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Resonae Labs — Advanced engineering

Engineering intelligent manufacturing.

Mechanical engineering · Industrial robotics · Artificial intelligence · Digital twin · Machine vision · Factory automation · Physical AI · Manufacturing intelligence

Sheet
RL-000
Discipline
Multi
Scale
Concept → Commissioning
Rev
2026.1
0+
Systems delivered
0+
Industries served
0%
Engineering owned
0/7
Production support
INFEED CONVEYOR OUTFEED / PALLET VISION STATION R1200 SAFETY FENCE — SIL 2 6200
FIG. 01 — Robot cell, plan viewSHEET RL-101 · REV C

Who we are

We build the machines that build everything else.

Resonae Labs designs and develops intelligent automation systems that combine robotics, artificial intelligence, advanced mechanical engineering, digital twins and industrial software.

We transform conventional factories into adaptive, autonomous, data-driven manufacturing environments — and we own the whole chain. The same team that sizes the servo writes the vision inference loop and stands on the line at 3 a.m. during commissioning.

That single-thread ownership is why our systems reach rate faster. There is no handoff between the mechanical house, the controls house and the AI vendor, because there is only one house.

Capabilities

Eight disciplines, one engineering thread.

Most plants buy these from six different suppliers and spend the project reconciling them. We deliver them as one system with one owner.

  • 01Machine design & special purpose machinesSolidWorks · Creo · CATIA
  • 02Robot cell design & offline programmingRobotStudio · Roboguide · ROS 2
  • 03PLC, motion & safety programmingTIA · Studio 5000 · TwinCAT
  • 04Electrical design & panel engineeringEPLAN · ISO 13849 PL d
  • 05Embedded systems & edge computeJetson · STM32 · Linux RT
  • 06Machine vision & metrologyHalcon · OpenCV · deep learning
  • 07Digital twin & virtual commissioningOmniverse · Isaac Sim · OpenUSD
  • 08Industrial AI & analyticsTime-series · VLM · agentic copilots
  • 09Autonomous mobile robots & fleetAMR · AGV · fleet orchestration
  • 10Humanoid & general-purpose roboticsTeleoperation · imitation learning
  • 11Industrial IoT & cloud integrationOPC UA · MQTT · Azure · AWS
  • 12Predictive maintenanceVibration · thermal · current signature
  • 13Virtual commissioningPLC-in-the-loop emulation
  • 14Industrial R&D on contractFeasibility → prototype → production

Discipline 01 — Mechanical

Drawings that survive the shop floor.

Machine design, special purpose machines, fixtures, jigs, end effectors and robot grippers — modelled, analysed and toleranced so that what is manufactured matches what was simulated.

Every structural assembly is checked with finite element analysis for stiffness, fatigue and dynamic response before release. Every fixture carries a tolerance stack against the part datum scheme, not against the nominal CAD.

Machine DesignSpecial Purpose MachinesFixture DesignJigsEnd EffectorsRobot GrippersFEADesign OptimisationPrototype DevelopmentSolidWorksCATIAInventorCreoANSYS
1234 ISO 9409-1-50-4-M6 FLANGE SERVO DRIVE + ENCODER LINEAR RAIL, PRELOADED JAW, HARDENED 58 HRC MAT: AL 7075-T6 / TOL ±0.02 / MASS 1.84 kg
FIG. 02 — Servo gripper, explodedSHEET RL-214 · REV B

Discipline 02 — Robotics

Platform-independent robot integration.

We are not a reseller for one brand. We select the arm that fits the payload, reach, cycle and service reality of your plant — then take responsibility for it.

Platforms

ABBFANUCKUKAYaskawaOmronStäubliUniversal RobotsDobotTechmanUnitreeKassowDoosan

Safety & standards

Risk assessment to ISO 12100, functional safety to ISO 13849-1 PL d, collaborative applications validated against ISO/TS 15066 with force and pressure measurement — documented, not assumed.

Applications

  • 01Arc & spot weldingseam tracking, weave, TCP calibration
  • 02Assembly & fasteningforce control, torque verification
  • 03Dispensing & sealingbead inspection, flow compensation
  • 04Inspectionin-cell vision and metrology
  • 05Material handlingmachine tending, part transfer
  • 06Packaging & palletisingmixed-SKU pattern generation
  • 07Bin picking3D vision, collision-free grasping
  • 08Collaborative operationsspeed and separation monitoring

Discipline 03 — Applied AI

AI that is accountable to a cycle time.

Factory copilot

A language interface over your historian, MES and maintenance records. Operators ask why line 3 slowed at 14:20 and get an answer traced to tag-level evidence.

Quality prediction

Models trained on process signals that flag drift before a part goes out of tolerance — moving inspection from detection to prevention.

Predictive maintenance

Vibration, thermal and motor-current signature analysis with a remaining-useful-life estimate that maintenance planners actually schedule against.

Industrial AIGenerative AILLMsVision Language ModelsVision Language Action ModelsWorld ModelsFactory IntelligenceDecision IntelligenceProduction OptimisationRoot Cause Analysis
OEE 92.4%CYCLE 41sDRIFT 0.6% OPENUSD SCENE — 1:1 PHYSICAL SYNC
FIG. 03 — Digital twin, line synchronisationSHEET RL-330 · REV A

Discipline 04 — Simulation

Commission the line twice. Once virtually.

We build the factory in OpenUSD before it exists in steel. Robot programs run against the emulated PLC, interlocks are tested by fault injection, and operators train on the twin while the panel is still being wired.

The result is measurable: most of the debugging that traditionally happens on site, under production pressure, happens instead at a desk, weeks earlier.

NVIDIA OmniverseIsaac SimOpenUSDROS 2Virtual CommissioningSynthetic DataFactory SimulationProduction Simulation

Discipline 05 — Vision

Inspection that holds up at line rate.

Lighting first, optics second, algorithm third. Most vision projects fail because the illumination was chosen last — we start with the physics of the defect and design backwards to the camera.

Deep-learning classifiers are deployed only where classical methods genuinely cannot hold, and always with a measured false-reject rate you can put in a quality plan.

Defect DetectionSurface InspectionDimensional MetrologyBarcode & DMROCR / OCV3D VisionDeep LearningVision-Guided Robotics
PASS 0.994 POROSITY 0.971 SCRATCH 0.883 CAM-01 · 5MP · 12 mm · RING LIGHT · 38 ms/PART
FIG. 04 — Inspection frame, castingSHEET RL-410 · REV D

Delivery

Thirteen phases, one accountable team.

This is the actual sequence we run. It is numbered because the order carries obligation — no phase starts until the previous one is signed off.

PHASE 01
Requirements

Cycle time, takt, part family, tolerance stack, safety category and the constraints of the existing line.

PHASE 02
Concept Design

Two or three cell topologies, each costed and simulated for reach, collision and throughput before selection.

PHASE 03
Mechanical Design

Full 3D model, GD&T drawings, tooling, guarding and a released bill of materials.

PHASE 04
Electrical Design

Schematics, panel layout, drive sizing, safety circuit to ISO 13849 PL d and the I/O map.

PHASE 05
Software Development

PLC logic, robot programs, HMI, vision routines, ROS 2 nodes and line-level sequencing.

PHASE 06
Robot Simulation

Offline programming and virtual commissioning against a digital twin of the emulated controller.

PHASE 07
Manufacturing

Machining, fabrication and procurement under inspection plans with material traceability.

PHASE 08
Assembly

Mechanical build, panel wiring, pneumatics and dry runs at our integration floor.

PHASE 09
Testing

Factory acceptance testing: cycle time, repeatability, capability studies, fault injection.

PHASE 10
Installation

Site preparation, rigging, alignment, utility connection and interface to upstream equipment.

PHASE 11
Commissioning

Site acceptance testing, tuning to production rate, safety validation and sign-off.

PHASE 12
Training

Operator, maintenance and engineering handover with documentation and recovery procedures.

PHASE 13
Support

Remote diagnostics, spares strategy, condition monitoring and continuous-improvement cycles.

Technology

The stack we are fluent in.

Tool choices follow the problem. These are the platforms our engineers work in daily and can defend in a design review.

ROS 2
NVIDIA Isaac Sim
Omniverse
OpenUSD
CUDA
TensorRT
Jetson Orin
OpenCV
Python
C++
Docker
Linux RT
MoveIt 2
Gazebo
SolidWorks
CATIA V5
Autodesk Inventor
PTC Creo
ANSYS Mechanical
Siemens TIA Portal
Rockwell Studio 5000
Beckhoff TwinCAT
OPC UA
MQTT Sparkplug
PROFINET
EtherCAT
Azure IoT
AWS IoT SiteWise
GitHub Actions
Grafana
L0 · SENSORS, DRIVES, ROBOTS, SAFETY I/O L1 · PLC / MOTION / SAFETY CONTROLLER — PROFINET, EtherCAT L2 · EDGE COMPUTE — VISION, ROS 2, JETSON, OPC UA L3 · DIGITAL TWIN + AI SERVICES — OMNIVERSE, MODEL SERVING L4 · MES / ERP / HISTORIAN / CLOUD ANALYTICS DETERMINISTIC ↑ CONTEXTUAL
FIG. 05 — Control architecture, L0–L4SHEET RL-500 · REV B

Industries

Where our systems run.

Different regulatory regimes, different failure costs, different definitions of “good”. We adapt the engineering, not just the sales deck.

AutomotiveSemiconductorElectronicsMedical DevicesHealthcareLogisticsWarehousingFood & BeveragePackagingPowerEnergyAerospaceHeavy EngineeringProcess Industries

Research & Development

What we are working on before clients ask for it.

Our R&D group runs a standing programme in adaptive manufacturing — cells that reconfigure from a CAD model instead of a re-teach, robot policies learned in simulation and transferred to hardware, and industrial agents that plan rather than merely report.

Some of it becomes product. Some of it becomes a paper. All of it is why our integration work does not look like 2015.

  • 01Physical AIpolicy learning for contact-rich tasks
  • 02Adaptive manufacturingCAD-driven cell reconfiguration
  • 03Factory AI agentsplanning over plant state
  • 04Robot learningimitation + reinforcement, sim-to-real
  • 05Humanoid roboticsgeneral-purpose material handling
  • 06Digital manufacturingclosed-loop twin synchronisation
  • 07AI planningschedule optimisation under disturbance
  • 08Factory optimisationbottleneck discovery from event logs

Why Resonae

What you are actually buying.

01

Engineering excellence

Design reviews with real calculations. Every load path, tolerance stack and cycle-time budget is documented and defensible.

02

Custom design

No forced product fit. If your part needs a machine that does not exist, we design it.

03

End-to-end delivery

One contract from requirements through commissioning and support. One team accountable for rate.

04

Global standards

ISO 12100, ISO 13849-1, IEC 61508, IEC 62443, UL 508A on request — engineered in, not retrofitted.

05

Applied innovation

A live R&D programme in physical AI and adaptive manufacturing feeding directly into delivery.

06

Experienced engineers

Mechanical, controls, vision and AI engineers in the same room, on the same drawing set.

07

Reliable support

24/7 production support, remote diagnostics and a defined spares strategy from day one.

08

Scalable architecture

Cells designed as line building blocks — replicable across plants without re-engineering.

09

Future-ready

Twin-ready, data-instrumented and AI-capable from commissioning, not as a later retrofit.

Clients

Trusted on production-critical lines.

AUTOMOTIVE OEM
TIER 1 SUPPLIER
SEMICONDUCTOR FAB
MEDICAL DEVICES
AEROSPACE MRO
3PL LOGISTICS
FMCG PACKAGING
POWER UTILITY
HEAVY ENGINEERING
ELECTRONICS EMS
PROCESS PLANT
COLD CHAIN

They were the only integrator who brought a simulation to the kickoff instead of a quotation. We knew the cycle time was achievable before we signed.

Head of Manufacturing EngineeringAutomotive Tier 1, Pune

The twin caught an interlock conflict that would have cost us a week on site. That single find paid for the simulation work.

Plant Operations DirectorPackaging, Chennai

Their vision team quoted a false-reject rate and then held it in production. Nobody else would put a number on paper.

Quality ManagerPrecision castings, Coimbatore

FAQ

Questions engineering teams ask us

What does Resonae Labs actually do?

Resonae Labs is an engineering company that designs, builds and commissions automated manufacturing systems. That covers mechanical design of the machine, the robot cell and its tooling, the electrical and control system, machine vision, the digital twin used to validate it, and any AI models that run on top. We deliver as a single accountable contract from requirements through to production support.

Do you supply robots, or integrate them?

We integrate. We are independent of any robot manufacturer and specify ABB, FANUC, KUKA, Yaskawa, Universal Robots, Stäubli, Omron, Doosan or others based on payload, reach, cycle time, service network and the controls environment already in your plant.

How long does a typical robot cell take from order to production?

A single-station cell with existing part geometry typically runs 14 to 20 weeks from order to site acceptance. A multi-station line with new tooling and vision runs 24 to 40 weeks. Virtual commissioning usually compresses the on-site phase by 40 to 70 percent, which is where most schedule risk lives.

Can you work on an existing line rather than a new one?

Yes — brownfield modernisation is a large share of our work. That includes retrofitting vision to an existing station, replacing obsolete controllers, adding data instrumentation and OPC UA connectivity, or converting a manual station to a collaborative robot application.

What is a digital twin used for in practice?

Three concrete things: validating robot reach and cycle time before steel is cut, virtual commissioning of PLC and robot code against an emulated controller so that logic faults are found at a desk instead of on the line, and generating synthetic training images for vision models where real defect samples are scarce.

Where do you operate?

Our engineering centre is in Bengaluru, with project delivery across India — Chennai, Pune, Hyderabad, Mumbai, Delhi NCR and Kerala — and commissioning teams that travel internationally for export lines and overseas plants.

Next step

Ready to scope your next automation programme?

Send us a drawing, a cycle-time target or a line layout. Our engineers respond with a technical assessment — not a brochure.