{"id":690,"date":"2025-08-27T11:12:59","date_gmt":"2025-08-27T11:12:59","guid":{"rendered":"https:\/\/datadecoded.com\/mcr\/?post_type=seminar&p=690"},"modified":"2025-09-02T17:56:35","modified_gmt":"2025-09-02T17:56:35","slug":"under-the-bonnet-of-conversational-bi-no-one-size-fits-all-nlq-engine","status":"publish","type":"seminar","link":"https:\/\/datadecoded.com\/mcr\/seminars\/under-the-bonnet-of-conversational-bi-no-one-size-fits-all-nlq-engine\/","title":{"rendered":"Under the Bonnet of Conversational BI: No One-Size-Fits-All NLQ Engine"},"content":{"rendered":"
AI is accelerating a data deluge. Everyone\u2019s expected to make sense of it, but not everyone is a SQL or Python expert. Conversational BI and natural language querying promise to democratise insight, yet opinions swing from \u201csilver bullet\u201d to \u201cdemo-ware\u201d. The missing piece? One size doesn\u2019t fit all, the engine under the bonnet matters.<\/div>\n
<\/div>\n
Beneath every \u201cchat with your data\u201d demo is one of four engine modes: strict semantic parsing, ontology\/graph reasoning, direct LLM-to-SQL, or a hybrid that grounds LLMs on a governed semantic\/metrics layer. In this vendor-neutral session, you\u2019ll learn how to identify which engine your use case needs, how to spot what vendors are running under the bonnet, and when building the engine yourself is the right call. You\u2019ll leave with an engine archetype map, a buyer\u2019s checklist to pressure-test vendor claims, and a pragmatic pilot plan you can run on your stack, without over-spending or over-promising.<\/div>\n
<\/div>\n
Takeaways<\/strong><\/div>\n
\n
    \n
  • \n
    Engine archetype map (A\/B\/C\/D) – strengths & pitfalls<\/div>\n<\/li>\n
  • \n
    Match-to-need decision framework incl. Buy-vs-build guidance<\/div>\n<\/li>\n
  • \n
    Vendor due-diligence checklist (8 questions)<\/div>\n<\/li>\n
  • \n
    Pragmatic\u00a04\u20136 week pilot plan (accuracy, latency, safety)<\/p>\n","protected":false},"template":"","class_list":["post-690","seminar","type-seminar","status-publish","hentry"],"acf":{"start_time":"2025-10-22 16:10:00","end_time":"2025-10-22 16:40:00","theatre":631,"speakers":[433],"businesses":""},"yoast_head":"\nUnder the Bonnet of Conversational BI: No One-Size-Fits-All NLQ Engine - Data Decoded MCR<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datadecoded.com\/mcr\/seminars\/under-the-bonnet-of-conversational-bi-no-one-size-fits-all-nlq-engine\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Under the Bonnet of Conversational BI: No One-Size-Fits-All NLQ Engine\" \/>\n<meta property=\"og:description\" content=\"AI is accelerating a data deluge. 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