrial environments. Not adapted from generic platforms. Designed for the factory floor from the ground up.
Most factories send everything to the cloud and hope the network keeps up. LETHE does the thinking before data leaves the building. It sits on the gateway — between your machines and the internet — and figures out in real time what actually matters. Noise gets dropped. Relevant signals get through. The result: 80% less data travelling to the cloud, but nothing important is ever lost. When something unusual happens on the production line, LETHE reacts in milliseconds — no waiting for a round-trip to the server. Validated on a live 5G network in the EU Horizon PULSE-5G trial: 400 sensors running simultaneously, zero packet loss, under one second end-to-end.
Your sensors measure temperature, pressure, speed — but not whether the tool is wearing out inside, or whether the bearing is two weeks from failing. ARGOS reads the signals your sensors can measure and works backwards to figure out the hidden ones. It's the difference between watching a speedometer and understanding the health of the engine. It also separates real problems from false alarms: if a different material batch is causing a temperature spike, ARGOS knows it's the batch — not a failing machine. It starts giving useful answers on day one, using your OEM documentation as a starting point. Over time, as it sees more data and confirmed repair outcomes, its predictions get tighter. Eventually it even learns the unwritten rules your best engineers carry in their heads.
When something goes wrong — a warranty claim, a quality failure, a rejected batch — someone has to figure out why. That investigation normally takes days and often ends without a clear answer. TRACEMEM makes it take seconds. It records exactly what happened at the moment each unit was produced: which material batch was used, what the machine was doing, who was operating it, how much energy it consumed. When a problem surfaces later, you can trace it back to the precise second it started — and often to the exact supplier delivery that caused it. It also calculates the real carbon footprint of every unit produced, measured from actual machine data rather than estimates. This is what regulators are starting to require, and it's generated automatically as a by-product of normal production.
Most AI systems are frozen the day they go live. The factory changes — new materials, new operators, different conditions — but the AI doesn't. It quietly starts making mistakes until someone notices. ATHENA is different because it only learns from things that have been proven to actually happen. When a recommendation is made, acted on, and the outcome is confirmed, that becomes new knowledge. When something doesn't work, that gets recorded too. It also holds all the structured knowledge about your machines — from OEM manuals, from your engineers' experience, from historical repairs — organised so the most relevant version is always used first. A factory's own field experience automatically takes priority over the manufacturer's default settings. No one has to update it manually.
Maintenance teams and energy managers usually work from different information and make separate plans. The result: a perfectly good repair window gets missed because no one connected the low-tariff energy period to the available technician to the machine that needed attention that night. KAIROS sees all of it at once. It takes machine health scores, the energy prices for the next few days, your technician schedule, and your production plan — and finds the moment when it makes the most sense to act. Not just for maintenance. Not just for energy. Both together, in one plan.
Knowing that something needs to happen and actually making it happen are two different problems. Most AI tools solve the first one and leave the second to you. HERMES closes that gap. It takes a goal — stated in plain language by a plant manager or operations director — and turns it into coordinated actions across your machines and teams. A recommendation doesn't just appear in someone's inbox. It gets routed to the right person, in the right system, with the right level of urgency. High-priority actions wait for a supervisor to approve. Routine ones go straight through. When operators respond — approve, reject, or add a note — that feedback goes back into the system and improves future decisions.
An AI recommendation is only useful if you can prove it worked. VRE is how GET closes that loop. After a recommendation is acted on, VRE watches what actually happens on the machine — not what was predicted, but what was measured. Did the energy consumption actually drop? Did the quality actually improve? If yes, and the production conditions stayed stable enough to trust the result, VRE creates a cryptographic proof of that outcome. This feeds back into the system, making future predictions more accurate. It also creates an auditable record for sustainability reporting. And — optionally — it mints tokens that reward operators for acting on AI recommendations, creating a direct financial incentive for behaviours that save energy and reduce waste.
Your data stays yours. Every deployment is production-ready from day one.
The fastest path from contract to live factory intelligence. We manage the infrastructure. You own the data.
| Cloud infrastructure | GET-managed AWS, hosted in Frankfurt. European data residency, configurable per requirement. |
| Data encryption | Encrypted at rest (DynamoDB) and in transit (TLS 1.3). End-to-end, always. |
| Authentication & access | OAuth 2.0 / AWS Cognito with MFA. RBAC + IBAC access control. |
| Edge-to-cloud security | TLS certificates govern every gateway connection. No unencrypted path. |
| Compliance | SOC, PCI DSS, HIPAA, and ISO certified cloud infrastructure. |
For organisations with strict data sovereignty requirements. Same architecture, same security — deployed inside your cloud environment.
GET Connect runs containerised on any industrial-grade gateway. No proprietary hardware lock-in.
| Supported hardware | Any industrial-grade gateway (Advantech or equivalent). Hardware-agnostic by design. |
| Deployment method | Containerised via Docker. Deployable in hours, not weeks. |
| Minimum requirements | 4 CPU cores · 8 GB RAM · 64 GB storage (recommended). |
| AI inference split | Cloud-side for high-complexity models. Edge-side for latency-critical loops. Validated over 5G. |
Not a demo. Not a proposal. A conversation about where you are today — and where the right entry point begins.
You don't need to be ready for full autonomy. You need to know whether connectivity, predictive intelligence, or autonomous agents is the right first step for where you are today.
Your engineering expertise already exists. GET Config packages it, deploys it at scale, and generates recurring revenue from every machine you've ever shipped.