Industrial automation & applied intelligence
Automation engineering,
end to end.
We are not a dashboard vendor bolted onto someone else's stack. Aimetric wires the sensors, writes the acquisition layer, models the data with machine learning, and ships the application that a plant actually runs on — across eight service lines that share one platform and one data model.
How every engagement runs
Four stages. No black boxes.
Smart shop-floor automation
The line runs the same way on every shift.
Most plants do not lose output to broken machines. They lose it to variation — a station sequenced differently by the night shift, a fixture released before a torque check completed, a batch that moved forward without its quality record. Shop-floor automation removes the room for that variation to exist.
We automate the station logic itself: interlocks that will not release a part until every preceding operation has passed, sequencing that enforces the routing, operator guidance delivered on-screen at the point of work, and a traceability record written for every single part as it moves. Because ADAM speaks to the existing PLCs directly, this rarely means replacing equipment — it means making the equipment you own accountable.
- 01
Interlocks & poka-yoke logic
Parts cannot advance until every preceding operation is confirmed complete and in-spec.
- 02
Operator guidance screens
The right work instruction, torque spec and drawing at the station, driven by the live part ID.
- 03
Genealogy & traceability
A per-part record — machine, parameters, operator, batch, timestamp — queryable years later.
- 04
Two-way recipe management
Setpoints and recipes pushed down to the equipment on changeover, with acknowledgement.
Applied intelligence
Once every station writes a record, cycle-time drift becomes measurable. Our models learn each station's normal signature and flag the ones creeping slower — usually the earliest visible symptom of tool wear or fixture misalignment, weeks before it becomes scrap.
Delivered for
A leading Indian automotive & farm-equipment conglomerate
Assembly sequencing · traceability
A tier-one auto-component supplier, South Korea
Station interlocks · recipe control
Asset performance optimization
More output from the assets you already own.
Every line has exactly one constraint at any moment, and it is almost never the machine people assume. Capacity spent anywhere other than the constraint is capacity wasted. Asset performance engineering is the discipline of finding that constraint with data rather than opinion, quantifying precisely what it costs, and then moving it.
We instrument the whole line, not just the suspect asset — because a press that appears slow is often starved by an upstream conveyor or blocked by a downstream buffer. With every loss event classified and time-stamped, the argument stops being about blame and becomes a ranked list: this is the constraint, this is what it costs per shift, this is what removing it returns. Clients typically see 10–15% more output without buying a machine.
- 01
Constraint identification
Line-wide throughput modelling that isolates the true bottleneck and its shift-by-shift movement.
- 02
Loss accounting to 100%
Every minute between theoretical and actual output classified — changeover, starve, block, breakdown, speed, quality.
- 03
OEE that survives scrutiny
Availability, performance and quality computed from machine signals, not from a manual logbook.
- 04
Prioritised improvement backlog
Interventions ranked by recoverable output per rupee, so engineering effort goes where it pays.
Applied intelligence
Constraints move. A model trained on production history predicts where the bottleneck will land for a given product mix and shift pattern — so planning can sequence around it before the shift starts, rather than discovering it in the morning review.
Delivered for
A top-three Indian automotive battery manufacturer
Formation & assembly throughput
One of the world's largest line-pipe manufacturers
Spiral mill output recovery
Asset performance analytics
A 360° view of every interconnected asset.
Asset Performance Management 4.0 is less about watching machines and more about understanding how they depend on each other. A compressor derating does not announce itself as a compressor problem — it appears as rejects at a station three departments away. Analytics is what connects the two.
We build a single asset model across the plant — criticality, running hours, energy draw, failure history, maintenance cost — and monitor it continuously through LARA. Reliability and availability stop being monthly numbers assembled in a spreadsheet and become live properties of the plant, with 100% visibility of production loss for root-cause analysis.
- 01
Enterprise-wide remote monitoring
One pane across lines, plants and geographies, with role-based views for each level.
- 02
Reliability & availability KPIs
MTBF, MTTR, availability and maintenance cost per asset, trended and benchmarked.
- 03
Root-cause analysis workflow
Every loss event traceable to the asset, parameter and moment that caused it.
- 04
Maintenance prioritisation
Work ranked by risk and production consequence, not by whichever asset complained loudest.
Predictive maintenance
This is where LARA's AI layer earns its place. Models trained on each asset's own history learn its normal signature across vibration, temperature, current draw and cycle time — then detect the drift that precedes failure. Clients see roughly 25% less unplanned downtime and 20% lower maintenance cost by servicing on evidence instead of calendar.
Blue nodes are assets whose predicted remaining life has dropped below the planned-maintenance window.
Delivered for
A top-three Indian automotive battery manufacturer
Plant-wide APM & energy
One of the world's largest line-pipe manufacturers
Predictive maintenance on critical assets
AI part defect analysis
A traceable reason for every rejection.
Quality departments rarely lack defect data. What they lack is the link between the defect and the machine conditions that produced it — so root cause becomes a meeting rather than a query. We capture defect data at the moment and place it is created, and bind it to the process parameters live at that instant.
On a pipe mill that means weld defect capture correlated to travel speed, current, voltage, wire feed and operator. On a press line it means dimensional deviation against tonnage curves and die temperature. Once thousands of parts carry that binding, correlation stops being anecdotal — the data itself shows which parameter window produces which defect, and control limits can be tightened around the evidence.
- 01
Point-of-creation capture
Defect logged at the station — by gauge, vision system or guided operator entry — never reconstructed later.
- 02
Parameter correlation
Each defect bound to the live machine parameters, batch, tool and shift that produced it.
- 03
Machine-vision inspection
Camera-based surface and dimensional inspection where human inspection cannot keep pace.
- 04
Pareto & cost-of-quality
Defect modes ranked by scrap and rework value, not by how often they get reported.
Applied intelligence
Classification models trained on labelled defect images and parameter histories learn the signature of each defect mode — then run forward, flagging parts likely to fail inspection while the process can still be corrected, rather than after the batch is scrap.
Photograph needed — weld seam inspection
A macro shot of a weld seam under inspection, or a vision camera mounted over the mill.
Recommended 1200×720, landscape. Replace this block with an <img>.
Delivered for
One of the world's largest line-pipe & home-textile manufacturers
Pipe weld defect data collection & analytics across the spiral mill
Autonomous mobile robots
We build the brain, not the chassis.
An AMR is only as good as its decision-making. Plenty of vendors will sell a plant a robot; far fewer can make a fleet behave sensibly on a real shop floor where forklifts cut corners, pallets appear in aisles, and people walk where the map says they should not.
We develop the ROS and ROS 2 stack that makes that work — mapping and localisation, global and local path planning, obstacle avoidance, docking and charging behaviour, and the fleet layer that stops two robots from arguing over the same aisle. The same stack reports into LARA, so robot utilisation, battery health and mission history sit alongside every other asset in the plant rather than in a separate vendor portal.
- 01
SLAM mapping & localisation
Reliable pose estimation in changing environments, not just on the day the map was built.
- 02
Navigation & dynamic re-routing
Nav2-based planning that re-routes around blocked aisles instead of stopping and waiting.
- 03
Fleet coordination
Mission allocation, traffic management, priority handling and automatic charge scheduling.
- 04
Safety & human interaction
Speed-and-separation behaviour tuned for aisles shared with people and manual vehicles.
Applied intelligence
Mission histories feed a model that learns the plant's congestion patterns by hour and shift — routing fleets around predicted traffic rather than reacting to it, and predicting battery degradation before a robot strands itself mid-mission.
Photograph needed — AMR on the floor
An AMR carrying material down a plant aisle. Recommended 1000×750.
Delivered for
A leading Indian automotive & farm-equipment conglomerate
ROS-based AMR brains — mapping, navigation, fleet coordination across assembly material movement
Automation feasibility studies
We will tell you which steps are not worth automating.
Automation capex fails more often from being pointed at the wrong process than from being badly executed. A station that looks manual and tedious may already be off the critical path; automating it buys nothing but a maintenance liability. A feasibility study exists to find that out before the money is committed.
We study the manual process as it is actually performed — not as the SOP describes it — with time studies, variability measurement and loss mapping. Then we model what automating each step would return: cycle-time gain, quality gain, labour redeployment, payback period and the risk of each option. The deliverable is a recommendation with the arithmetic attached, including the steps we advise leaving alone. There is no obligation to build anything with us afterwards.
- 01
Process & time study
Observed cycle times, variability and true bottleneck position on the current manual process.
- 02
Automation option modelling
Full, partial and assisted-automation scenarios modelled against the same baseline.
- 03
ROI & payback analysis
Capex, opex, recovered output and payback period per option — with sensitivity ranges.
- 04
Analytics-readiness assessment
What data the process could emit once automated, and what that data would then be worth.
Applied intelligence
Where a comparable process already runs instrumented elsewhere in the plant, we simulate the proposed line against that real distribution rather than against ideal cycle times — producing a payback estimate grounded in how the plant actually behaves.
Delivered for
Manufacturing majors across automotive, batteries and heavy engineering
Pre-capex studies on manual machine processes
Energy & utility intelligence
The second-largest cost line, usually the least measured.
Most plants can tell you what they spent on energy last month and almost nothing about where it went. Energy, compressed air, water and steam are billed in aggregate and consumed specifically — which makes waste invisible until it is very large.
We instrument the utility side with the same rigour as the production side: energy per line, per shift and per product; compressed-air leakage and specific power; water drawn against water accounted for; solar generation measured against irradiance so underperforming strings and inverters cannot hide inside a healthy-looking plant total. This is the family of systems behind our EMS, UMS, WMS and solar management work.
- 01
Energy Management System
Consumption attributed to line, shift and product — turning an overhead into a controllable cost.
- 02
Utility Management System
Compressed air, chillers, boilers and pumps monitored together, with efficiency-drift alerts.
- 03
Water Management System
Intake, treatment, process consumption and effluent tracked end to end and reconciled.
- 04
Solar Management System
String-level generation against irradiance, isolating inverter faults and soiling losses.
Applied intelligence
Baseline models learn what each line should consume for a given output and ambient condition. Deviation from that predicted baseline surfaces as an alert — catching a failing trap, a leaking header or a fouled heat exchanger while it is still cheap.
Delivered for
A top-three Indian automotive battery manufacturer
Energy management across formation & utility blocks
A captive solar generation operator
String-level solar productivity monitoring
Custom industrial applications
Built to your process, not to a product roadmap.
Products are where we start, not where we stop. Plenty of manufacturing problems do not fit a packaged system — a routing that exists nowhere else, a compliance record a regulator asks for in a specific format, an integration with a thirty-year-old machine whose vendor no longer exists.
We build those applications properly, in the stack your IT team will have to maintain afterwards — Python, .NET and C#, PHP, C++ — with live analytics built in rather than bolted on. That includes full custom MES platforms where an off-the-shelf MES would have forced the plant to change how it manufactures. Everything ships with documentation, source and a handover; we would rather your team could maintain it than have you locked to us.
- 01
Custom MES & production systems
Order execution, routing, WIP tracking and genealogy modelled on your actual process.
- 02
Live analytics applications
Reporting and dashboards generated on the fly through LARA, on top of any of the above.
- 03
Legacy & enterprise integration
ERP, SCADA, historian and legacy-machine interfaces — including protocols nobody documents any more.
- 04
On-premise or cloud, with handover
Deployed where your policy requires, delivered with documentation, source and team training.
Applied intelligence
Because every custom application writes into the same time-series model as ADAM and LARA, predictive analytics is available from day one rather than as a later project — the data is already in the shape a model needs.
Delivered for
A tier-one auto-component supplier, South Korea
Full custom MES platform built to their process
Artificial intelligence & predictive analytics
The layer that runs
underneath all eight.
We do not sell AI as a separate product line. It is the reason the other eight services return more than instrumentation alone would — and it only works because we own the data path from the sensor upward.
Less unplanned downtime once predictive maintenance is live
Lower maintenance cost by servicing on evidence, not calendar
Production-loss visibility for root-cause analysis
Start here
Tell us which process
still runs on paper.
We will study it, model what automating it returns, and tell you plainly whether it is worth doing — including when the answer is no. No obligation to build anything with us afterwards.