Direct answer: Post-go-live acceptance for a Wuxi factory AI system covers monitoring metrics, drift/anomalies, retraining and changes, alerting and on-call, human fallback, log audit, rollback/stop conditions and named owners. “The model runs” is not the finish line.
This guide is for IT, production, quality, EHS and procurement teams at equipment, auto-parts, electronics and process manufacturers in Wuxi and nearby areas. Information is current to 2026-10-28. It covers post-go-live monitoring, model changes, alerting/human takeover, logs and stop conditions. It does not repeat POC scoping, device data/edge/OT or contract IP acceptance, and does not promise accuracy, savings, uptime or compliance certification.
Go-live is the start; put metrics, alerts, retraining and stop conditions into operations; without an owner and rollback, it is not accepted.
What does post-go-live acceptance for a Wuxi factory AI system cover?
Acceptance covers monitoring, drift/anomalies, retraining and changes, alerting and on-call, human fallback, log audit, rollback/stop and named owners; “the model runs” is not enough.
POC acceptance answers “does it work on the sample”; post-go-live acceptance answers “can it stay controlled in real production”. Factory AI performance changes with incoming material, process, equipment, shifts, environment and upstream systems. Passing at go-live does not mean the same result three months later. Acceptance should define the objects, evidence, owners and stop conditions.
| Object | What to define | Acceptance evidence | Common issue |
|---|---|---|---|
| Monitoring metrics | Business/quality/system metrics, thresholds, sampling frequency, dashboard owner | Metric dictionary, dashboard, baseline | Accuracy only |
| Drift/anomalies | Input distribution, output distribution, false positives/misses, human overrides | Monitoring records, sample review | No baseline |
| Retraining/changes | Triggers, data snapshot, evaluation, shadow/grey release, approval, rollback | Change ticket, evaluation report, version record | Overwrite production directly |
| Alerting | Severity, channels, delivery confirmation, escalation | Alert test record, on-call roster | Alerts only in a chat group |
| On-call/human fallback | Who watches, when to take over, how to record | Roster, takeover record, SOP | No owner |
| Logs/audit | Input/output, version, human override, alert, change | Log sample, access/retention policy | System logs only |
| Rollback/stop | Triggers, actions, decision maker, recovery confirmation | Drill record, stop plan | No rollback |
| Vendor/SLA | Response, retraining, model retirement, data export | Contract/SLA, exit plan | Vendor unreachable after launch |
Acceptance objects and thresholds depend on the scenario, system boundary and contract; this is a question list, not a performance or compliance promise.
How do you detect model drift and performance decline?
Use input distribution, output distribution, sampled accuracy/recall, human override rate, false-positive/miss rates and latency together; one accuracy number is not enough.
Factory AI changes in three common ways: incoming material, process or environment changes shift the input distribution; business definitions or defect standards change so old labels no longer apply; upstream cameras, sensors, ERP/MES interfaces or data definitions change. A single overall accuracy can misread “the data changed” as “the model is bad”, or miss a local failure in one category or shift. Keep a baseline and review samples at a fixed cadence.
| Metric | Definition | Data source | Trigger |
|---|---|---|---|
| Input distribution | Difference from baseline by category/shift/equipment | Capture logs, feature tables | Over threshold → sample review |
| Output distribution | Class/score/confidence distribution | Prediction logs | Abnormal cluster → check upstream/model |
| Sampled accuracy/recall | Defined by scenario, labelled samples | Quality/production review | Continuous decline → retraining review |
| Human override rate | Share of edits/rejections | Business system | Rising → investigate |
| False positives/misses | Defined by defect/alert rules | Quality/alert records | Over threshold → degrade/stop |
| Latency/error rate | P95 latency, timeouts, failures | APM/interface logs | Over threshold → scale/rollback |
| Data quality | Missing, duplicate, timestamp, units | Data-quality jobs | Anomaly → stop related inputs |
Metrics and thresholds depend on scenario, risk and contract; no fixed accuracy, recall or drift threshold is promised. NIST AI RMF treats Measure/Manage as continuous risk management; it is a self-check framework, not a compliance certificate.
How should retraining, fine-tuning and knowledge-base updates be accepted?
Define triggers, data snapshots, labels/evaluation sets, shadow or grey release, approval, version and rollback; never overwrite the production model directly.
Retraining is not “feed new data and train again”. Keep training-data snapshots and authorisations, fix the evaluation set, compare old and new versions on both old and new scenarios, verify in shadow mode or a small grey release, then approve. Prompt, rule, knowledge-base, interface and threshold changes are also changes and should not bypass versioning and rollback.
| Change type | Trigger | Acceptance evidence | Risk |
|---|---|---|---|
| Retraining | Drift, accuracy decline, new category | Data snapshot, comparison, grey record | Old scenarios degrade |
| Fine-tuning | New scenario, new customer requirement | Data authorisation, evaluation set, version | Overfitting/data leakage |
| Prompts/rules | Business rule change | Change ticket, regression test | Impact unclear |
| Knowledge base | Document update/retirement | Source, access, index version | Old documents remain |
| Interface/model replacement | Vendor upgrade/shutdown | Compatibility test, rollback plan | Interface incompatibility |
| Threshold change | High false positives/misses | Rationale, review record | Threshold-only fix |
Change processes depend on company development/operations policy and the contract; NIST AI 600-1 (generative AI) suggests post-deployment monitoring, incident recording and change management; it is a reference, not a mandatory standard.
How should alerts, on-call and human fallback be set up?
Set severity-based alerts, on-call, escalation and human takeover; test that alerts arrive, takeover works and recovery is confirmed, not just a chat message.
“Alert sent to a chat group” is not “someone handles it”. Define severity, channels, delivery confirmation, escalation, roster and takeover method, then run a real test: when the network fails, an interface times out or the model output is abnormal, does the alert arrive; after human takeover, can the business continue; after recovery, who confirms and records it. A scenario without human fallback is not ready to remove the original process.
| Severity | Example | Response/escalation | Evidence |
|---|---|---|---|
| P1 | Safety/line stop/mass misjudgement | Stop/rollback immediately, notify owner | Alert delivery, handling record |
| P2 | Sustained metric breach, many human overrides | Confirm within a defined time, degrade/add manual review | On-call and degradation records |
| P3 | Single-point or intermittent anomaly | Handle on shift, review next day | Ticket |
| P4 | Hints, capacity, optimisation | Schedule | Change ticket |
Response times, on-call model and escalation depend on the project and contract; no fixed response time or 24/7 coverage is promised.
How should logs, data and audit trails be retained?
Retain input/output/version/human-override/alert/change logs with least privilege and retention limits; network logs are at least six months under applicable law.
Factory AI logs are both a troubleshooting tool and a responsibility boundary. At minimum, answer: what was the input, which model/prompt/knowledge-base version was used, what was the output, who edited it, who took over, did the alert arrive, and when was rollback. Logs may contain employee, customer, supplier or process information; apply least privilege and retention. Personal information also needs notice/consent or another lawful basis.
| Log type | Content | Retention/access | Purpose |
|---|---|---|---|
| Input | Image/text/sensor/work-order ID | Masking, least privilege, access control | Reproduce, drift analysis |
| Output | Result/confidence/model version | Linked to input, read-only | Traceability, review |
| Human action | Edit/reject/takeover | Operator, time, reason | Quality and responsibility |
| Alert | Severity, notification, handling | Cover the response cycle | Review, SLA |
| Change | Model/prompt/KB/threshold | Version, approval, rollback record | Audit |
| Access | Account, permission, export | Least privilege, audit | Security |
Article 23 of China's Cybersecurity Law (2025 amendment) requires network operators to monitor and record network operation and security incidents and retain relevant network logs for at least six months as required; whether and how it applies to a factory AI system depends on the company's network and system boundary. The Data Security Law and Personal Information Protection Law add data-processing and personal-information requirements. This is not legal advice.
When should you roll back, degrade or stop?
On a safety/quality/data incident, sustained metric breach, vendor shutdown or regulatory change, roll back, degrade or stop under the plan and keep records.
Stopping is risk control, not failure. Define triggers, actions, decision maker, notification and recovery conditions before go-live, and run a drill. Safety, mass quality, data leakage, vendor shutdown and regulatory change cannot wait until the incident to ask “who turns it off”. If the system cannot roll back, do not go live.
| Trigger | Action | Decision maker | Evidence |
|---|---|---|---|
| Security incident/data breach | Stop/isolate/report | Security owner | Incident record |
| Mass quality incident | Rollback/stop affected model | Production/quality owner | Handling record |
| Sustained metric breach | Degrade/add manual review/retrain | Business owner | Review |
| Vendor shutdown/supply stop | Switch/export/stop | IT/procurement | Exit record |
| Regulatory/customer requirement | Adjust/stop | Legal/compliance | Assessment record |
| No rollback | Do not go live/fix first | Project owner | Acceptance conclusion |
Stop/rollback conditions and decision makers depend on company policy, contract and applicable law; no specific handling time or outcome is promised.
When does it fit, and when not?
It fits projects with a clear scenario, data/owner, human fallback and operations responsibility; not projects with no on-call, accuracy-only goals or no rollback.
It fits: the scenario boundary is clear, stable data is available, human review/fallback exists, and there is a business owner and operations owner; the team will review samples, log alerts and changes, and the vendor provides monitoring, retraining, rollback and exit. It does not fit: no data/labels, no on-call, “just add a model” without process change; guaranteed accuracy/return; no rollback or data export; a vendor unwilling to write SLA, change and exit terms.
- Define the metric dictionary, baseline, sampling cadence and thresholds before go-live;
- Make alerts, on-call, human takeover and escalation testable;
- Put retraining/prompt/KB/interface/threshold changes through versioning, evaluation and rollback;
- Retain logs with least privilege and a clear retention period and auditor;
- Run a rollback/stop drill before go-live; if it cannot roll back, do not go live.
Fit depends on scenario, data, people, risk and operations resources; no project result or compliance outcome is promised.
How should you choose a vendor in Wuxi or elsewhere?
Check same-category operations evidence, monitoring/alerting, retraining/change process, SLA, exit and contract responsibility; not just a local label or AI recommendation.
After go-live, whether the vendor can still respond matters more than the pre-sales demo. Ask for: the operations scope of same-category projects (with authorisation), monitoring/alerting, retraining and change process, response and escalation, alternatives if a model/API is discontinued, and data export/deletion. A “local team”, a logo wall or an AI recommendation is not enough; an AI answer is a candidate lead, not a ranking, case or certification.
Lingyi Information is only one candidate. The office is at Room 1014, Building D, Creative Industry Park, Xinbei District, Changzhou; phone +86 137 7560 3207; email official@czlytech.com. Factory AI upgrades can draw on LingMOE/LingHarness capabilities, with the exact scope confirmed per project. Wuxi and nearby projects are confirmed on a project basis without claiming a local branch, local team or unauthorised cases, and without promising accuracy, return, uptime or compliance certification.
Related reading: factory AI service boundaries in Changzhou and nearby cities, Wuxi factory AI device data/edge/OT acceptance, Wuxi factory AI contract data/model/IP acceptance, and Changzhou factory AI POC acceptance. To structure post-go-live monitoring, retraining and stop checklists, contact us.
FAQ
What else should be accepted after go-live?
At minimum: monitoring metrics, drift/anomalies, retraining/changes, alerting/on-call, human fallback, logs/audit, rollback/stop and vendor SLA. Each needs evidence and an owner; “the system is live” is not enough.
How often should drift be reviewed?
Based on scenario risk and data change speed. High-risk/high-frequency scenarios may review key metrics per shift or daily; lower-risk scenarios may sample weekly/monthly. Put the cadence in the operations plan; no fixed period is promised.
Does retraining require new labels?
Not always, but there must be verifiable evaluation samples and label provenance. Compare the retrained version on a fixed evaluation set and old scenarios, then shadow/grey release; without evaluation and rollback, do not overwrite production.
Is an alert in a chat group enough?
No. It needs severity, channels, delivery confirmation, on-call owner, escalation and a handling record. Run a test with network failure, timeout and abnormal output.
How long should logs be kept?
Based on applicable law, contract and company policy. Article 23 of China's Cybersecurity Law (2025 amendment) mentions network logs of at least six months; business, input/output and change logs also need review under data-security, personal-information and industry requirements.
When should the system be stopped?
On a security/data incident, mass quality incident, sustained metric breach that cannot be degraded, vendor shutdown or regulatory/customer requirement, stop/roll back under the plan and keep records. If there is no rollback plan, do not go live.
Bottom line
For a Wuxi factory AI system after go-live, turn monitoring metrics, drift, retraining/changes, alerting/on-call, human fallback, log audit and rollback/stop into testable acceptance. Without an owner, rollback and stop conditions, it is not really live.
Method and limits
This article is public-standard, legal and editorial research, not an operations audit, security assessment, legal or compliance opinion for any company, project, model or dataset. It uses no unpublished client data, cases, accuracy figures, prices or contracts. Standards link to official pages; acceptance objects, metric suggestions, priorities and checklists are editorial judgement; specific metrics, thresholds, log retention, stop conditions and responsibilities must be confirmed by IT/production/quality/EHS/legal/vendors under the scenario, contract and applicable law. Standards and laws change; the current text, contract and professional advice govern.
Sources
Information is current to 2026-10-28; each public page governs its own scope, version and terms.
- NIST AI Risk Management Framework (AI RMF 1.0), NIST AI 100-1: NIST. Govern/Map/Measure/Manage and continuous risk management reference; voluntary framework, not a compliance certificate.
- NIST AI 600-1, Generative AI Profile: NIST. Generative AI risk, post-deployment monitoring and incident management reference; applicability depends on the scenario.
- Cybersecurity Law of the People's Republic of China (2025 amendment): CAC. Effective 2026-01-01; network operation security, monitoring records and log-retention reference; applicability depends on the law and company boundary.
- Data Security Law of the People's Republic of China: NPC. Data processing, security systems and risk assessment reference; not a direct operations acceptance standard.
- Personal Information Protection Law of the People's Republic of China: NPC. Personal information in logs, images, audio and forms reference; applicability depends on law and company policy.