{"observation":{"id":"4e60f528-bc2c-4ae1-9c9f-a18f86d2d8ad","tool":"definite","tool_name":"Definite","criterion":"business-insight","criterion_name":"Business Insight","criterion_definition":"Does it explain what the result means?","criterion_evidence_type":"transformation","criterion_rank_role":null,"criterion_rank_role_reason":null,"scenario":"order-pipeline-breakdown-with-paid-pending-edge-case-and-last-month-comparison","scenario_name":"Order pipeline breakdown with paid-pending edge case and last-month comparison","group_tag":"ecommerce-nl2sql-benchmark","scenario_description":"A deeper operational analysis of current order stages, delivery-versus-cancellation rates, pending-but-paid edge cases, and a month-over-month comparison of the same breakdown.","modality":"text","input_text":"How many orders do we have at each stage right now?\n\nFollow-up 1: What percentage of our orders were successfully delivered vs cancelled?\n\nFollow-up 2: Are there any orders that are pending but already paid?\n\nFollow-up 3: Compare that to last month — same breakdown, I want to see if things have improved or got worse.","input_artifact_refs":["image-2.png"],"stresses":["Order pipeline analysis","Percentage calculation","Edge-case detection","Payment/order status joins","Multi-turn context retention","Month-over-month comparison","Ambiguity handling for 'same breakdown'"],"verdict":"worked","score":null,"score_total":null,"note":"It explained what the numbers meant, including the 14% cancellation rate, the completed-orders caveat, the 2 pending-but-paid orders that need attention, and the warning that May is still early because 17 of 21 orders remain pending.","evidence_state":"verified","source":null,"artifacts":[{"url":"https://d3epheqghktydj.cloudfront.net/definite-image-7-c3b7ca9bc20d.png","role":null,"alt":null},{"url":"https://d3epheqghktydj.cloudfront.net/definite-image-8-5dccfe01ae41.png","role":null,"alt":null},{"url":"https://d3epheqghktydj.cloudfront.net/definite-image-11-b91a0c1626b9.png","role":null,"alt":null}],"run_id":"db2bb5d5-0e0e-4cb3-8d76-3555c45c23cd","study_title":"Query Live Databases Using Plain English with AI","study_kind":"generation","research_task":"86b9y6c99","tested_at":null,"completeness":"input-and-output","input":{"state":"text","text":"How many orders do we have at each stage right now?\n\nFollow-up 1: What percentage of our orders were successfully delivered vs cancelled?\n\nFollow-up 2: Are there any orders that are pending but already paid?\n\nFollow-up 3: Compare that to last month — same breakdown, I want to see if things have improved or got worse.","files":[],"modality":"text","stresses":["Order pipeline analysis","Percentage calculation","Edge-case detection","Payment/order status joins","Multi-turn context retention","Month-over-month comparison","Ambiguity handling for 'same breakdown'"]},"tool_page_slug":"definite","tool_url":"https://aidemos.com/tools/definite","permalink":"https://aidemos.com/evidence/4e60f528-bc2c-4ae1-9c9f-a18f86d2d8ad","api_url":"https://ai.aidemos.com/v1/observations/4e60f528-bc2c-4ae1-9c9f-a18f86d2d8ad"},"peers":[{"id":"11b11634-12da-417a-8c8f-2a69060619ab","tool":"askyourdatabase","tool_name":"AskYourDatabase","verdict":"worked","score":null,"score_total":null,"note":"The follow-up summaries explained what the raw counts meant operationally, including delivery/cancellation rates and the backlog reduction story.","artifact_count":2,"thumbnail":"https://d3epheqghktydj.cloudfront.net/askyourdatabase-image-9-93c40da3f7dd.png","evidence_url":"https://aidemos.com/evidence/11b11634-12da-417a-8c8f-2a69060619ab"},{"id":"82eb80c3-8afa-429f-b476-46edd043f78e","tool":"draxlr","tool_name":"Draxlr","verdict":"worked","score":null,"score_total":null,"note":"Can turn numeric results into plain-English takeaways; the delivered-vs-cancelled follow-up automatically summarized the split and framed delivered orders as more common than cancelled orders.","artifact_count":1,"thumbnail":"https://d3epheqghktydj.cloudfront.net/draxlr-image-5-3d65a48f356b.png","evidence_url":"https://aidemos.com/evidence/82eb80c3-8afa-429f-b476-46edd043f78e"},{"id":"3f5103f1-b095-4b6f-b13e-20b0b7dc4af3","tool":"querio","tool_name":"Querio","verdict":"worked","score":null,"score_total":null,"note":"Explains the comparison rather than just reporting numbers, including that the month-to-date view is an early signal and should not be treated as a stable trend yet.","artifact_count":1,"thumbnail":"https://d3epheqghktydj.cloudfront.net/querio-image-17-3c748f3c0c67.png","evidence_url":"https://aidemos.com/evidence/3f5103f1-b095-4b6f-b13e-20b0b7dc4af3"}],"other_criteria":[{"id":"79fec0da-750d-4b9b-b1a4-f85f74d69b29","criterion":"chart-visualization-support","criterion_name":"Chart / Visualization Support","rank_role":null,"verdict":"mixed","score":null,"score_total":null,"note":"Charts were not generated automatically in the chat; the report says an extra prompt was needed before Definite created a separate dashboard/doc, and the dashboard view shows an 'Orders By Stage' summary with a donut chart.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/79fec0da-750d-4b9b-b1a4-f85f74d69b29"},{"id":"bb39a696-0cad-4eff-8a52-fa272573962f","criterion":"follow-up-context","criterion_name":"Follow-Up Context","rank_role":null,"verdict":"worked","score":null,"score_total":null,"note":"It remembered the earlier breakdown when asked for 'the same breakdown' last month, reusing the stage categories and the pending-but-paid edge case across turns.","artifact_count":2,"evidence_url":"https://aidemos.com/evidence/bb39a696-0cad-4eff-8a52-fa272573962f"},{"id":"9d5f020f-02c0-4e3e-ad77-c501e9beb8c7","criterion":"result-readability","criterion_name":"Result Readability","rank_role":null,"verdict":"worked","score":null,"score_total":null,"note":"The order pipeline outputs were presented in compact tables with totals and percentages, making the 93-order breakdown and the April-vs-May comparison straightforward to read.","artifact_count":3,"evidence_url":"https://aidemos.com/evidence/9d5f020f-02c0-4e3e-ad77-c501e9beb8c7"},{"id":"3597b066-7467-4ee7-94c5-45e5c4184ff0","criterion":"sql-generation","criterion_name":"SQL Generation","rank_role":null,"verdict":"worked","score":null,"score_total":null,"note":"It correctly produced the order-pipeline result set across the multi-turn flow, including the 93-order stage breakdown and the April-versus-May comparison tables, without manual SQL writing.","artifact_count":3,"evidence_url":"https://aidemos.com/evidence/3597b066-7467-4ee7-94c5-45e5c4184ff0"}],"appears_in":[{"page_type":"use-case","slug":"query-live-databases","title":"Query Live Databases Using Plain English with AI","url":"https://aidemos.com/use-cases/query-live-databases","binding":"run"},{"page_type":"ranking","slug":"text-to-sql-tools","title":"Best AI Tools to Query Live Databases Using Plain English","url":"https://aidemos.com/best/text-to-sql-tools","binding":"study"}],"same_scenario":[{"id":"11b11634-12da-417a-8c8f-2a69060619ab","tool":"askyourdatabase","tool_name":"AskYourDatabase","verdict":"worked","score":null,"score_total":null,"note":"The follow-up summaries explained what the raw counts meant operationally, including delivery/cancellation rates and the backlog reduction story."},{"id":"82eb80c3-8afa-429f-b476-46edd043f78e","tool":"draxlr","tool_name":"Draxlr","verdict":"worked","score":null,"score_total":null,"note":"Can turn numeric results into plain-English takeaways; the delivered-vs-cancelled follow-up automatically summarized the split and framed delivered orders as more common than cancelled orders."},{"id":"3f5103f1-b095-4b6f-b13e-20b0b7dc4af3","tool":"querio","tool_name":"Querio","verdict":"worked","score":null,"score_total":null,"note":"Explains the comparison rather than just reporting numbers, including that the month-to-date view is an early signal and should not be treated as a stable trend yet."}]}