Inspection that runs at line rate, downtime that explains itself, and the machine knowledge that currently walks out at retirement.
Visual quality inspection, predictive maintenance and shop-floor copilots.
A plant is already instrumented. PLCs log every stop, the MES holds the production order, the CMMS holds the work order history, and the quality department has years of inspection records. What is missing is the join between them, so the downtime code entered at end of shift never meets the process parameter that caused it, and the escaped defect never meets the tool life it correlates with.
Vision is where most plants see the first honest return. Surface defects on castings, weave faults, missing components, wrong labels, seal integrity — all judged today by a person under time pressure, sampling rather than checking. A camera and a model trained on your own reject bin can check everything at line rate and route the borderline units to that same person, who is now spending their attention where it changes the outcome.
The second return is knowledge. SOPs, maintenance manuals, drawings, past 8Ds and CAPAs and the supervisor's memory are the plant's real operating system, and most of it is not retrievable at 2am on a night shift. A private assistant over that corpus, with the source document cited, is what keeps a new technician from guessing.
Named the way your operators name them, not the way a vendor deck names them. If none of these land, we are the wrong partner for this problem.
The operator picks a reason code at end of shift from a list that never fits. The real cause — a parameter drift, a material batch, a changeover done differently — is already gone by then, so the same stop repeats next month.
Visual checks at line rate mean statistical confidence, not certainty. The defect that escapes is found by the customer, and the cost is an 8D, a containment sort and a credit note rather than a scrap ticket.
Preventive schedules replace parts that had life left and miss the bearing that was already failing. Condition data exists in the drive and the vibration route sheet, but it is not scored against the failure history.
One supervisor knows how the machine behaves in humid weather and which fixture needs a specific setup. Nothing about that is written down in a form a new hire can find at 2am.
Ordered the way we would sequence them in a manufacturing engagement. Each one links to the capability behind it.
A model trained on your own reject bin checks every part rather than a sample — surface defects, missing components, label and print verification, seal integrity. Borderline calls are routed to an inspector, and their decision retrains the model.
PLC and SCADA stop events joined to the production order, the operator note and the process parameters either side of the stop. Reason codes are proposed from evidence rather than picked from a list at shift end.
Vibration, current signature, temperature and cycle-count history scored against the CMMS failure record, served on the plant network and monitored for drift. Signals raise a work order; the maintenance planner still decides.
Machine manuals, standard operating procedures, setup sheets, 8D reports and CAPA history in one private assistant, answering in the operator's language with the source document and page cited.
An agent that assembles the 8D, the CAPA entry and the PPAP submission pack from inspection data, process records and the containment log — leaving the quality engineer to review a draft instead of building one.
Camera-based checks for helmet, glove and shoe compliance at defined gates, and for people entering a robot or press envelope. Alerts are aggregated for the safety officer rather than pointed at individuals.
Read-only to begin with, write access only where a workflow needs it, and every integration documented before it goes near production.
These are targets and mechanisms, not results borrowed from somebody else’s project. We baseline each one on your data in the first fortnight, and report against that baseline monthly.
Measured against the inspection baseline recorded before the model goes in, on the same shifts and the same product mix.
The number a condition-based programme has to move. Availability improvement without a fall in quality is the only version that counts.
Complaints per million parts, and the number of 8Ds opened. Inline inspection either changes this or it has not earned its place.
From a technician's question to a sourced answer. The comparison is against finding the right person, not against a perfect answer.
Anything that influences a quality decision needs a validation record, a change log and traceability from result to input. We build the audit trail with the system rather than assembling it before a surveillance audit.
Plant data does not need to leave the plant. Inference runs at the edge or on an on-premise server inside your OT segment, with a one-way path to the reporting layer and no inbound route to the controller network.
Safety monitoring is a legitimate purpose; individual surveillance is a different one. We keep detection anonymous, aggregate to zone and shift, and set a short retention window agreed with your HR and works committee.
Drawings and specifications carried in a retrieval system inherit the NDA that covers them. Deployments are private, indexes stay inside your tenancy, and nothing is used for third-party model training.
This is how we scope and build, not legal advice. Your compliance, risk and legal teams stay the authority on what applies to your organisation, and we work to their reading of it.
Pick one line and one defect family — the one that generates the most rework or the most customer noise — and run inline inspection alongside the existing check for four weeks. The inspector keeps deciding; the model is scored against them. You end with a measured accuracy figure on your own parts, a labelled dataset you own, and a defensible answer on whether to roll it to the next line.
We sit with your operators, map the workflows, and score every candidate use case on value, data readiness and risk. You leave with a ranked backlog and a cost model.
We build the single highest-value agent against your real data and measure it against the humans doing that job today. If the number is not there, we say so.
Describe how manufacturing works in your organisation — the systems, the constraints, the part that goes wrong every week. We will tell you whether it is worth automating and what the first project would cost.