AI agents for manufacturing operations.
Most factory AI never leaves the pilot: only about one manufacturer in four runs generative AI at facility scale today.[1] Mirai360 AI takes a different route. Agents act on the data your ERP, maintenance log, and inbox already hold, and computer vision watches the line itself. Your people approve every decision before it reaches the floor.
of manufacturers have deployed generative AI at facility or network scale; 38% are still piloting. Deloitte, May 2025.[1]
of companies ever scale AI beyond pilots, across industries. McKinsey, Nov 2025.[2]
of agentic AI projects are forecast to be cancelled by end-2027: cost, unclear value, and weak controls. Gartner, Jun 2025.[3]
How every deployment is framed
Each use case below follows the same discipline, taken from our operating loop: name the workflow, define what the agent does, define what a person approves, and measure the outcome against a baseline recorded before go-live. A workflow that cannot be framed this way is a workflow we advise you not to automate yet.
The workflows agents take on
RFQ and quote generation
- Workflow
- Turning an inbound request for quote into a priced, lead-timed response before the buyer awards the order elsewhere.
- What the agent does
- Reads the spec sheet, checks current pricing rules, searches past quotes for comparable jobs, and drafts the response in your standard format.
- What the human approves
- A person with pricing authority reviews every quote before it leaves the building.
- Measured outcome
- Quote turnaround time, correction rate, and on-time response rate, compared to the baseline recorded before the agent went live.
Inventory and procurement
- Workflow
- Keeping materials available without overstocking, across systems that rarely agree with each other.
- What the agent does
- Monitors stock and demand, detects shortfalls, checks supplier availability in the ERP, and drafts purchase orders.
- What the human approves
- Procurement approves the purchase order and the supplier choice before anything is placed.
- Measured outcome
- Stockout incidents, excess-stock value, and order-cycle time against the pre-deployment baseline.
Quality-inspection triage
- Workflow
- Detecting and dispositioning defects on high-volume lines before they become customer returns.
- What the agent does
- A vision model reviews line images and inspection data and flags likely defects; the agent assembles the evidence and proposes a disposition of scrap, rework, or hold.
- What the human approves
- A quality supervisor confirms borderline rejects and any action that stops the line.
- Measured outcome
- Defect escape rate and triage time. The published benchmark: a Foxconn (Ingrasys) plant recognised in the World Economic Forum's Global Lighthouse Network reports a 97% cut in product defect rate from its AI transformation, with AI quality control among the named drivers.[4]
Maintenance scheduling
- Workflow
- Turning machine-condition signals into scheduled repairs before a failure stops production.
- What the agent does
- Reads equipment alerts and telemetry, predicts likely failures, and drafts the work order and technician slot.
- What the human approves
- The maintenance planner confirms the work order and the schedule before dispatch.
- Measured outcome
- Unplanned downtime and planning time; Deloitte reports predictive maintenance raising equipment uptime 10–20% and cutting maintenance-planning time 20–50%.[5]
Order status and delivery support
- Workflow
- Answering "where is my order?" accurately, across the full order lifecycle.
- What the agent does
- Reads ERP and logistics data, drafts the status reply, and raises an exception alert when an order slips.
- What the human approves
- A customer-service person confirms replies on delayed or exception orders before they are sent.
- Measured outcome
- Response time and escalation rate against the pre-deployment baseline.
Computer vision on the line
Agents read your systems; computer vision reads the line itself. The division of labour stays the same throughout: a vision model detects, the agent decides the next step and assembles the evidence, and a person approves the action, on the same platform, under the same guardrails, and in the same audit trail. Visual defect detection is covered in the quality-inspection workflow above; the capabilities below extend vision to the rest of the floor. Where a figure below is a model-accuracy metric rather than a plant outcome, it is labelled as such.
Assembly and process verification
- Workflow
- Confirming the right part, in the right position and orientation, before a station releases the unit.
- What the agent does
- A vision model checks presence, position, and orientation against the work instruction; the agent logs the check, holds the unit on a failure, and drafts the rework ticket.
- What the human approves
- The line supervisor confirms any hold or rework before the unit moves on.
- Measured outcome
- First-pass yield and escape rate against the pre-deployment baseline. Published capability benchmark: a peer-reviewed in-line system verified welded-flange positions to below a millimetre and processed 50 frames in 1.3 seconds without slowing production.[6]
Safety and PPE compliance
- Workflow
- Confirming required protective equipment is worn in designated hazard zones. This capability is a safety aid for supervisors rather than a means of individual tracking.
- What the agent does
- A vision model flags a missing hard hat or safety jacket in a hazard zone; the agent notifies the shift supervisor and logs the event for the safety record.
- What the human approves
- The supervisor decides every intervention; the system takes no automated action against any worker.
- Measured outcome
- Time-to-intervention and safety-audit completeness. Published capability benchmark: a peer-reviewed PPE-detection model reports 96.51% accuracy with an F1 score of 0.97. This figure is a model metric rather than an incident-reduction outcome.[7]
Document OCR and paperwork digitisation
- Workflow
- Turning inspection sheets, delivery notes, and mill certificates into structured data agents can act on.
- What the agent does
- A vision model reads the document and extracts the fields; the agent validates them against the ERP record and routes every mismatch for review.
- What the human approves
- A person clears every flagged mismatch before any record is updated.
- Measured outcome
- Manual data-entry hours and mismatch catch rate against the pre-deployment baseline.
What end-to-end agentic automation creates
Each workflow above removes one queue of manual reading, checking, and drafting. Run together on one platform, they compound: the same agents share your pricing rules, your stock data, and your maintenance history, and every action they take is logged in one audit trail. The result is a plant where systems that already hold the data finally act on it, and where every loop leaves reusable assets behind: an approved vendor path, a control template, an evaluation harness, and a correction log. The next workflow starts further along rather than from scratch.
The controls are not an afterthought. Every deployment ships with a one-page rulebook covering what the agent may do alone, what needs approval, full logging, a kill switch, and a named owner. This discipline keeps a deployment out of the 40% Gartner expects to be cancelled.[3]
One 30-minute call.
We look at your operation, find the one workflow worth starting with, and tell you honestly whether it clears the bar.
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- Deloitte, 2025 Smart Manufacturing and Operations Survey, May 2025: deloitte.com/us/en/insights/industry/manufacturing/2025-smart-manufacturing-survey.html
- McKinsey, The State of AI, November 2025: mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
- Gartner press release, 25 June 2025: gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027
- Hon Hai Technology Group (Foxconn) press release, "Foxconn Technology Group's Chengdu Campus Recognized by World Economic Forum's Global Lighthouse Network," 25 December 2023: honhai.com/en-us/press-center/press-releases/latest-news/572
- Deloitte Insights, Using predictive technologies for asset maintenance, May 2017: deloitte.com/us/en/insights/industry/manufacturing-industrial-products/industry-4-0/using-predictive-technologies-for-asset-maintenance.html
- Frustaci et al., "Robust and High-Performance Machine Vision System for Automatic Quality Inspection in Assembly Processes," Sensors (peer-reviewed), 2022: pmc.ncbi.nlm.nih.gov/articles/PMC9032890/
- Delhi, Sankarlal & Thomas, "Detection of PPE Compliance on Construction Site Using Computer Vision Based Deep Learning Techniques," Frontiers in Built Environment (peer-reviewed), 24 September 2020: frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2020.00136/full