AI co-pilot for robotic MIG/MAG welding. Real-time arc vision, audio analysis and voice coaching. Compresses 6 months of mentorship into 2 weeks — validated on RobotMeta installations.
Market Context
The skilled welder shortage was already structural. Mobilization, retirement and EU reshoring pushed it past the breaking point. Real-time AI vision finally made it possible to compress mentorship.
European Welding Federation projects a structural shortfall of 330,000 qualified welders by 2027 — driven by retirement, reshoring, and the energy transition. CEE manufacturing absorbs the gap first.
A single 5-day MIG/MAG course at Polski Instytut Spawalnictwa or SLV runs €1,500–2,500 plus travel and lost production. For a 12-person shop replacing two welders a year, that's €10K+ disappearing into off-site training that may not transfer to your specific robot.
Tier-2 fabricators in Poland, Romania and western Ukraine report 40% YoY growth in EU subcontract orders. The bottleneck is no longer demand — it's qualified operators standing next to a six-figure robot they can't yet program confidently.
How it works
Lucid Triton IMX490 HDR camera on the torch + BOYA M1 microphone + live SENOU controller telemetry. Six fused signals describe the weld pool, the arc and the machine state at 30 Hz.
6 streams · 30 Hz · synchronizedEdge AI inference (YOLOv8 on Jetson Orin) classifies arc state in real time: porosity risk, undercut, lack of fusion, torch angle, travel-speed deviation. Median latency 180 ms — faster than the operator's reflex.
edge inference · 180 ms p50Voice prompts in Ukrainian, Polish or English through a bone-conduction headset under the welding hood: "drop the angle 5° right" · "slow down 20%" · "stop — back-step and re-strike". Senior welder Human-in-the-Loop validates novel cases.
native UA/PL/EN · sub-second cueEvery successful weld auto-logs to a WPS record: parameters, joint geometry, operator, traceable serial. Cloud-side AWS SageMaker re-trains the defect model nightly. Audit-ready for ISO 3834 / EN 15085 acceptance.
WPS · ISO 3834 · EN 15085Solutions
Built for fabrication shops that already own a welding robot but can't find or keep experienced operators. Designed around RobotMeta cells, extensible to any MIG/MAG controller.
Real-time voice coaching during training shifts. Replaces the absent senior welder with a co-pilot that sees the arc, hears the bead, and corrects within 5 seconds — so a new hire stops scrapping plates by day 4.
Pilot price · €1,500/cell · 14-day deployEvery weld becomes an auditable record: parameters, joint, plate, operator, time-stamp. Generates the WPS book your EU customer's quality auditor wants to see — without a quality engineer spending 2 days per part.
ISO 3834 / EN 15085 · €390/monthAcoustic + visual signature detects porosity and lack-of-fusion before they happen. Stops the bead, saves the plate. Pilot data shows 73% defect-rate reduction inside one shift cycle.
From €590/cell/month · roadmap Q3 2026Record your senior welder's corrections during their last working months. The system learns the unwritten rules — torch dance for galvanized steel, when to skip a tack, the sound of a cold start. Knowledge stays in the shop after they leave.
One-time · €2,400 capture · grant-eligibleNative integration with SENOU controller. Reads Tech > Welding parameters, suggests Technology File presets, walks through teach-mode programming step-by-step on the first new part. Cuts robot-cell first-program time from 2 days to 4 hours.
Bundled with new RM cells · OEM partnershipFor OEMs and integrators: send a frame and audio chunk, receive arc state classification as JSON. Edge or cloud inference. Pay-per-call for systems integrators building their own MES dashboards.
Pay-per-call · REST API · Roadmap 2026Fig. 1 — Live arc + weld pool visualization, RobotMeta RM 1450/6 cell, Lviv-region pilot, week 2. Lucid Triton IMX490 HDR torch-mounted vision overlaid with audio-derived arc state. Coach prompt fired 3.1 s before predicted porosity event; operator corrected in real time.
Pilot Result
Operator hired November 2025, no prior robotic welding experience. Carbon-steel agro-frame subcontract for an EU Tier-2 buyer. ARCMENTOR kit on day 1; senior welder on call remotely.
Visible defects on cooled bead (porosity, undercut, spatter beyond spec) fell from 11.4% of welds in week 1 to 3.1% in week 2. Most-saved category: porosity in T-joints on galvanized stock.
Industry baseline for a complete novice on a robotic cell is 4–8 weeks of supervised training before a single weld leaves QA. Our pilot operator passed QA on a customer-facing part on calendar day 4.
From voice cue in the headset to corrective action on the torch — 5.2 s median, 92% acted-on rate. Above the 60% threshold we set as a kill-switch for the behavioral hypothesis.
Avoided scrap, avoided owner's hands-on time, avoided external course in Gliwice. Calculated at the shop's actual EU subcontract rate. Payback on the pilot fee inside one work cycle.
Four phases derived from the pilot. Each is a checkpoint you can walk away from if the result isn't there.
Camera on torch, microphone on shroud, bone-conduction headset under hood, edge box wired into SENOU controller's I/O. No production downtime — installed during a regular changeover.
Day 1 · 4 hours · zero downtimeTwo days of shadowing your existing master welder while they correct the trainee. Their judgments become the ground truth. The coach inherits their dialect, terminology and tolerances.
Day 2–3 · senior shadowing · IP retentionTrainee runs production parts. Voice coach corrects in real time. Senior welder remote-monitors three shifts then disengages. Action ratio and defect rate logged each shift.
Day 4–13 · production work · KPIs dailyAuto-generated WPS records for the run. Operator passes internal qualification on day 14. You decide: discontinue the kit, scale to next cell, or upgrade to continuous mode.
Day 14 · ISO 3834 dossier · go/no-go decision"What worked wasn't the AI. What worked was that a 23-year-old who had never touched a torch before could put on a headset and hear, in his own dialect, the same correction our retired master would have shouted from across the bay. The arc didn't change. The mentor finally scaled."
— Owner, 18-person fabrication shop, Lviv region · February 2026Pilot Data
Every metric below comes from logged frames and prompt-response pairs in a working shop — not synthetic data, not a simulator.
An 18-person shop, RM 1450/6 cell, novice operator with no prior arc time. Day 1 defect rate matched the industry baseline for a green hire. By day 10, the operator was clearing carbon-steel T-joints below the shop's veteran threshold of 4%. The drop is coach-driven, not maturation: prompts disabled on day 6 spiked defects back up; re-enabled, they fell again.
The single hypothesis that could kill this whole product: would a stressed novice in a hood actually obey a voice in his ear? Day 1 acted-on rate was 71% — already above the 60% threshold we'd set as the abort line. By day 10 it was 92%, and the operator was unprompted-asking the system to repeat itself when missed. Median time-to-act dropped from 8.4 s to 3.2 s as familiarity built.
"He stopped flinching at the prompts around day three. By day seven he was finishing the sentence in his head before the headset said it. That's the moment we knew we'd captured something useful — not the AI, the master welder we'd already lost."
— Pilot retrospective · Lviv region · 2026
Porosity is the cleanest win for acoustic detection — the arc's hiss-to-crackle transition leads the visible bubble formation by a measurable margin. Vision-only would have flagged 65% of the porosity events; vision + audio caught 94%. The implication for the business: every shop running galvanized stock is structurally underserved by current visual-only weld-monitoring rigs, because the dirtiest steels are the loudest.
Transparent Pricing
No annual lock-in. The 14-day pilot is the audition. First 5 pilot shops get 50% off in exchange for full data access and a public case study.
Hardware fitted, senior-welder-on-call, daily KPI report. One operator, one cell, two weeks. Walk away if defect rate doesn't fall.
Continuous coach + WPS auto-documentation across one cell. Monthly billing, 30-day cancel. Supports up to 4 operators rotating on the cell.
For multi-cell shops or OEM integrators. Includes API access, MES dashboard, custom defect-class training, and quarterly on-site senior-welder calibration.
Live Demo
Ask the agent to analyze a weld parameter set, suggest a coach prompt, or draft a WPS for a sample joint.
Get In Touch
Have a robotic welding cell with a training bottleneck? Let's talk about a 14-day pilot in your shop.
First 5 pilot shops receive 50% off in exchange for full data access and a public case study.
Built on validated industrial practice · Partners & references
Agentic AI · Architecture
A supervisor agent orchestrates specialized worker agents that fuse vision (Lucid Triton IMX490 HDR), audio (BOYA M1), controller telemetry (SENOU / Siemens S7-1200 G2), and historical WPS records. Edge inference handles real-time coaching; AWS SageMaker handles nightly retraining; Human-in-the-Loop senior welders confirm novel cases before the model promotes them to production.
Compliance Framework
Tiered responsibility for welding quality assurance — from the operator at the cell, through the shop's IWE/IWS, up to EU directives. ARCMENTOR is the audit trail that connects them.