QUALITY
Inspection and root cause
Detect defects earlier, connect failure patterns to process conditions, and route exceptions to the accountable owner.
MEASUREScrap / FPY / CoPQ
AI IMPLEMENTATION FOR MANUFACTURING
Put AI to work on the manufacturing floor.
Aryo helps manufacturing and HardTech leaders identify, implement, and scale AI across engineering, operations, and the frontline—without losing control of quality, safety, or the process.
One line. One problem. One KPI. Prove the workflow in production, then scale the operating model.ARYO IMPLEMENTATION SYSTEMWORKFLOW / CONTROL / VALUE
START WITH A BOUNDED SYSTEMOne line. One problem. One KPI. Prove the workflow in production, then scale the operating model.
01Frontline workflowCONNECT
02MES · QMS · CMMS · ERPCONNECT
03Governance · security · ROIGOVERN
01Lower scrap
02Less downtime
03Faster changeovers
04More throughput
WHERE AI EARNS ITS KEEP
Redesign the workflow, not just the task.
We prioritize decisions that repeat, have usable evidence, and can be measured by an accountable technical or operating owner.
RELIABILITY
Predictive maintenance
Turn machine signals, work orders, and technician knowledge into risk-based maintenance decisions.
MEASUREDowntime / MTBF / MTTR
OPERATIONS
Scheduling and throughput
Surface constraints, sequence work intelligently, and give planners explainable options around the real line.
MEASUREOEE / Cycle time / OTIF
FRONTLINE
Operator knowledge systems
Put SOPs, troubleshooting history, and governed expert guidance at the point of work.
MEASURETraining / Resolution time
PAST / PRESENT / FUTURE
Adoption is moving from tools to operating systems.
Benchmark progress shows what is becoming technically possible. Implementation determines whether that capability becomes safe, repeatable enterprise value.
Isolated prediction
Point solutions forecast a failure or classify an image, but sit outside the operating decision.
Connected assistance
AI reads plant context, proposes the next action, and routes exceptions through existing systems and owners.
Governed closed loops
Validated agents coordinate planning, quality, maintenance, and engineering inside explicit control limits.
CONVENTIONAL VS. AI-ENABLED
Make the operating hypothesis visible.
Illustrative person-hours for a bounded engineering workflow. Aryo replaces these assumptions with client baselines during the diagnostic.
01
−35%Machined component
Bracket / fixture / manifold
02
−36%Molded enclosure
Housing / cover / handheld product
03
−31%Mechatronic assembly
Actuator / sensor module / end effector
04
−30%Configured industrial machine
Workcell / line module / capital equipment
THE ARYO ENGAGEMENT
From use case to governed capability.
A fixed-timeline model establishes the baseline, proves the workflow, connects the stack, and transfers the operating model.
- 01WEEKS 1–3
Diagnostic and opportunity map
Map value streams, systems, data quality, decision rights, and frontline pain points.
- 02WEEKS 4–8
Pilot and business case
Deploy one workflow on a real line or part family with a baseline and named KPI owner.
- 03WEEKS 9–16
Integration and scale
Connect MES, ERP, QMS, CMMS, PLM, and the people who operate the process.
- 04ONGOING
Governance and handover
Establish controls, monitoring, ownership, training, and a repeatable rollout model.
EXTERNAL SIGNALS
Capability is moving quickly. Production discipline matters more.
BEGIN WITH A REAL WORKFLOW
Define what the first implementation must prove.
Bring one product family, device class, line, or automation loop. Aryo will frame the baseline, systems, controls, pilot scope, and measurable decision gate.