syed7741/aegis-industrial-rag-assistant
Text Generation • Updated • 24
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values | module stringclasses 8
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values | title stringlengths 42 66 | industry stringclasses 4
values | equipment_id stringclasses 8
values | severity stringclasses 3
values | text stringlengths 286 443 | expected_action stringclasses 8
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value | synthetic bool 1
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|---|---|---|---|---|---|---|---|---|---|---|---|---|
AEGIS-0001 | train | Worker Safety | safety_procedure | Worker Safety - Manufacturing - CONV-02 - Scenario 1 | Manufacturing | CONV-02 | high | Before maintenance, isolate all energy sources, apply lockout/tagout, verify zero-energy state, and record the responsible technician. AEGIS AI should associate this record with asset CONV-02, classify the issue as high severity, and surface the recommended next action to the operator. | Stop unsafe work, secure the area, and follow the approved safety procedure. | [
"worker-safety",
"manufacturing",
"conv-02"
] | synthetic_aegis_training_sample | true |
AEGIS-0002 | train | Worker Safety | safety_procedure | Worker Safety - Manufacturing - ROBOT-ARM-04 - Scenario 2 | Manufacturing | ROBOT-ARM-04 | high | Before maintenance, isolate all energy sources, apply lockout/tagout, verify zero-energy state, and record the responsible technician. AEGIS AI should associate this record with asset ROBOT-ARM-04, classify the issue as high severity, and surface the recommended next action to the operator. The event should also be log... | Stop unsafe work, secure the area, and follow the approved safety procedure. | [
"worker-safety",
"manufacturing",
"robot-arm-04"
] | synthetic_aegis_training_sample | true |
AEGIS-0003 | train | Predictive Maintenance | maintenance_note | Predictive Maintenance - Manufacturing - ROBOT-ARM-04 - Scenario 1 | Manufacturing | ROBOT-ARM-04 | medium | A rising vibration trend combined with higher bearing temperature can indicate bearing wear or misalignment. Inspect lubrication, alignment, and mounting. AEGIS AI should associate this record with asset ROBOT-ARM-04, classify the issue as medium severity, and surface the recommended next action to the operator. | Inspect the asset, confirm the trend, and schedule maintenance before failure. | [
"predictive-maintenance",
"manufacturing",
"robot-arm-04"
] | synthetic_aegis_training_sample | true |
AEGIS-0004 | train | Predictive Maintenance | maintenance_note | Predictive Maintenance - Manufacturing - CONV-02 - Scenario 2 | Manufacturing | CONV-02 | medium | A rising vibration trend combined with higher bearing temperature can indicate bearing wear or misalignment. Inspect lubrication, alignment, and mounting. AEGIS AI should associate this record with asset CONV-02, classify the issue as medium severity, and surface the recommended next action to the operator. The event s... | Inspect the asset, confirm the trend, and schedule maintenance before failure. | [
"predictive-maintenance",
"manufacturing",
"conv-02"
] | synthetic_aegis_training_sample | true |
AEGIS-0005 | train | Robot Monitoring | robot_event | Robot Monitoring - Manufacturing - CONV-02 - Scenario 1 | Manufacturing | CONV-02 | medium | If a robot enters repeated protective-stop state, inspect safety scanners, obstruction zones, joint torque limits, and recent program changes before resetting. AEGIS AI should associate this record with asset CONV-02, classify the issue as medium severity, and surface the recommended next action to the operator. | Diagnose the protective stop and verify safe conditions before restart. | [
"robot-monitoring",
"manufacturing",
"conv-02"
] | synthetic_aegis_training_sample | true |
AEGIS-0006 | train | Robot Monitoring | robot_event | Robot Monitoring - Manufacturing - ROBOT-ARM-04 - Scenario 2 | Manufacturing | ROBOT-ARM-04 | medium | If a robot enters repeated protective-stop state, inspect safety scanners, obstruction zones, joint torque limits, and recent program changes before resetting. AEGIS AI should associate this record with asset ROBOT-ARM-04, classify the issue as medium severity, and surface the recommended next action to the operator. T... | Diagnose the protective stop and verify safe conditions before restart. | [
"robot-monitoring",
"manufacturing",
"robot-arm-04"
] | synthetic_aegis_training_sample | true |
AEGIS-0007 | train | Vision Inspection | inspection_guide | Vision Inspection - Manufacturing - ROBOT-ARM-04 - Scenario 1 | Manufacturing | ROBOT-ARM-04 | low | For visual inspection, reject parts with cracks, missing fasteners, severe surface defects, incorrect labels, or dimensions outside the approved tolerance. AEGIS AI should associate this record with asset ROBOT-ARM-04, classify the issue as low severity, and surface the recommended next action to the operator. | Quarantine the suspect item and send it for secondary inspection. | [
"vision-inspection",
"manufacturing",
"robot-arm-04"
] | synthetic_aegis_training_sample | true |
AEGIS-0008 | train | Vision Inspection | inspection_guide | Vision Inspection - Manufacturing - CONV-02 - Scenario 2 | Manufacturing | CONV-02 | low | For visual inspection, reject parts with cracks, missing fasteners, severe surface defects, incorrect labels, or dimensions outside the approved tolerance. AEGIS AI should associate this record with asset CONV-02, classify the issue as low severity, and surface the recommended next action to the operator. The event sho... | Quarantine the suspect item and send it for secondary inspection. | [
"vision-inspection",
"manufacturing",
"conv-02"
] | synthetic_aegis_training_sample | true |
AEGIS-0009 | validation | AI Alerts | alert_playbook | AI Alerts - Manufacturing - CONV-02 - Scenario 1 | Manufacturing | CONV-02 | high | Critical alerts require acknowledgement, confirmation of the affected asset, immediate risk assessment, and escalation to the shift supervisor when safety or production is threatened. AEGIS AI should associate this record with asset CONV-02, classify the issue as high severity, and surface the recommended next action t... | Acknowledge, assess severity, and escalate according to the alert playbook. | [
"ai-alerts",
"manufacturing",
"conv-02"
] | synthetic_aegis_training_sample | true |
AEGIS-0010 | test | AI Alerts | alert_playbook | AI Alerts - Manufacturing - ROBOT-ARM-04 - Scenario 2 | Manufacturing | ROBOT-ARM-04 | high | Critical alerts require acknowledgement, confirmation of the affected asset, immediate risk assessment, and escalation to the shift supervisor when safety or production is threatened. AEGIS AI should associate this record with asset ROBOT-ARM-04, classify the issue as high severity, and surface the recommended next act... | Acknowledge, assess severity, and escalate according to the alert playbook. | [
"ai-alerts",
"manufacturing",
"robot-arm-04"
] | synthetic_aegis_training_sample | true |
AEGIS-0011 | train | Workflow Automation | workflow_sop | Workflow Automation - Manufacturing - ROBOT-ARM-04 - Scenario 1 | Manufacturing | ROBOT-ARM-04 | medium | When an anomaly is confirmed, create a work order, assign an owner, attach evidence, set priority, and track the issue until verification and closure. AEGIS AI should associate this record with asset ROBOT-ARM-04, classify the issue as medium severity, and surface the recommended next action to the operator. | Create and track a work order with owner, priority, evidence, and closure verification. | [
"workflow-automation",
"manufacturing",
"robot-arm-04"
] | synthetic_aegis_training_sample | true |
AEGIS-0012 | train | Workflow Automation | workflow_sop | Workflow Automation - Manufacturing - CONV-02 - Scenario 2 | Manufacturing | CONV-02 | medium | When an anomaly is confirmed, create a work order, assign an owner, attach evidence, set priority, and track the issue until verification and closure. AEGIS AI should associate this record with asset CONV-02, classify the issue as medium severity, and surface the recommended next action to the operator. The event shoul... | Create and track a work order with owner, priority, evidence, and closure verification. | [
"workflow-automation",
"manufacturing",
"conv-02"
] | synthetic_aegis_training_sample | true |
AEGIS-0013 | train | Document Assistant | knowledge_article | Document Assistant - Manufacturing - CONV-02 - Scenario 1 | Manufacturing | CONV-02 | low | When answering technical questions, use the latest approved SOP or manual, cite the source document, distinguish requirements from recommendations, and state uncertainty when evidence is incomplete. AEGIS AI should associate this record with asset CONV-02, classify the issue as low severity, and surface the recommended... | Answer only from approved evidence and cite the source. | [
"document-assistant",
"manufacturing",
"conv-02"
] | synthetic_aegis_training_sample | true |
AEGIS-0014 | train | Document Assistant | knowledge_article | Document Assistant - Manufacturing - ROBOT-ARM-04 - Scenario 2 | Manufacturing | ROBOT-ARM-04 | low | When answering technical questions, use the latest approved SOP or manual, cite the source document, distinguish requirements from recommendations, and state uncertainty when evidence is incomplete. AEGIS AI should associate this record with asset ROBOT-ARM-04, classify the issue as low severity, and surface the recomm... | Answer only from approved evidence and cite the source. | [
"document-assistant",
"manufacturing",
"robot-arm-04"
] | synthetic_aegis_training_sample | true |
AEGIS-0015 | train | Factory Status | operations_note | Factory Status - Manufacturing - ROBOT-ARM-04 - Scenario 1 | Manufacturing | ROBOT-ARM-04 | medium | A production line should be marked degraded when throughput drops materially below target, multiple assets are in warning state, or quality rejection rate exceeds the normal operating band. AEGIS AI should associate this record with asset ROBOT-ARM-04, classify the issue as medium severity, and surface the recommended ... | Review line KPIs and investigate the assets driving the degraded status. | [
"factory-status",
"manufacturing",
"robot-arm-04"
] | synthetic_aegis_training_sample | true |
AEGIS-0016 | train | Factory Status | operations_note | Factory Status - Manufacturing - CONV-02 - Scenario 2 | Manufacturing | CONV-02 | medium | A production line should be marked degraded when throughput drops materially below target, multiple assets are in warning state, or quality rejection rate exceeds the normal operating band. AEGIS AI should associate this record with asset CONV-02, classify the issue as medium severity, and surface the recommended next ... | Review line KPIs and investigate the assets driving the degraded status. | [
"factory-status",
"manufacturing",
"conv-02"
] | synthetic_aegis_training_sample | true |
AEGIS-0017 | train | Worker Safety | safety_procedure | Worker Safety - Oil & Gas - VALVE-12 - Scenario 1 | Oil & Gas | VALVE-12 | high | Before maintenance, isolate all energy sources, apply lockout/tagout, verify zero-energy state, and record the responsible technician. AEGIS AI should associate this record with asset VALVE-12, classify the issue as high severity, and surface the recommended next action to the operator. | Stop unsafe work, secure the area, and follow the approved safety procedure. | [
"worker-safety",
"oil-gas",
"valve-12"
] | synthetic_aegis_training_sample | true |
AEGIS-0018 | train | Worker Safety | safety_procedure | Worker Safety - Oil & Gas - TANK-14 - Scenario 2 | Oil & Gas | TANK-14 | high | Before maintenance, isolate all energy sources, apply lockout/tagout, verify zero-energy state, and record the responsible technician. AEGIS AI should associate this record with asset TANK-14, classify the issue as high severity, and surface the recommended next action to the operator. The event should also be logged w... | Stop unsafe work, secure the area, and follow the approved safety procedure. | [
"worker-safety",
"oil-gas",
"tank-14"
] | synthetic_aegis_training_sample | true |
AEGIS-0019 | validation | Predictive Maintenance | maintenance_note | Predictive Maintenance - Oil & Gas - TANK-14 - Scenario 1 | Oil & Gas | TANK-14 | medium | A rising vibration trend combined with higher bearing temperature can indicate bearing wear or misalignment. Inspect lubrication, alignment, and mounting. AEGIS AI should associate this record with asset TANK-14, classify the issue as medium severity, and surface the recommended next action to the operator. | Inspect the asset, confirm the trend, and schedule maintenance before failure. | [
"predictive-maintenance",
"oil-gas",
"tank-14"
] | synthetic_aegis_training_sample | true |
AEGIS-0020 | test | Predictive Maintenance | maintenance_note | Predictive Maintenance - Oil & Gas - VALVE-12 - Scenario 2 | Oil & Gas | VALVE-12 | medium | A rising vibration trend combined with higher bearing temperature can indicate bearing wear or misalignment. Inspect lubrication, alignment, and mounting. AEGIS AI should associate this record with asset VALVE-12, classify the issue as medium severity, and surface the recommended next action to the operator. The event ... | Inspect the asset, confirm the trend, and schedule maintenance before failure. | [
"predictive-maintenance",
"oil-gas",
"valve-12"
] | synthetic_aegis_training_sample | true |
AEGIS-0021 | train | Robot Monitoring | robot_event | Robot Monitoring - Oil & Gas - VALVE-12 - Scenario 1 | Oil & Gas | VALVE-12 | medium | If a robot enters repeated protective-stop state, inspect safety scanners, obstruction zones, joint torque limits, and recent program changes before resetting. AEGIS AI should associate this record with asset VALVE-12, classify the issue as medium severity, and surface the recommended next action to the operator. | Diagnose the protective stop and verify safe conditions before restart. | [
"robot-monitoring",
"oil-gas",
"valve-12"
] | synthetic_aegis_training_sample | true |
AEGIS-0022 | train | Robot Monitoring | robot_event | Robot Monitoring - Oil & Gas - TANK-14 - Scenario 2 | Oil & Gas | TANK-14 | medium | If a robot enters repeated protective-stop state, inspect safety scanners, obstruction zones, joint torque limits, and recent program changes before resetting. AEGIS AI should associate this record with asset TANK-14, classify the issue as medium severity, and surface the recommended next action to the operator. The ev... | Diagnose the protective stop and verify safe conditions before restart. | [
"robot-monitoring",
"oil-gas",
"tank-14"
] | synthetic_aegis_training_sample | true |
AEGIS-0023 | train | Vision Inspection | inspection_guide | Vision Inspection - Oil & Gas - TANK-14 - Scenario 1 | Oil & Gas | TANK-14 | low | For visual inspection, reject parts with cracks, missing fasteners, severe surface defects, incorrect labels, or dimensions outside the approved tolerance. AEGIS AI should associate this record with asset TANK-14, classify the issue as low severity, and surface the recommended next action to the operator. | Quarantine the suspect item and send it for secondary inspection. | [
"vision-inspection",
"oil-gas",
"tank-14"
] | synthetic_aegis_training_sample | true |
AEGIS-0024 | train | Vision Inspection | inspection_guide | Vision Inspection - Oil & Gas - VALVE-12 - Scenario 2 | Oil & Gas | VALVE-12 | low | For visual inspection, reject parts with cracks, missing fasteners, severe surface defects, incorrect labels, or dimensions outside the approved tolerance. AEGIS AI should associate this record with asset VALVE-12, classify the issue as low severity, and surface the recommended next action to the operator. The event sh... | Quarantine the suspect item and send it for secondary inspection. | [
"vision-inspection",
"oil-gas",
"valve-12"
] | synthetic_aegis_training_sample | true |
AEGIS-0025 | train | AI Alerts | alert_playbook | AI Alerts - Oil & Gas - VALVE-12 - Scenario 1 | Oil & Gas | VALVE-12 | high | Critical alerts require acknowledgement, confirmation of the affected asset, immediate risk assessment, and escalation to the shift supervisor when safety or production is threatened. AEGIS AI should associate this record with asset VALVE-12, classify the issue as high severity, and surface the recommended next action ... | Acknowledge, assess severity, and escalate according to the alert playbook. | [
"ai-alerts",
"oil-gas",
"valve-12"
] | synthetic_aegis_training_sample | true |
AEGIS-0026 | train | AI Alerts | alert_playbook | AI Alerts - Oil & Gas - TANK-14 - Scenario 2 | Oil & Gas | TANK-14 | high | Critical alerts require acknowledgement, confirmation of the affected asset, immediate risk assessment, and escalation to the shift supervisor when safety or production is threatened. AEGIS AI should associate this record with asset TANK-14, classify the issue as high severity, and surface the recommended next action t... | Acknowledge, assess severity, and escalate according to the alert playbook. | [
"ai-alerts",
"oil-gas",
"tank-14"
] | synthetic_aegis_training_sample | true |
AEGIS-0027 | train | Workflow Automation | workflow_sop | Workflow Automation - Oil & Gas - TANK-14 - Scenario 1 | Oil & Gas | TANK-14 | medium | When an anomaly is confirmed, create a work order, assign an owner, attach evidence, set priority, and track the issue until verification and closure. AEGIS AI should associate this record with asset TANK-14, classify the issue as medium severity, and surface the recommended next action to the operator. | Create and track a work order with owner, priority, evidence, and closure verification. | [
"workflow-automation",
"oil-gas",
"tank-14"
] | synthetic_aegis_training_sample | true |
AEGIS-0028 | train | Workflow Automation | workflow_sop | Workflow Automation - Oil & Gas - VALVE-12 - Scenario 2 | Oil & Gas | VALVE-12 | medium | When an anomaly is confirmed, create a work order, assign an owner, attach evidence, set priority, and track the issue until verification and closure. AEGIS AI should associate this record with asset VALVE-12, classify the issue as medium severity, and surface the recommended next action to the operator. The event shou... | Create and track a work order with owner, priority, evidence, and closure verification. | [
"workflow-automation",
"oil-gas",
"valve-12"
] | synthetic_aegis_training_sample | true |
AEGIS-0029 | validation | Document Assistant | knowledge_article | Document Assistant - Oil & Gas - VALVE-12 - Scenario 1 | Oil & Gas | VALVE-12 | low | When answering technical questions, use the latest approved SOP or manual, cite the source document, distinguish requirements from recommendations, and state uncertainty when evidence is incomplete. AEGIS AI should associate this record with asset VALVE-12, classify the issue as low severity, and surface the recommende... | Answer only from approved evidence and cite the source. | [
"document-assistant",
"oil-gas",
"valve-12"
] | synthetic_aegis_training_sample | true |
AEGIS-0030 | test | Document Assistant | knowledge_article | Document Assistant - Oil & Gas - TANK-14 - Scenario 2 | Oil & Gas | TANK-14 | low | When answering technical questions, use the latest approved SOP or manual, cite the source document, distinguish requirements from recommendations, and state uncertainty when evidence is incomplete. AEGIS AI should associate this record with asset TANK-14, classify the issue as low severity, and surface the recommended... | Answer only from approved evidence and cite the source. | [
"document-assistant",
"oil-gas",
"tank-14"
] | synthetic_aegis_training_sample | true |
AEGIS-0031 | train | Factory Status | operations_note | Factory Status - Oil & Gas - TANK-14 - Scenario 1 | Oil & Gas | TANK-14 | medium | A production line should be marked degraded when throughput drops materially below target, multiple assets are in warning state, or quality rejection rate exceeds the normal operating band. AEGIS AI should associate this record with asset TANK-14, classify the issue as medium severity, and surface the recommended next ... | Review line KPIs and investigate the assets driving the degraded status. | [
"factory-status",
"oil-gas",
"tank-14"
] | synthetic_aegis_training_sample | true |
AEGIS-0032 | train | Factory Status | operations_note | Factory Status - Oil & Gas - VALVE-12 - Scenario 2 | Oil & Gas | VALVE-12 | medium | A production line should be marked degraded when throughput drops materially below target, multiple assets are in warning state, or quality rejection rate exceeds the normal operating band. AEGIS AI should associate this record with asset VALVE-12, classify the issue as medium severity, and surface the recommended next... | Review line KPIs and investigate the assets driving the degraded status. | [
"factory-status",
"oil-gas",
"valve-12"
] | synthetic_aegis_training_sample | true |
AEGIS-0033 | train | Worker Safety | safety_procedure | Worker Safety - Warehouse - SORTER-22 - Scenario 1 | Warehouse | SORTER-22 | high | Before maintenance, isolate all energy sources, apply lockout/tagout, verify zero-energy state, and record the responsible technician. AEGIS AI should associate this record with asset SORTER-22, classify the issue as high severity, and surface the recommended next action to the operator. | Stop unsafe work, secure the area, and follow the approved safety procedure. | [
"worker-safety",
"warehouse",
"sorter-22"
] | synthetic_aegis_training_sample | true |
AEGIS-0034 | train | Worker Safety | safety_procedure | Worker Safety - Warehouse - DOCK-24 - Scenario 2 | Warehouse | DOCK-24 | high | Before maintenance, isolate all energy sources, apply lockout/tagout, verify zero-energy state, and record the responsible technician. AEGIS AI should associate this record with asset DOCK-24, classify the issue as high severity, and surface the recommended next action to the operator. The event should also be logged w... | Stop unsafe work, secure the area, and follow the approved safety procedure. | [
"worker-safety",
"warehouse",
"dock-24"
] | synthetic_aegis_training_sample | true |
AEGIS-0035 | train | Predictive Maintenance | maintenance_note | Predictive Maintenance - Warehouse - DOCK-24 - Scenario 1 | Warehouse | DOCK-24 | medium | A rising vibration trend combined with higher bearing temperature can indicate bearing wear or misalignment. Inspect lubrication, alignment, and mounting. AEGIS AI should associate this record with asset DOCK-24, classify the issue as medium severity, and surface the recommended next action to the operator. | Inspect the asset, confirm the trend, and schedule maintenance before failure. | [
"predictive-maintenance",
"warehouse",
"dock-24"
] | synthetic_aegis_training_sample | true |
AEGIS-0036 | train | Predictive Maintenance | maintenance_note | Predictive Maintenance - Warehouse - SORTER-22 - Scenario 2 | Warehouse | SORTER-22 | medium | A rising vibration trend combined with higher bearing temperature can indicate bearing wear or misalignment. Inspect lubrication, alignment, and mounting. AEGIS AI should associate this record with asset SORTER-22, classify the issue as medium severity, and surface the recommended next action to the operator. The event... | Inspect the asset, confirm the trend, and schedule maintenance before failure. | [
"predictive-maintenance",
"warehouse",
"sorter-22"
] | synthetic_aegis_training_sample | true |
AEGIS-0037 | train | Robot Monitoring | robot_event | Robot Monitoring - Warehouse - SORTER-22 - Scenario 1 | Warehouse | SORTER-22 | medium | If a robot enters repeated protective-stop state, inspect safety scanners, obstruction zones, joint torque limits, and recent program changes before resetting. AEGIS AI should associate this record with asset SORTER-22, classify the issue as medium severity, and surface the recommended next action to the operator. | Diagnose the protective stop and verify safe conditions before restart. | [
"robot-monitoring",
"warehouse",
"sorter-22"
] | synthetic_aegis_training_sample | true |
AEGIS-0038 | train | Robot Monitoring | robot_event | Robot Monitoring - Warehouse - DOCK-24 - Scenario 2 | Warehouse | DOCK-24 | medium | If a robot enters repeated protective-stop state, inspect safety scanners, obstruction zones, joint torque limits, and recent program changes before resetting. AEGIS AI should associate this record with asset DOCK-24, classify the issue as medium severity, and surface the recommended next action to the operator. The ev... | Diagnose the protective stop and verify safe conditions before restart. | [
"robot-monitoring",
"warehouse",
"dock-24"
] | synthetic_aegis_training_sample | true |
AEGIS-0039 | validation | Vision Inspection | inspection_guide | Vision Inspection - Warehouse - DOCK-24 - Scenario 1 | Warehouse | DOCK-24 | low | For visual inspection, reject parts with cracks, missing fasteners, severe surface defects, incorrect labels, or dimensions outside the approved tolerance. AEGIS AI should associate this record with asset DOCK-24, classify the issue as low severity, and surface the recommended next action to the operator. | Quarantine the suspect item and send it for secondary inspection. | [
"vision-inspection",
"warehouse",
"dock-24"
] | synthetic_aegis_training_sample | true |
AEGIS-0040 | test | Vision Inspection | inspection_guide | Vision Inspection - Warehouse - SORTER-22 - Scenario 2 | Warehouse | SORTER-22 | low | For visual inspection, reject parts with cracks, missing fasteners, severe surface defects, incorrect labels, or dimensions outside the approved tolerance. AEGIS AI should associate this record with asset SORTER-22, classify the issue as low severity, and surface the recommended next action to the operator. The event s... | Quarantine the suspect item and send it for secondary inspection. | [
"vision-inspection",
"warehouse",
"sorter-22"
] | synthetic_aegis_training_sample | true |
AEGIS-0041 | train | AI Alerts | alert_playbook | AI Alerts - Warehouse - SORTER-22 - Scenario 1 | Warehouse | SORTER-22 | high | Critical alerts require acknowledgement, confirmation of the affected asset, immediate risk assessment, and escalation to the shift supervisor when safety or production is threatened. AEGIS AI should associate this record with asset SORTER-22, classify the issue as high severity, and surface the recommended next action... | Acknowledge, assess severity, and escalate according to the alert playbook. | [
"ai-alerts",
"warehouse",
"sorter-22"
] | synthetic_aegis_training_sample | true |
AEGIS-0042 | train | AI Alerts | alert_playbook | AI Alerts - Warehouse - DOCK-24 - Scenario 2 | Warehouse | DOCK-24 | high | Critical alerts require acknowledgement, confirmation of the affected asset, immediate risk assessment, and escalation to the shift supervisor when safety or production is threatened. AEGIS AI should associate this record with asset DOCK-24, classify the issue as high severity, and surface the recommended next action t... | Acknowledge, assess severity, and escalate according to the alert playbook. | [
"ai-alerts",
"warehouse",
"dock-24"
] | synthetic_aegis_training_sample | true |
AEGIS-0043 | train | Workflow Automation | workflow_sop | Workflow Automation - Warehouse - DOCK-24 - Scenario 1 | Warehouse | DOCK-24 | medium | When an anomaly is confirmed, create a work order, assign an owner, attach evidence, set priority, and track the issue until verification and closure. AEGIS AI should associate this record with asset DOCK-24, classify the issue as medium severity, and surface the recommended next action to the operator. | Create and track a work order with owner, priority, evidence, and closure verification. | [
"workflow-automation",
"warehouse",
"dock-24"
] | synthetic_aegis_training_sample | true |
AEGIS-0044 | train | Workflow Automation | workflow_sop | Workflow Automation - Warehouse - SORTER-22 - Scenario 2 | Warehouse | SORTER-22 | medium | When an anomaly is confirmed, create a work order, assign an owner, attach evidence, set priority, and track the issue until verification and closure. AEGIS AI should associate this record with asset SORTER-22, classify the issue as medium severity, and surface the recommended next action to the operator. The event sho... | Create and track a work order with owner, priority, evidence, and closure verification. | [
"workflow-automation",
"warehouse",
"sorter-22"
] | synthetic_aegis_training_sample | true |
AEGIS-0045 | train | Document Assistant | knowledge_article | Document Assistant - Warehouse - SORTER-22 - Scenario 1 | Warehouse | SORTER-22 | low | When answering technical questions, use the latest approved SOP or manual, cite the source document, distinguish requirements from recommendations, and state uncertainty when evidence is incomplete. AEGIS AI should associate this record with asset SORTER-22, classify the issue as low severity, and surface the recommend... | Answer only from approved evidence and cite the source. | [
"document-assistant",
"warehouse",
"sorter-22"
] | synthetic_aegis_training_sample | true |
AEGIS-0046 | train | Document Assistant | knowledge_article | Document Assistant - Warehouse - DOCK-24 - Scenario 2 | Warehouse | DOCK-24 | low | When answering technical questions, use the latest approved SOP or manual, cite the source document, distinguish requirements from recommendations, and state uncertainty when evidence is incomplete. AEGIS AI should associate this record with asset DOCK-24, classify the issue as low severity, and surface the recommended... | Answer only from approved evidence and cite the source. | [
"document-assistant",
"warehouse",
"dock-24"
] | synthetic_aegis_training_sample | true |
AEGIS-0047 | train | Factory Status | operations_note | Factory Status - Warehouse - DOCK-24 - Scenario 1 | Warehouse | DOCK-24 | medium | A production line should be marked degraded when throughput drops materially below target, multiple assets are in warning state, or quality rejection rate exceeds the normal operating band. AEGIS AI should associate this record with asset DOCK-24, classify the issue as medium severity, and surface the recommended next ... | Review line KPIs and investigate the assets driving the degraded status. | [
"factory-status",
"warehouse",
"dock-24"
] | synthetic_aegis_training_sample | true |
AEGIS-0048 | train | Factory Status | operations_note | Factory Status - Warehouse - SORTER-22 - Scenario 2 | Warehouse | SORTER-22 | medium | A production line should be marked degraded when throughput drops materially below target, multiple assets are in warning state, or quality rejection rate exceeds the normal operating band. AEGIS AI should associate this record with asset SORTER-22, classify the issue as medium severity, and surface the recommended nex... | Review line KPIs and investigate the assets driving the degraded status. | [
"factory-status",
"warehouse",
"sorter-22"
] | synthetic_aegis_training_sample | true |
AEGIS-0049 | validation | Worker Safety | safety_procedure | Worker Safety - Robotics - VISION-32 - Scenario 1 | Robotics | VISION-32 | high | Before maintenance, isolate all energy sources, apply lockout/tagout, verify zero-energy state, and record the responsible technician. AEGIS AI should associate this record with asset VISION-32, classify the issue as high severity, and surface the recommended next action to the operator. | Stop unsafe work, secure the area, and follow the approved safety procedure. | [
"worker-safety",
"robotics",
"vision-32"
] | synthetic_aegis_training_sample | true |
AEGIS-0050 | test | Worker Safety | safety_procedure | Worker Safety - Robotics - AGV-34 - Scenario 2 | Robotics | AGV-34 | high | Before maintenance, isolate all energy sources, apply lockout/tagout, verify zero-energy state, and record the responsible technician. AEGIS AI should associate this record with asset AGV-34, classify the issue as high severity, and surface the recommended next action to the operator. The event should also be logged wi... | Stop unsafe work, secure the area, and follow the approved safety procedure. | [
"worker-safety",
"robotics",
"agv-34"
] | synthetic_aegis_training_sample | true |
AEGIS-0051 | train | Predictive Maintenance | maintenance_note | Predictive Maintenance - Robotics - AGV-34 - Scenario 1 | Robotics | AGV-34 | medium | A rising vibration trend combined with higher bearing temperature can indicate bearing wear or misalignment. Inspect lubrication, alignment, and mounting. AEGIS AI should associate this record with asset AGV-34, classify the issue as medium severity, and surface the recommended next action to the operator. | Inspect the asset, confirm the trend, and schedule maintenance before failure. | [
"predictive-maintenance",
"robotics",
"agv-34"
] | synthetic_aegis_training_sample | true |
AEGIS-0052 | train | Predictive Maintenance | maintenance_note | Predictive Maintenance - Robotics - VISION-32 - Scenario 2 | Robotics | VISION-32 | medium | A rising vibration trend combined with higher bearing temperature can indicate bearing wear or misalignment. Inspect lubrication, alignment, and mounting. AEGIS AI should associate this record with asset VISION-32, classify the issue as medium severity, and surface the recommended next action to the operator. The event... | Inspect the asset, confirm the trend, and schedule maintenance before failure. | [
"predictive-maintenance",
"robotics",
"vision-32"
] | synthetic_aegis_training_sample | true |
AEGIS-0053 | train | Robot Monitoring | robot_event | Robot Monitoring - Robotics - VISION-32 - Scenario 1 | Robotics | VISION-32 | medium | If a robot enters repeated protective-stop state, inspect safety scanners, obstruction zones, joint torque limits, and recent program changes before resetting. AEGIS AI should associate this record with asset VISION-32, classify the issue as medium severity, and surface the recommended next action to the operator. | Diagnose the protective stop and verify safe conditions before restart. | [
"robot-monitoring",
"robotics",
"vision-32"
] | synthetic_aegis_training_sample | true |
AEGIS-0054 | train | Robot Monitoring | robot_event | Robot Monitoring - Robotics - AGV-34 - Scenario 2 | Robotics | AGV-34 | medium | If a robot enters repeated protective-stop state, inspect safety scanners, obstruction zones, joint torque limits, and recent program changes before resetting. AEGIS AI should associate this record with asset AGV-34, classify the issue as medium severity, and surface the recommended next action to the operator. The eve... | Diagnose the protective stop and verify safe conditions before restart. | [
"robot-monitoring",
"robotics",
"agv-34"
] | synthetic_aegis_training_sample | true |
AEGIS-0055 | train | Vision Inspection | inspection_guide | Vision Inspection - Robotics - AGV-34 - Scenario 1 | Robotics | AGV-34 | low | For visual inspection, reject parts with cracks, missing fasteners, severe surface defects, incorrect labels, or dimensions outside the approved tolerance. AEGIS AI should associate this record with asset AGV-34, classify the issue as low severity, and surface the recommended next action to the operator. | Quarantine the suspect item and send it for secondary inspection. | [
"vision-inspection",
"robotics",
"agv-34"
] | synthetic_aegis_training_sample | true |
AEGIS-0056 | train | Vision Inspection | inspection_guide | Vision Inspection - Robotics - VISION-32 - Scenario 2 | Robotics | VISION-32 | low | For visual inspection, reject parts with cracks, missing fasteners, severe surface defects, incorrect labels, or dimensions outside the approved tolerance. AEGIS AI should associate this record with asset VISION-32, classify the issue as low severity, and surface the recommended next action to the operator. The event s... | Quarantine the suspect item and send it for secondary inspection. | [
"vision-inspection",
"robotics",
"vision-32"
] | synthetic_aegis_training_sample | true |
AEGIS-0057 | train | AI Alerts | alert_playbook | AI Alerts - Robotics - VISION-32 - Scenario 1 | Robotics | VISION-32 | high | Critical alerts require acknowledgement, confirmation of the affected asset, immediate risk assessment, and escalation to the shift supervisor when safety or production is threatened. AEGIS AI should associate this record with asset VISION-32, classify the issue as high severity, and surface the recommended next action... | Acknowledge, assess severity, and escalate according to the alert playbook. | [
"ai-alerts",
"robotics",
"vision-32"
] | synthetic_aegis_training_sample | true |
AEGIS-0058 | train | AI Alerts | alert_playbook | AI Alerts - Robotics - AGV-34 - Scenario 2 | Robotics | AGV-34 | high | Critical alerts require acknowledgement, confirmation of the affected asset, immediate risk assessment, and escalation to the shift supervisor when safety or production is threatened. AEGIS AI should associate this record with asset AGV-34, classify the issue as high severity, and surface the recommended next action to... | Acknowledge, assess severity, and escalate according to the alert playbook. | [
"ai-alerts",
"robotics",
"agv-34"
] | synthetic_aegis_training_sample | true |
AEGIS-0059 | validation | Workflow Automation | workflow_sop | Workflow Automation - Robotics - AGV-34 - Scenario 1 | Robotics | AGV-34 | medium | When an anomaly is confirmed, create a work order, assign an owner, attach evidence, set priority, and track the issue until verification and closure. AEGIS AI should associate this record with asset AGV-34, classify the issue as medium severity, and surface the recommended next action to the operator. | Create and track a work order with owner, priority, evidence, and closure verification. | [
"workflow-automation",
"robotics",
"agv-34"
] | synthetic_aegis_training_sample | true |
AEGIS-0060 | test | Workflow Automation | workflow_sop | Workflow Automation - Robotics - VISION-32 - Scenario 2 | Robotics | VISION-32 | medium | When an anomaly is confirmed, create a work order, assign an owner, attach evidence, set priority, and track the issue until verification and closure. AEGIS AI should associate this record with asset VISION-32, classify the issue as medium severity, and surface the recommended next action to the operator. The event sho... | Create and track a work order with owner, priority, evidence, and closure verification. | [
"workflow-automation",
"robotics",
"vision-32"
] | synthetic_aegis_training_sample | true |
AEGIS-0061 | train | Document Assistant | knowledge_article | Document Assistant - Robotics - VISION-32 - Scenario 1 | Robotics | VISION-32 | low | When answering technical questions, use the latest approved SOP or manual, cite the source document, distinguish requirements from recommendations, and state uncertainty when evidence is incomplete. AEGIS AI should associate this record with asset VISION-32, classify the issue as low severity, and surface the recommend... | Answer only from approved evidence and cite the source. | [
"document-assistant",
"robotics",
"vision-32"
] | synthetic_aegis_training_sample | true |
AEGIS-0062 | train | Document Assistant | knowledge_article | Document Assistant - Robotics - AGV-34 - Scenario 2 | Robotics | AGV-34 | low | When answering technical questions, use the latest approved SOP or manual, cite the source document, distinguish requirements from recommendations, and state uncertainty when evidence is incomplete. AEGIS AI should associate this record with asset AGV-34, classify the issue as low severity, and surface the recommended ... | Answer only from approved evidence and cite the source. | [
"document-assistant",
"robotics",
"agv-34"
] | synthetic_aegis_training_sample | true |
AEGIS-0063 | train | Factory Status | operations_note | Factory Status - Robotics - AGV-34 - Scenario 1 | Robotics | AGV-34 | medium | A production line should be marked degraded when throughput drops materially below target, multiple assets are in warning state, or quality rejection rate exceeds the normal operating band. AEGIS AI should associate this record with asset AGV-34, classify the issue as medium severity, and surface the recommended next a... | Review line KPIs and investigate the assets driving the degraded status. | [
"factory-status",
"robotics",
"agv-34"
] | synthetic_aegis_training_sample | true |
AEGIS-0064 | train | Factory Status | operations_note | Factory Status - Robotics - VISION-32 - Scenario 2 | Robotics | VISION-32 | medium | A production line should be marked degraded when throughput drops materially below target, multiple assets are in warning state, or quality rejection rate exceeds the normal operating band. AEGIS AI should associate this record with asset VISION-32, classify the issue as medium severity, and surface the recommended nex... | Review line KPIs and investigate the assets driving the degraded status. | [
"factory-status",
"robotics",
"vision-32"
] | synthetic_aegis_training_sample | true |
AEGIS Industrial AI Dataset
A synthetic industrial operations dataset created for AEGIS AI, an end-to-end industrial artificial intelligence platform.
The dataset is designed to support experimentation with:
- Retrieval-Augmented Generation (RAG)
- Semantic Search
- Industrial AI Assistants
- Document Intelligence
- Predictive Maintenance
- Worker Safety
- Robot Monitoring
- Computer Vision / Inspection
- AI Alerts
- Workflow Automation
Project Overview
AEGIS AI is an industrial intelligence platform designed to demonstrate how modern AI systems can combine operational data, semantic retrieval, local language models, computer vision, and workflow automation.
The project is being developed as an end-to-end AI engineering portfolio project using:
- Python
- FastAPI
- React
- TypeScript
- Hugging Face Datasets
- Sentence Transformers
- Vector Search
- Retrieval-Augmented Generation
- Local Language Models
- PostgreSQL
- Computer Vision
- AI Workflow Automation
The objective is to demonstrate the architecture of a real AI application rather than only an isolated machine-learning notebook.
Dataset Structure
The dataset currently contains 64 synthetic industrial records covering multiple operational domains.
Each record contains structured metadata together with natural-language operational knowledge that can be indexed and retrieved by an AI system.
Example fields include:
| Field | Description |
|---|---|
id |
Unique AEGIS record identifier |
split |
Dataset split |
module |
AEGIS AI functional module |
document_type |
Type of industrial record |
title |
Human-readable record title |
industry |
Industrial sector |
equipment_id |
Associated machine or asset |
severity |
Operational severity |
text |
Main industrial knowledge content |
expected_action |
Recommended operational response |
tags |
Search/retrieval metadata |
source_type |
Record source classification |
synthetic |
Indicates synthetic data |
AEGIS Modules
The dataset includes records from eight major AEGIS AI modules.
Worker Safety
Industrial safety procedures, unsafe operating conditions, lockout/tagout scenarios, and recommended safety responses.
Predictive Maintenance
Equipment degradation, maintenance observations, abnormal operational behavior, and recommended maintenance actions.
Robot Monitoring
Industrial robot events, robot status information, operating abnormalities, and robot-related alerts.
Vision Inspection
Synthetic visual inspection findings representing manufacturing or industrial quality-control events.
AI Alerts
Machine and operational alerts designed for automated AI prioritization.
Workflow Automation
Operational events that can trigger automated industrial workflows.
Document Assistant
Knowledge records designed for retrieval, semantic search, and AI-assisted question answering.
Factory Status
Operational status information representing industrial facilities and equipment.
Industries Represented
The synthetic records cover multiple industrial environments including:
- Manufacturing
- Oil & Gas
- Warehousing / Logistics
- Robotics
Using the Dataset
Install Hugging Face Datasets:
pip install datasets
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