[{"data":1,"prerenderedAt":163},["ShallowReactive",2],{"blog-nlp-for-business-insights":3,"blog-related-\u002Fblog\u002Fnlp-for-business-insights":131},{"id":4,"title":5,"author":6,"authorRole":7,"body":8,"cover":114,"coverAlt":115,"date":116,"description":117,"draft":118,"extension":119,"meta":120,"navigation":121,"path":122,"readingTime":123,"seo":124,"stem":125,"tags":126,"updated":129,"__hash__":130},"blog\u002Fblog\u002Fnlp-for-business-insights.md","NLP for business: turning feedback, tickets and documents into decisions","William Ali","Founder & AI Specialist",{"type":9,"value":10,"toc":107},"minimark",[11,15,20,28,32,61,65,76,79,83,104],[12,13,14],"p",{},"Roughly four-fifths of business data is unstructured text: reviews, tickets, emails, contracts, call notes. Almost none of it is analysed systematically. Modern NLP makes that cheap.",[16,17,19],"h2",{"id":18},"start-with-a-decision-not-a-model","Start with a decision, not a model",[12,21,22,23,27],{},"The most useful projects begin with a question someone will act on: ",[24,25,26],"em",{},"Which product issues are growing fastest? Which contracts contain a clause we've stopped accepting? Which customers sound like they're about to leave?"," Pick one; the pipeline follows.",[16,29,31],{"id":30},"the-four-workhorse-tasks","The four workhorse tasks",[33,34,35,43,49,55],"ol",{},[36,37,38,42],"li",{},[39,40,41],"strong",{},"Classification:"," assign topics, sentiment, urgency or intent. Fine-tuned small transformers are fast, cheap and accurate for stable label sets.",[36,44,45,48],{},[39,46,47],{},"Extraction:"," pull entities, dates, amounts, clauses. LLMs with structured output are excellent here, especially for messy documents.",[36,50,51,54],{},[39,52,53],{},"Clustering:"," discover topics you didn't know to look for. Embeddings + clustering + LLM-generated labels is a strong default.",[36,56,57,60],{},[39,58,59],{},"Summarisation:"," condense long threads or reports into what a human needs to know.",[16,62,64],{"id":63},"a-reference-pipeline","A reference pipeline",[66,67,73],"pre",{"className":68,"code":70,"language":71,"meta":72},[69],"language-text","sources → ingestion → cleaning → model(s) → warehouse → dashboard \u002F alerts\n","text","",[74,75,70],"code",{"__ignoreMap":72},[12,77,78],{},"Keep raw text alongside model outputs so you can re-run when models improve. Store confidence scores; they let you route low-confidence items to people.",[16,80,82],{"id":81},"pitfalls-we-see-repeatedly","Pitfalls we see repeatedly",[84,85,86,92,98],"ul",{},[36,87,88,91],{},[39,89,90],{},"Sentiment without context."," \"Fast\" is positive for delivery, negative for battery drain. Topic-aware sentiment is worth the effort.",[36,93,94,97],{},[39,95,96],{},"Label drift."," Business categories change; schedule a quarterly review of the taxonomy.",[36,99,100,103],{},[39,101,102],{},"Analysis nobody reads."," Deliver a weekly one-page report with movers and quotes, not a dashboard with forty charts.",[12,105,106],{},"Text is the largest under-used asset most companies own. The tooling is mature; the differentiator is asking a sharp question and closing the loop with the team that acts on the answer.",{"title":72,"searchDepth":108,"depth":108,"links":109},2,[110,111,112,113],{"id":18,"depth":108,"text":19},{"id":30,"depth":108,"text":31},{"id":63,"depth":108,"text":64},{"id":81,"depth":108,"text":82},"\u002Fcovers\u002Fblog\u002Fnlp-for-business-insights.svg","Line-art illustration of a text document fanning out into three clustered groups of points.","2025-10-02","A practical guide to using natural-language processing on the unstructured text your company already has.",false,"md",{},true,"\u002Fblog\u002Fnlp-for-business-insights",6,{"title":5,"description":117},"blog\u002Fnlp-for-business-insights",[127,128],"NLP","Analytics",null,"bdWb9TkUiPzt2mUyMSUJt1GW3lraMvs0mGsJNSoL90E",[132,142,153],{"path":133,"title":134,"description":135,"date":136,"tags":137,"cover":139,"coverAlt":140,"readingTime":141},"\u002Fblog\u002Fmeasuring-ai-roi-honestly","Measuring AI ROI honestly: a method that survives the finance review","How to measure the return on an AI project without fooling yourself: baselines, full costs, holdout attribution, leading indicators and inflated-ROI red flags.","2026-08-11",[138,128],"Strategy","\u002Fcovers\u002Fblog\u002Fmeasuring-ai-roi-honestly.svg","Line-art illustration of a ruler measuring a stack of coins beside a rising bar.",7,{"path":143,"title":144,"description":145,"date":146,"tags":147,"cover":151,"coverAlt":152,"readingTime":141},"\u002Fblog\u002Fmultimodal-ai-business-workflows","Multimodal AI in real business workflows: where it earns its place","Vision-language and audio models now read forms, photos, screenshots and calls directly. Where multimodal AI beats OCR-then-LLM, with examples, limits and cost.","2026-06-30",[148,149,150],"Computer Vision","LLMs","Automation","\u002Fcovers\u002Fblog\u002Fmultimodal-ai-business-workflows.svg","Line-art illustration of an eye, an ear and a speech bubble converging into a single processing node.",{"path":154,"title":155,"description":156,"date":157,"tags":158,"cover":161,"coverAlt":162,"readingTime":141},"\u002Fblog\u002Fmlops-basics-first-model","MLOps basics for teams shipping their first model","The minimum MLOps a team needs to put a first model into production safely: versioning, reproducible training, a registry, serving, monitoring and rollback.","2026-05-19",[159,160],"MLOps","Product","\u002Fcovers\u002Fblog\u002Fmlops-basics-first-model.svg","Line-art illustration of a looping pipeline with gauges, from data to model to monitoring and back.",1792973685982]