nwhite-systems/nwhite-ai-operations-intent-classifier
Text Classification • Updated • 10
id stringlengths 11 11 | user_request stringlengths 253 406 | intent stringclasses 8
values | operational_domain stringclasses 8
values | risk_level stringclasses 3
values | requires_human_review bool 2
classes | expected_output stringclasses 8
values | notes stringclasses 24
values |
|---|---|---|---|---|---|---|---|
NW-AIO-0001 | Design a controlled workflow for triaging new claim notifications at a regional insurer. When a synthetic claim-intake form entering the shared queue, capture an audit event, assign the next step to the claims team leader, and pause before approving, declining or pricing an insurance claim. Include retry, expiry and ma... | workflow_automation | insurance_operations | medium | true | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the claims team leader. |
NW-AIO-0003 | Create a repeatable operating sequence for monitoring complaint resolution that shows the trigger, validation checks, responsible roles, exception route and completion evidence. The sequence must support complaints resolution staff without allowing the system to perform accepting liability or promising a financial reme... | workflow_automation | insurance_operations | medium | true | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the complaints resolution manager. |
NW-AIO-0005 | Map an automation that moves a verified record from the demonstration registration form to the mock attendance planner for a community learning centre. It must prevent duplicate work, preserve the original submission, notify the programme administrator of failures, and require explicit approval before overriding capaci... | workflow_automation | education_services | low | false | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the programme administrator. |
NW-AIO-0007 | Design a controlled workflow for co-ordinating production hand-offs at an independent media studio. When a synthetic production brief moving from planning to editing, capture an audit event, assign the next step to the production manager, and pause before publishing unverified material or clearing third-party rights. I... | workflow_automation | media_and_creative | low | false | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the production manager. |
NW-AIO-0008 | Map an automation that moves a verified record from the demonstration pitch queue to the mock commissioning register for a public-interest newsroom. It must prevent duplicate work, preserve the original submission, notify the commissioning editor of failures, and require explicit approval before publishing allegations ... | workflow_automation | media_and_creative | low | false | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the commissioning editor. |
NW-AIO-0013 | Design a controlled workflow for routing new service enquiries at a small professional-services firm. When a synthetic service enquiry passing the basic completeness check, capture an audit event, assign the next step to the client services manager, and pause before accepting contractual terms or promising an outcome. ... | workflow_automation | professional_services_sme | low | false | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the client services manager. |
NW-AIO-0015 | Create a repeatable operating sequence for co-ordinating service incidents that shows the trigger, validation checks, responsible roles, exception route and completion evidence. The sequence must support service desk staff without allowing the system to perform changing production access or promising a restoration time... | workflow_automation | professional_services_sme | low | false | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the service delivery manager. |
NW-AIO-0016 | Design a controlled workflow for managing stock replenishment at a co-operative retail network. When a synthetic replenishment request falling below its reorder threshold, capture an audit event, assign the next step to the operations co-ordinator, and pause before placing an order or changing a recorded stock balance.... | workflow_automation | local_commerce | low | false | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the operations co-ordinator. |
NW-AIO-0017 | Map an automation that moves a verified record from the demonstration dispatch board to the mock customer-update queue for a neighbourhood delivery service. It must prevent duplicate work, preserve the original submission, notify the dispatch supervisor of failures, and require explicit approval before changing payment... | workflow_automation | local_commerce | low | false | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the dispatch supervisor. |
NW-AIO-0018 | Create a repeatable operating sequence for scheduling approved repair work that shows the trigger, validation checks, responsible roles, exception route and completion evidence. The sequence must support workshop and service staff without allowing the system to perform authorising additional cost or changing a warranty... | workflow_automation | local_commerce | low | false | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the workshop manager. |
NW-AIO-0019 | Design a controlled workflow for routing equipment inspection findings at a mining equipment support contractor. When a synthetic inspection note identifying a serviceability concern, capture an audit event, assign the next step to the maintenance support supervisor, and pause before returning equipment to service or o... | workflow_automation | mining_support_services | medium | true | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the maintenance support supervisor. |
NW-AIO-0020 | Map an automation that moves a verified record from the demonstration logistics register to the mock dispatch exception queue for an industrial logistics provider. It must prevent duplicate work, preserve the original submission, notify the logistics control supervisor of failures, and require explicit approval before ... | workflow_automation | mining_support_services | medium | true | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the logistics control supervisor. |
NW-AIO-0021 | Create a repeatable operating sequence for co-ordinating competency reviews that shows the trigger, validation checks, responsible roles, exception route and completion evidence. The sequence must support technical training staff without allowing the system to perform awarding competence or changing an assessment resul... | workflow_automation | mining_support_services | medium | true | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the technical training manager. |
NW-AIO-0022 | Design a controlled workflow for triaging public enquiries at a civic information service. When a synthetic public enquiry entering the triage queue, capture an audit event, assign the next step to the public information co-ordinator, and pause before making an eligibility decision or disclosing protected information. ... | workflow_automation | public_interest_services | medium | true | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the public information co-ordinator. |
NW-AIO-0023 | Map an automation that moves a verified record from the demonstration information queue to the mock guidance referral register for a community legal-information project. It must prevent duplicate work, preserve the original submission, notify the information service supervisor of failures, and require explicit approval... | workflow_automation | public_interest_services | medium | true | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the information service supervisor. |
NW-AIO-0024 | Create a repeatable operating sequence for reviewing public health education content that shows the trigger, validation checks, responsible roles, exception route and completion evidence. The sequence must support health education staff without allowing the system to perform giving medical advice or publishing an unsup... | workflow_automation | public_interest_services | medium | true | A versioned workflow specification with trigger, validation steps, ownership, approval gate, exception route and audit events. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the health content editor. |
NW-AIO-0027 | Turn synthetic complaint correspondence packs into a validated tabular record for complaints resolution staff. Record source-file provenance, normalise dates and controlled categories, and ask the complaints resolution manager to review low-confidence extraction before anything reaches the sandbox resolution tracker. | document_processing | insurance_operations | medium | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the complaints resolution manager. |
NW-AIO-0029 | Define a document-ingestion job for synthetic workshop registration exports received through the demonstration registration form. Produce structured JSON and a review queue, detect duplicates, redact unnecessary free text, and block downstream use when a required field is uncertain. | document_processing | education_services | low | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the programme administrator. |
NW-AIO-0030 | Turn synthetic mentor session summaries into a validated tabular record for outreach programme staff. Record source-file provenance, normalise dates and controlled categories, and ask the outreach programme lead to review low-confidence extraction before anything reaches the sandbox outreach dashboard. | document_processing | education_services | low | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the outreach programme lead. |
NW-AIO-0032 | Define a document-ingestion job for synthetic story pitch submissions received through the demonstration pitch queue. Produce structured JSON and a review queue, detect duplicates, redact unnecessary free text, and block downstream use when a required field is uncertain. | document_processing | media_and_creative | low | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the commissioning editor. |
NW-AIO-0033 | Turn synthetic resource submission forms into a validated tabular record for membership and content staff. Record source-file provenance, normalise dates and controlled categories, and ask the membership content curator to review low-confidence extraction before anything reaches the sandbox resource catalogue. | document_processing | media_and_creative | low | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the membership content curator. |
NW-AIO-0034 | Process a batch of entirely synthetic programme referral forms for a youth development non-profit. Extract referral reason, age band, consent status and preferred contact route, return a confidence score for each field, preserve page references, and send unreadable or conflicting values to the programme intake manager ... | document_processing | non_profit_programmes | medium | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the programme intake manager. |
NW-AIO-0036 | Turn synthetic field observation notes into a validated tabular record for monitoring and programme staff. Record source-file provenance, normalise dates and controlled categories, and ask the monitoring and evaluation lead to review low-confidence extraction before anything reaches the sandbox conservation register. | document_processing | non_profit_programmes | medium | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the monitoring and evaluation lead. |
NW-AIO-0038 | Define a document-ingestion job for synthetic milestone review notes received through the mock delivery plan. Produce structured JSON and a review queue, detect duplicates, redact unnecessary free text, and block downstream use when a required field is uncertain. | document_processing | professional_services_sme | low | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the delivery lead. |
NW-AIO-0039 | Turn synthetic support ticket exports into a validated tabular record for service desk staff. Record source-file provenance, normalise dates and controlled categories, and ask the service delivery manager to review low-confidence extraction before anything reaches the mock incident register. | document_processing | professional_services_sme | low | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the service delivery manager. |
NW-AIO-0041 | Define a document-ingestion job for synthetic delivery exception notes received through the demonstration dispatch board. Produce structured JSON and a review queue, detect duplicates, redact unnecessary free text, and block downstream use when a required field is uncertain. | document_processing | local_commerce | low | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the dispatch supervisor. |
NW-AIO-0042 | Turn synthetic repair estimate forms into a validated tabular record for workshop and service staff. Record source-file provenance, normalise dates and controlled categories, and ask the workshop manager to review low-confidence extraction before anything reaches the sandbox workshop schedule. | document_processing | local_commerce | low | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the workshop manager. |
NW-AIO-0043 | Process a batch of entirely synthetic equipment inspection sheets for a mining equipment support contractor. Extract asset class, observation, evidence status, priority and inspector role, return a confidence score for each field, preserve page references, and send unreadable or conflicting values to the maintenance su... | document_processing | mining_support_services | medium | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the maintenance support supervisor. |
NW-AIO-0045 | Turn synthetic competency observation forms into a validated tabular record for technical training staff. Record source-file provenance, normalise dates and controlled categories, and ask the technical training manager to review low-confidence extraction before anything reaches the sandbox assessment queue. | document_processing | mining_support_services | medium | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the technical training manager. |
NW-AIO-0046 | Process a batch of entirely synthetic public enquiry submissions for a civic information service. Extract topic, service area, urgency, consent and preferred reply format, return a confidence score for each field, preserve page references, and send unreadable or conflicting values to the public information co-ordinator... | document_processing | public_interest_services | medium | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the public information co-ordinator. |
NW-AIO-0047 | Define a document-ingestion job for synthetic legal-information request summaries received through the demonstration information queue. Produce structured JSON and a review queue, detect duplicates, redact unnecessary free text, and block downstream use when a required field is uncertain. | document_processing | public_interest_services | medium | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the information service supervisor. |
NW-AIO-0048 | Turn synthetic health education content briefs into a validated tabular record for health education staff. Record source-file provenance, normalise dates and controlled categories, and ask the health content editor to review low-confidence extraction before anything reaches the sandbox evidence-review queue. | document_processing | public_interest_services | medium | true | Structured CSV or JSON with field-level confidence, source references, validation errors and a human-review queue. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the health content editor. |
NW-AIO-0049 | Analyse synthetic claims queue records for a regional insurer by cover type, age of case and assigned queue. Show volumes, median turnaround, ageing distribution and data-completeness warnings, but do not infer causes or rank individuals. Give the claims team leader the filters and definitions used. | analytics | insurance_operations | medium | true | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the claims team leader. |
NW-AIO-0054 | Investigate where operational delays appear in synthetic outreach engagement records for outreach programme staff. Return reproducible aggregates, missing-data rates and outlier records for review; do not use the result to automate giving personal counselling or changing a learner record. | analytics | education_services | low | false | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the outreach programme lead. |
NW-AIO-0056 | Build a decision-support analysis from synthetic pitch and commissioning records that compares the current period with the previous one across topic, decision stage, verification status and commissioning desk. Separate observed figures from interpretations, expose small-sample warnings, and route any consequential conc... | analytics | media_and_creative | low | false | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the commissioning editor. |
NW-AIO-0057 | Investigate where operational delays appear in synthetic member-resource records for membership and content staff. Return reproducible aggregates, missing-data rates and outlier records for review; do not use the result to automate granting reuse rights or publishing personal contact details. | analytics | media_and_creative | low | false | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the membership content curator. |
NW-AIO-0058 | Analyse synthetic programme referral records for a youth development non-profit by referral route, programme, status and waiting-time band. Show volumes, median turnaround, ageing distribution and data-completeness warnings, but do not infer causes or rank individuals. Give the programme intake manager the filters and ... | analytics | non_profit_programmes | medium | true | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the programme intake manager. |
NW-AIO-0059 | Build a decision-support analysis from synthetic distribution planning records that compares the current period with the previous one across service area, request status, capacity band and week. Separate observed figures from interpretations, expose small-sample warnings, and route any consequential conclusion to the d... | analytics | non_profit_programmes | medium | true | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the distribution programme lead. |
NW-AIO-0060 | Investigate where operational delays appear in synthetic conservation activity records for monitoring and programme staff. Return reproducible aggregates, missing-data rates and outlier records for review; do not use the result to automate publishing precise habitat locations or asserting an unverified impact. | analytics | non_profit_programmes | medium | true | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the monitoring and evaluation lead. |
NW-AIO-0061 | Analyse synthetic enquiry and response records for a small professional-services firm by service category, source, status and response-time band. Show volumes, median turnaround, ageing distribution and data-completeness warnings, but do not infer causes or rank individuals. Give the client services manager the filters... | analytics | professional_services_sme | low | false | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the client services manager. |
NW-AIO-0062 | Build a decision-support analysis from synthetic delivery milestone records that compares the current period with the previous one across project type, milestone status, dependency risk and week. Separate observed figures from interpretations, expose small-sample warnings, and route any consequential conclusion to the ... | analytics | professional_services_sme | low | false | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the delivery lead. |
NW-AIO-0064 | Analyse synthetic stock movement records for a co-operative retail network by item category, outlet, stock band and week. Show volumes, median turnaround, ageing distribution and data-completeness warnings, but do not infer causes or rank individuals. Give the operations co-ordinator the filters and definitions used. | analytics | local_commerce | low | false | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the operations co-ordinator. |
NW-AIO-0066 | Investigate where operational delays appear in synthetic workshop job records for workshop and service staff. Return reproducible aggregates, missing-data rates and outlier records for review; do not use the result to automate authorising additional cost or changing a warranty decision. | analytics | local_commerce | low | false | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the workshop manager. |
NW-AIO-0067 | Analyse synthetic equipment support records for a mining equipment support contractor by asset class, service status, priority band and site category. Show volumes, median turnaround, ageing distribution and data-completeness warnings, but do not infer causes or rank individuals. Give the maintenance support supervisor... | analytics | mining_support_services | medium | true | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the maintenance support supervisor. |
NW-AIO-0068 | Build a decision-support analysis from synthetic industrial delivery records that compares the current period with the previous one across route, load category, exception status and delivery-window band. Separate observed figures from interpretations, expose small-sample warnings, and route any consequential conclusion... | analytics | mining_support_services | medium | true | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the logistics control supervisor. |
NW-AIO-0069 | Investigate where operational delays appear in synthetic technical training records for technical training staff. Return reproducible aggregates, missing-data rates and outlier records for review; do not use the result to automate awarding competence or changing an assessment result. | analytics | mining_support_services | medium | true | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the technical training manager. |
NW-AIO-0070 | Analyse synthetic public enquiry records for a civic information service by topic, service area, status and response-time band. Show volumes, median turnaround, ageing distribution and data-completeness warnings, but do not infer causes or rank individuals. Give the public information co-ordinator the filters and defin... | analytics | public_interest_services | medium | true | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the public information co-ordinator. |
NW-AIO-0072 | Investigate where operational delays appear in synthetic health education content records for health education staff. Return reproducible aggregates, missing-data rates and outlier records for review; do not use the result to automate giving medical advice or publishing an unsupported health claim. | analytics | public_interest_services | medium | true | A reproducible aggregate analysis with definitions, data-quality indicators, uncertainty notes and no automated consequential decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the health content editor. |
NW-AIO-0073 | Answer staff questions about claim evidence requirements and escalation thresholds using only an approved, versioned knowledge base for a regional insurer. Return the relevant section titles and source links, state when the material is silent, and refer unresolved cases to the claims team leader. | knowledge_retrieval | insurance_operations | medium | true | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the claims team leader. |
NW-AIO-0074 | Create a retrieval workflow for policy servicing staff covering authorised policy changes and identity-check steps. Rank passages from authorised guidance, quote only the minimum useful excerpt, attach document version and date, and refuse to invent a procedure when no source supports it. | knowledge_retrieval | insurance_operations | medium | true | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the policy servicing supervisor. |
NW-AIO-0075 | Given a non-sensitive question on complaint ownership, response standards and evidence handling, find the most relevant approved guidance and produce a concise answer with paragraph-level citations. Highlight conflicting sources and ask the complaints resolution manager to resolve them before the answer is acted upon. | knowledge_retrieval | insurance_operations | medium | true | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the complaints resolution manager. |
NW-AIO-0076 | Answer staff questions about learner support routes and reasonable adjustment procedures using only an approved, versioned knowledge base for a vocational training provider. Return the relevant section titles and source links, state when the material is silent, and refer unresolved cases to the learner support co-ordin... | knowledge_retrieval | education_services | low | false | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the learner support co-ordinator. |
NW-AIO-0077 | Create a retrieval workflow for programme administration staff covering registration, waiting-list and accessibility procedures. Rank passages from authorised guidance, quote only the minimum useful excerpt, attach document version and date, and refuse to invent a procedure when no source supports it. | knowledge_retrieval | education_services | low | false | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the programme administrator. |
NW-AIO-0080 | Create a retrieval workflow for editorial staff covering source verification, corrections and editorial sign-off. Rank passages from authorised guidance, quote only the minimum useful excerpt, attach document version and date, and refuse to invent a procedure when no source supports it. | knowledge_retrieval | media_and_creative | low | false | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the commissioning editor. |
NW-AIO-0081 | Given a non-sensitive question on attribution, reuse permissions and catalogue standards, find the most relevant approved guidance and produce a concise answer with paragraph-level citations. Highlight conflicting sources and ask the membership content curator to resolve them before the answer is acted upon. | knowledge_retrieval | media_and_creative | low | false | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the membership content curator. |
NW-AIO-0082 | Answer staff questions about consent, eligibility screening and safeguarding escalation using only an approved, versioned knowledge base for a youth development non-profit. Return the relevant section titles and source links, state when the material is silent, and refer unresolved cases to the programme intake manager. | knowledge_retrieval | non_profit_programmes | medium | true | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the programme intake manager. |
NW-AIO-0084 | Given a non-sensitive question on evidence quality, location sensitivity and public reporting, find the most relevant approved guidance and produce a concise answer with paragraph-level citations. Highlight conflicting sources and ask the monitoring and evaluation lead to resolve them before the answer is acted upon. | knowledge_retrieval | non_profit_programmes | medium | true | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the monitoring and evaluation lead. |
NW-AIO-0087 | Given a non-sensitive question on incident severity, customer communication and restoration approval, find the most relevant approved guidance and produce a concise answer with paragraph-level citations. Highlight conflicting sources and ask the service delivery manager to resolve them before the answer is acted upon. | knowledge_retrieval | professional_services_sme | low | false | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the service delivery manager. |
NW-AIO-0088 | Answer staff questions about stock verification, purchase limits and discrepancy handling using only an approved, versioned knowledge base for a co-operative retail network. Return the relevant section titles and source links, state when the material is silent, and refer unresolved cases to the operations co-ordinator. | knowledge_retrieval | local_commerce | low | false | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the operations co-ordinator. |
NW-AIO-0089 | Create a retrieval workflow for dispatch and customer-service staff covering delivery exceptions, contact consent and refund authority. Rank passages from authorised guidance, quote only the minimum useful excerpt, attach document version and date, and refuse to invent a procedure when no source supports it. | knowledge_retrieval | local_commerce | low | false | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the dispatch supervisor. |
NW-AIO-0090 | Given a non-sensitive question on estimate approval, warranty boundaries and job-status updates, find the most relevant approved guidance and produce a concise answer with paragraph-level citations. Highlight conflicting sources and ask the workshop manager to resolve them before the answer is acted upon. | knowledge_retrieval | local_commerce | low | false | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the workshop manager. |
NW-AIO-0093 | Given a non-sensitive question on assessment evidence, assessor authority and appeal procedures, find the most relevant approved guidance and produce a concise answer with paragraph-level citations. Highlight conflicting sources and ask the technical training manager to resolve them before the answer is acted upon. | knowledge_retrieval | mining_support_services | medium | true | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the technical training manager. |
NW-AIO-0094 | Answer staff questions about public information sources, referral routes and response standards using only an approved, versioned knowledge base for a civic information service. Return the relevant section titles and source links, state when the material is silent, and refer unresolved cases to the public information c... | knowledge_retrieval | public_interest_services | medium | true | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the public information co-ordinator. |
NW-AIO-0095 | Create a retrieval workflow for information and referral staff covering the distinction between general information, referral and legal advice. Rank passages from authorised guidance, quote only the minimum useful excerpt, attach document version and date, and refuse to invent a procedure when no source supports it. | knowledge_retrieval | public_interest_services | medium | true | A concise answer grounded in approved sources, with section-level citations, version details and an explicit no-answer route. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the information service supervisor. |
NW-AIO-0097 | When the incident date predates the stated policy period or a required attachment is absent at a regional insurer, stop the normal workflow, retain the original evidence, open a clearly labelled exception case and alert the claims team leader. Specify safe retry conditions and prevent approving, declining or pricing an... | exception_handling | insurance_operations | high | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the claims team leader. |
NW-AIO-0099 | Handle this operational failure safely: the chronology is inconsistent or the requested remedy requires authority outside the team. Quarantine only the affected synthetic record, continue unaffected work, give the complaints resolution manager a diagnostic summary and require a documented decision before resuming any s... | exception_handling | insurance_operations | high | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the complaints resolution manager. |
NW-AIO-0100 | When the request mentions immediate welfare risk or lacks enough information for safe routing at a vocational training provider, stop the normal workflow, retain the original evidence, open a clearly labelled exception case and alert the learner support co-ordinator. Specify safe retry conditions and prevent making a s... | exception_handling | education_services | medium | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the learner support co-ordinator. |
NW-AIO-0101 | Design an exception path for managing workshop registrations when capacity is exceeded or an accessibility request needs individual confirmation. It should classify severity, avoid duplicate alerts, preserve prior states, set an accountable owner and record how the case was resolved without silently changing source dat... | exception_handling | education_services | medium | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the programme administrator. |
NW-AIO-0103 | When usage rights are unverified or the brief requests an unsupported factual claim at an independent media studio, stop the normal workflow, retain the original evidence, open a clearly labelled exception case and alert the production manager. Specify safe retry conditions and prevent publishing unverified material or... | exception_handling | media_and_creative | medium | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the production manager. |
NW-AIO-0105 | Handle this operational failure safely: the creator is not credited or the stated reuse permission is ambiguous. Quarantine only the affected synthetic record, continue unaffected work, give the membership content curator a diagnostic summary and require a documented decision before resuming any step that could involve... | exception_handling | media_and_creative | medium | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the membership content curator. |
NW-AIO-0107 | Design an exception path for scheduling community distributions when the requested quantity exceeds the approved band or consent is missing. It should classify severity, avoid duplicate alerts, preserve prior states, set an accountable owner and record how the case was resolved without silently changing source data. | exception_handling | non_profit_programmes | high | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the distribution programme lead. |
NW-AIO-0109 | When the enquiry requests a guarantee or presents a possible conflict of interest at a small professional-services firm, stop the normal workflow, retain the original evidence, open a clearly labelled exception case and alert the client services manager. Specify safe retry conditions and prevent accepting contractual t... | exception_handling | professional_services_sme | medium | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the client services manager. |
NW-AIO-0110 | Design an exception path for tracking delivery milestones when evidence is missing or a dependency changes the agreed scope. It should classify severity, avoid duplicate alerts, preserve prior states, set an accountable owner and record how the case was resolved without silently changing source data. | exception_handling | professional_services_sme | medium | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the delivery lead. |
NW-AIO-0111 | Handle this operational failure safely: the ticket requests unsafe access or the impact cannot be confirmed. Quarantine only the affected synthetic record, continue unaffected work, give the service delivery manager a diagnostic summary and require a documented decision before resuming any step that could involve chang... | exception_handling | professional_services_sme | medium | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the service delivery manager. |
NW-AIO-0112 | When the physical count and recorded quantity differ beyond the approved tolerance at a co-operative retail network, stop the normal workflow, retain the original evidence, open a clearly labelled exception case and alert the operations co-ordinator. Specify safe retry conditions and prevent placing an order or changin... | exception_handling | local_commerce | medium | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the operations co-ordinator. |
NW-AIO-0114 | Handle this operational failure safely: the observed fault differs materially from the approved estimate. Quarantine only the affected synthetic record, continue unaffected work, give the workshop manager a diagnostic summary and require a documented decision before resuming any step that could involve authorising addi... | exception_handling | local_commerce | medium | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the workshop manager. |
NW-AIO-0115 | When the observation suggests an immediate safety risk or lacks inspection evidence at a mining equipment support contractor, stop the normal workflow, retain the original evidence, open a clearly labelled exception case and alert the maintenance support supervisor. Specify safe retry conditions and prevent returning e... | exception_handling | mining_support_services | high | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the maintenance support supervisor. |
NW-AIO-0116 | Design an exception path for checking delivery manifests when dispatch authorisation is missing or the route changes after approval. It should classify severity, avoid duplicate alerts, preserve prior states, set an accountable owner and record how the case was resolved without silently changing source data. | exception_handling | mining_support_services | high | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the logistics control supervisor. |
NW-AIO-0117 | Handle this operational failure safely: the evidence is incomplete or the observer lacks the required assessor role. Quarantine only the affected synthetic record, continue unaffected work, give the technical training manager a diagnostic summary and require a documented decision before resuming any step that could inv... | exception_handling | mining_support_services | high | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the technical training manager. |
NW-AIO-0119 | Design an exception path for routing legal-information requests when the request needs individual legal advice or includes an imminent-risk statement. It should classify severity, avoid duplicate alerts, preserve prior states, set an accountable owner and record how the case was resolved without silently changing sourc... | exception_handling | public_interest_services | high | true | An exception case with severity, preserved evidence, accountable owner, safe retry criteria and an auditable resolution state. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the information service supervisor. |
NW-AIO-0121 | Detect when a request at a regional insurer could involve approving, declining or pricing an insurance claim. Prepare a neutral case summary with evidence gaps, urgency and the applicable guidance, then escalate it to the claims team leader; do not recommend or execute the final decision. | human_escalation | insurance_operations | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the claims team leader. |
NW-AIO-0126 | Route this scenario to an accountable person: a note contains sensitive welfare information or indicates a missed referral. Minimise the information shown, notify the outreach programme lead through the approved channel, enforce a response deadline and leave the record paused until a named reviewer acts. | human_escalation | education_services | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the outreach programme lead. |
NW-AIO-0127 | Detect when a request at an independent media studio could involve publishing unverified material or clearing third-party rights. Prepare a neutral case summary with evidence gaps, urgency and the applicable guidance, then escalate it to the production manager; do not recommend or execute the final decision. | human_escalation | media_and_creative | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the production manager. |
NW-AIO-0128 | Create a human-review gate for reviewing story pitches. The gate must show the triggering condition, source evidence, system confidence and possible consequences, give the commissioning editor approve, return and reject options, and record their reason. | human_escalation | media_and_creative | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the commissioning editor. |
NW-AIO-0129 | Route this scenario to an accountable person: the creator is not credited or the stated reuse permission is ambiguous. Minimise the information shown, notify the membership content curator through the approved channel, enforce a response deadline and leave the record paused until a named reviewer acts. | human_escalation | media_and_creative | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the membership content curator. |
NW-AIO-0132 | Route this scenario to an accountable person: the observation cannot be corroborated or contains a sensitive habitat location. Minimise the information shown, notify the monitoring and evaluation lead through the approved channel, enforce a response deadline and leave the record paused until a named reviewer acts. | human_escalation | non_profit_programmes | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the monitoring and evaluation lead. |
NW-AIO-0135 | Route this scenario to an accountable person: the ticket requests unsafe access or the impact cannot be confirmed. Minimise the information shown, notify the service delivery manager through the approved channel, enforce a response deadline and leave the record paused until a named reviewer acts. | human_escalation | professional_services_sme | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the service delivery manager. |
NW-AIO-0136 | Detect when a request at a co-operative retail network could involve placing an order or changing a recorded stock balance. Prepare a neutral case summary with evidence gaps, urgency and the applicable guidance, then escalate it to the operations co-ordinator; do not recommend or execute the final decision. | human_escalation | local_commerce | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the operations co-ordinator. |
NW-AIO-0137 | Create a human-review gate for resolving delivery exceptions. The gate must show the triggering condition, source evidence, system confidence and possible consequences, give the dispatch supervisor approve, return and reject options, and record their reason. | human_escalation | local_commerce | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the dispatch supervisor. |
NW-AIO-0138 | Route this scenario to an accountable person: the observed fault differs materially from the approved estimate. Minimise the information shown, notify the workshop manager through the approved channel, enforce a response deadline and leave the record paused until a named reviewer acts. | human_escalation | local_commerce | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the workshop manager. |
NW-AIO-0139 | Detect when a request at a mining equipment support contractor could involve returning equipment to service or overriding a safety hold. Prepare a neutral case summary with evidence gaps, urgency and the applicable guidance, then escalate it to the maintenance support supervisor; do not recommend or execute the final d... | human_escalation | mining_support_services | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the maintenance support supervisor. |
NW-AIO-0140 | Create a human-review gate for checking delivery manifests. The gate must show the triggering condition, source evidence, system confidence and possible consequences, give the logistics control supervisor approve, return and reject options, and record their reason. | human_escalation | mining_support_services | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the logistics control supervisor. |
NW-AIO-0141 | Route this scenario to an accountable person: the evidence is incomplete or the observer lacks the required assessor role. Minimise the information shown, notify the technical training manager through the approved channel, enforce a response deadline and leave the record paused until a named reviewer acts. | human_escalation | mining_support_services | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the technical training manager. |
NW-AIO-0142 | Detect when a request at a civic information service could involve making an eligibility decision or disclosing protected information. Prepare a neutral case summary with evidence gaps, urgency and the applicable guidance, then escalate it to the public information co-ordinator; do not recommend or execute the final de... | human_escalation | public_interest_services | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the public information co-ordinator. |
NW-AIO-0143 | Create a human-review gate for routing legal-information requests. The gate must show the triggering condition, source evidence, system confidence and possible consequences, give the information service supervisor approve, return and reject options, and record their reason. | human_escalation | public_interest_services | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the information service supervisor. |
NW-AIO-0144 | Route this scenario to an accountable person: a claim lacks an authoritative source or could be interpreted as individual medical advice. Minimise the information shown, notify the health content editor through the approved channel, enforce a response deadline and leave the record paused until a named reviewer acts. | human_escalation | public_interest_services | high | true | A privacy-minimised review pack containing evidence, uncertainty, urgency, options and a recorded human decision. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the health content editor. |
NW-AIO-0145 | Synchronise synthetic records from the mock claims mailbox to the sandbox case-management register for a regional insurer. Use an idempotency key, schema validation, least-privilege access, bounded retries and a dead-letter queue; never trigger approving, declining or pricing an insurance claim from an unverified respo... | api_integration | insurance_operations | high | true | A tested integration contract with schema mapping, idempotency, least privilege, failure handling and reconciliation evidence. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the claims team leader. |
NW-AIO-0147 | Design the contract for transferring approved synthetic records into the sandbox resolution tracker. Include field mapping, consent and authorisation checks, rate-limit handling, rollback evidence and reconciliation totals, with the complaints resolution manager responsible for unresolved mismatches. | api_integration | insurance_operations | high | true | A tested integration contract with schema mapping, idempotency, least privilege, failure handling and reconciliation evidence. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the complaints resolution manager. |
NW-AIO-0149 | Specify a read-only API integration that gives programme administration staff current status from the demonstration registration form. Validate response types, cache only non-sensitive fields, log correlation identifiers rather than payloads, and alert the programme administrator when the upstream contract changes. | api_integration | education_services | medium | true | A tested integration contract with schema mapping, idempotency, least privilege, failure handling and reconciliation evidence. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the programme administrator. |
NW-AIO-0150 | Design the contract for transferring approved synthetic records into the sandbox outreach dashboard. Include field mapping, consent and authorisation checks, rate-limit handling, rollback evidence and reconciliation totals, with the outreach programme lead responsible for unresolved mismatches. | api_integration | education_services | medium | true | A tested integration contract with schema mapping, idempotency, least privilege, failure handling and reconciliation evidence. | Entirely synthetic planning example. Use only non-sensitive demonstration data; the accountable human role is the outreach programme lead. |
End of preview. Expand in Data Studio
N.White AI Operations Intent Dataset
An entirely synthetic English-language dataset of practical requests for responsible AI-enabled operational work. Each request is labelled with one of eight intents and grounded in a realistic—but fictional—operational context.
The dataset supports the theme practical, responsible AI systems for operational workflows. It is maintained by Whitemore Ngwira (N.White) for N.White Systems.
No real client, employee, learner, policyholder, claimant or beneficiary data is included. Organisation descriptions are generic and every record is a synthetic planning example.
Dataset summary
| Property | Value |
|---|---|
| Version | 1.0.0 |
| Total records | 192 |
| Train | 128 |
| Validation | 32 |
| Test | 32 |
| Intent labels | 8 (24 records each) |
| Operational domains | 8 (24 records each) |
| Intent-domain combinations | 64 (3 records each) |
| Languages | English, using professional British spelling |
The splits are deterministic and stratified by intent: each label contributes 16 training, 4 validation and 4 test records. Stable SHA-256 ordering derived from the versioned generation seed assigns records to splits.
Intent labels
workflow_automationdocument_processinganalyticsknowledge_retrievalexception_handlinghuman_escalationapi_integrationreporting
Operational domains
The fictional situations span insurance operations, education services, media and creative work, non-profit programmes, professional-services SMEs, local commerce, mining support services and public-interest services. The examples are informed by common operational patterns; they do not claim that N.White Systems has deployed every listed use case.
Schema
| Field | Type | Description |
|---|---|---|
id |
string | Stable record identifier in the form NW-AIO-0001. |
user_request |
string | Synthetic operational request written in professional British English. |
intent |
string | One of the eight classification labels. |
operational_domain |
string | Fictional operational setting. |
risk_level |
string | Illustrative low, medium or high triage level. |
requires_human_review |
boolean | Whether an accountable reviewer is explicitly required. |
expected_output |
string | Suitable deliverable for the request. |
notes |
string | Synthetic-data and governance reminder. |
The machine-readable contract is in schema.json.
Files
data/train.csv,data/validation.csv,data/test.csv: canonical Hugging Face Dataset Viewer splits.- Matching
.jsonlfiles: convenient streaming and command-line alternatives. data/all.csvanddata/all.jsonl: complete exports.data/SHA256SUMS: integrity hashes for every data export.scripts/generate_dataset.py: deterministic, offline generator.scripts/validate_dataset.py: schema, split, balance, language, safety and checksum validation.validation/validation_report.json: generated validation evidence.
Synthetic generation method
The records are generated offline from manually authored operational situations and manually authored intent-specific request patterns. The generator takes the Cartesian product of eight intents and 24 fictional situations (three per domain), then combines the matching pattern variant with each situation. No live system, private repository, client source, external model or web service is read during generation.
Generation is deterministic. Version 1.0.0 uses the fixed seed string nwhite-ai-operations-intent-dataset-v1.0.0-20260801; it affects only stable split ordering. Re-running the generator produces byte-stable CSV and JSONL exports in the same Python environment.
Loading the dataset
from datasets import load_dataset
dataset = load_dataset(
"nwhite-systems/nwhite-ai-operations-intent-dataset",
name="default",
)
print(dataset["train"][0])
Load the JSONL exports without the Hub client:
import json
with open("data/train.jsonl", encoding="utf-8") as handle:
first_record = json.loads(next(handle))
print(first_record["intent"], first_record["user_request"])
Reproduce and validate locally:
python scripts/generate_dataset.py
python scripts/validate_dataset.py
Intended uses
- teaching and demonstrating operational intent classification;
- benchmarking lightweight classifiers on a small, transparent synthetic corpus;
- testing routing logic, human-review gates and audit-friendly workflow design;
- prototyping user interfaces with non-sensitive examples;
- extending evaluation sets with clearly documented synthetic scenarios.
The related N.White AI Operations Intent Classifier is trained only on the published training split.
Unsuitable uses
Do not use this dataset to:
- make insurance, credit, employment, education, legal, medical or other high-stakes decisions;
- infer a person's intent, risk, identity, eligibility or character;
- train systems on real confidential records without a separate lawful governance process;
- claim production readiness, fairness or accuracy in a real organisation;
- automate financial actions, policy decisions, safety decisions or publication without accountable human approval;
- measure language performance beyond the narrow synthetic English patterns represented here.
Privacy and safety
The corpus contains no deliberately collected personal information and no real operational records. Generic organisation descriptions and role names are used instead of identifiable entities. Safety-oriented examples mention approval, escalation, consent, least privilege and source attribution because these controls are part of the intended learning task.
Users remain responsible for checking derived systems, logs and prompts for sensitive information. The risk_level and requires_human_review fields are educational annotations, not professional advice.
Bias considerations
The situations reflect the maintainer's selected sectors and responsible-operations framing. They may over-represent formal workflows, English-language terminology, explicit control language and well-documented organisations. African operational settings are diverse; these fictional examples cannot represent every country, language, institution, infrastructure constraint or community priority.
Labels are balanced by construction, unlike many real request streams. A classifier may therefore learn generator phrasing rather than robust intent semantics. Evaluate with independently written, locally relevant and lawfully obtained examples before considering any operational use.
Limitations
- Small, synthetic and English-only.
- Eight intentionally broad, mutually exclusive labels; real requests can be multi-intent.
- Split records share an authoring framework, so held-out scores can overestimate real-world performance.
- No adversarial misspellings, code-switching, voice transcripts or long conversations are included.
- Risk labels are illustrative and are not calibrated against a regulatory or actuarial framework.
- No claim is made that each scenario has been implemented by N.White Systems.
Licence
The dataset content is released under the Creative Commons Attribution 4.0 International licence. Attribution should identify Whitemore Ngwira / N.White Systems and link to this repository. Code in the scripts directory may be reused under the same repository licence.
Versioning
Version 1.0.0 is the initial release. Any future change to record text, identifiers, labels, split assignment or schema should increment the version and regenerate data/SHA256SUMS plus the validation report.
Maintainer and citation
Maintainer: Whitemore Ngwira (N.White), N.White Systems
Website: https://nwhite.systems/
Hugging Face: https://huggingface.co/nwhite-systems
@dataset{ngwira_2026_nwhite_ai_operations_intent,
author = {Whitemore Ngwira},
title = {N.White AI Operations Intent Dataset},
year = {2026},
version = {1.0.0},
publisher = {N.White Systems},
url = {https://huggingface.co/datasets/nwhite-systems/nwhite-ai-operations-intent-dataset}
}
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