Most computer vision on a factory floor still lives in a pilot folder. Only about one manufacturer in four runs generative AI at facility or network scale today, and roughly the same share is still piloting it (Deloitte, 2025 Smart Manufacturing and Operations Survey, May 2025: deloitte.com). The gap is rarely the model itself. It is the workflow around the model: what a camera catches, who reviews it, and what happens next.
Mirai360 AI builds agents for three shop-floor workflows where a vision model already does useful work: assembly and process verification, safety and PPE compliance, and document OCR for paperwork that never reaches the ERP. In each case, the vision model detects, the agent assembles the evidence and drafts the next step, and a named person approves the action.
What does a vision model add to an agent deployment?
An AI agent built on a large language model reads text: reports, tickets, spreadsheets. A vision model reads the floor itself: a part on a jig, a worker at a hazard-zone door, a stamped inspection sheet. Paired together, the vision model detects what a camera sees and the agent turns that detection into a drafted action — a hold, a notification, a data entry — that a person then approves or corrects. Neither replaces the judgment call; both remove the manual step of watching, reading, and typing that call up by hand.
Assembly and process verification
A station that releases the wrong part, in the wrong position, or the wrong orientation, usually finds out downstream, after the unit has already moved on. A vision model checks the unit against the work instruction before release: the right part, in the right position and orientation. On a failure, the agent logs the check, holds the unit, and drafts the rework ticket. The line supervisor confirms the hold before the unit moves again.
The capability is not experimental. A peer-reviewed in-line vision system verified welded-flange positions to sub-millimetre accuracy and processed 50 frames in 1.3 seconds without slowing the line (Frustaci et al., "Robust and High-Performance Machine Vision System for Automatic Quality Inspection in Assembly Processes," Sensors, 2022: pmc.ncbi.nlm.nih.gov). That is a published benchmark of the underlying technique, not a claim about any specific Mirai360 deployment.
Safety and PPE compliance
A missed hard hat in a hazard zone is a safety gap, not a productivity metric, and it deserves a different kind of caution than a missed part. A vision model flags a missing hard hat or safety jacket in a designated hazard zone; the agent notifies the shift supervisor and logs the event for the safety record. The supervisor decides every intervention. The system takes no automated action against any worker: this is a safety aid, not a means of individual tracking.
Detection accuracy at this task is well studied. A peer-reviewed PPE-detection model reports 96.51% accuracy with an F1 score of 0.97 (Delhi, Sankarlal & Thomas, "Detection of PPE Compliance on Construction Site Using Computer Vision Based Deep Learning Techniques," Frontiers in Built Environment, 2020: frontiersin.org). That figure is a model metric, not a measured incident-reduction outcome, and Mirai360 does not present it as one.
Document OCR and paperwork digitisation
Inspection sheets, delivery notes, and mill certificates carry data a plant already produces and mostly never reaches the ERP, because someone would have to retype it. A vision model reads the document and extracts the fields. The agent validates the extracted fields against the ERP record and routes every mismatch for review. A person clears every flagged mismatch before any record is updated — the agent digitises the paperwork; it does not get the final say over what the record says.
Why does a person approve every action, not just review it later?
A hold, a safety notification, and a data update are each a small commitment with a downstream cost if wrong: a line stopped for nothing, a supervisor pulled off the floor for a false flag, a record updated with a misread figure. Mirai360 builds the approval step into the workflow itself, not as an audit added afterward. The vision model detects and the agent drafts; a named person with the authority to act reviews the evidence and approves before anything changes on the floor or in the record.
How does Mirai360 apply its deployment discipline to vision agents?
The same discipline applies here as to every workflow Mirai360 deploys, because the failure pattern is industry-wide: Gartner expects over 40% of agentic AI projects to be cancelled by the end of 2027, citing escalating costs, unclear value, and weak controls (Gartner press release, 25 June 2025: gartner.com).
- Pick one workflow first. One camera position and one decision, not the whole line.
- Record a baseline before go-live. Current hold rate, false-flag rate, or manual re-entry time, written down before the agent starts.
- Keep a person on every action. The agent detects and drafts; a named supervisor or engineer approves.
- Log everything. Every detection, draft, and approval sits in one audit trail.
- Scale against pre-agreed numbers. Wider camera coverage or wider agent authority follows only when the recorded numbers support it.
What does a manufacturer need to start?
A plant does not need a computer-vision team to start. It needs a camera position on the workflow in question, a written description of the work instruction or hazard-zone rule the vision model should check against, and a decision about who approves the resulting action. Mirai360 supplies the model, the guardrails that keep detections inside the rule a plant defines, and the logging that makes every detection traceable.
How do you measure whether it is working?
Judge the deployment against the baseline recorded before it started: hold rate against actual defect rate, false-flag rate on safety alerts, and time saved on manual re-entry for digitised documents. Review these numbers on a set schedule, and widen the agent's coverage only when they support it.
Frequently asked questions
Does the vision model replace the line supervisor or safety officer?
No. It detects and the agent drafts the next step; a named supervisor or safety officer approves every hold, notification, or record update.
Is the PPE-compliance agent used to track individual workers?
No. It flags a missing item of protective equipment in a designated hazard zone and notifies the supervisor. It takes no automated action against any worker, and Mirai360 does not build it as an individual-tracking tool.
What does a plant need before deploying a vision agent?
A camera position on the workflow, a written work instruction or hazard-zone rule for the model to check against, and a decision about who approves the resulting action. No in-house computer-vision team is required.
How is this different from a general quality-inspection workflow?
Quality-inspection triage (see our related post) starts from inspection reports and defect notes an engineer has already written. The workflows described here start from the camera itself, before any report exists.
Talk to us
Mirai360 AI scopes a vision-agent deployment against a plant's own baseline numbers before anything goes live. Start with a free discovery call — see the workflows and get in touch on our manufacturing page.
FAQ
- Does the vision model replace the line supervisor or safety officer?
- No. It detects and the agent drafts the next step; a named supervisor or safety officer approves every hold, notification, or record update.
- Is the PPE-compliance agent used to track individual workers?
- No. It flags a missing item of protective equipment in a designated hazard zone and notifies the supervisor. It takes no automated action against any worker, and Mirai360 does not build it as an individual-tracking tool.
- What does a plant need before deploying a vision agent?
- A camera position on the workflow, a written work instruction or hazard-zone rule for the model to check against, and a decision about who approves the resulting action. No in-house computer-vision team is required.
- How is this different from a general quality-inspection workflow?
- Quality-inspection triage starts from inspection reports and defect notes an engineer has already written. The workflows described here start from the camera itself, before any report exists.