1(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[3118],{37947:function(t,e,a){(window.__NEXT_P=window.__NEXT_P||[]).push(["/product/ai-analyst",function(){return a(22238)}])},22238:function(t,e,a){"use strict";a.r(e);var n=a(85893),i=a(2593),s=a(44738),o=a(41766),r=a(54),l=a(73521),d=a(42960),c=a(69275);let p={headerBtnBgClass:"bg-[#D6FA9B]/30",headerNavBgClass:"hover:bg-[#D6FA9B]/40",svgFillClass:"fill-[#D6FA9B]",bgClass:"bg-[#FCFEF9]",tag:"AI Analyst",title:"Your trusted always-on analyst that gets better every day.*",description:"*Fire it if it fails to deliver and learn at the pace you expect.",descClass:"lg:max-w-[613px]",cta:{text:"Request demo",href:"/book-demo",variant:"primary-light"}},u={meta_title:"AI Analyst | PromptQL",meta_description:"Your trusted always-on analyst that gets better every day.",meta_image:{data:{attributes:{url:"https://promptql.io/og/product.png"}}},canonical_url:"https://promptql.io/product/ai-analyst"},h={title:"Adapts to your business reality",desc:"Gets your business context, not just data â just like your best analyst.\n",img:"https://res.cloudinary.com/dh8fp23nd/image/upload/v1758186208/promptql-website/product/ai-analyst-adapts_pate4r.png",imgMaxWidth:"md:max-w-[400px] lg:max-w-[510px]"},g={title:"The Solution",description:"For your org, ACME, PromptQL helps you build AcmeQL, a language that an LLM can use to plan, reason and act with the level of reliability you can expect from an analyst or engineer on staff.\n\nPromptQL is an AI platform that continuously adapts LLMs to your domain by capturing and encoding the proprietary know-how into a planning language the LLM can understand and generate.\n\nThe planning language incorporates terminology, processes and a semantic graph of data and tools.\n\nImportantly, this generated language can be compiled into precise machine executable code that can run deterministically and hence handle arbitrarily large amounts of data and complex plans without LLM context limitations.",architectureDiagram:(0,n.jsx)(d.Z,{})},m=[{title:"Q&A ",tabName:"Q&A ",tabContentID:"qa",description:"Ask questions, get instant answers",leftSideContent:{title:"How PromptQL solves this:",description:["Encodes company-specific ARR rules directly into the query layer","Knows which accounts to exclude based on Financeâs divestment logic","Understands internal jargon like âlogo churnâ and applies it correctly"]},rightSideContent:'"What was our net ARR impact from logo churn in H1, excluding divested accounts?"'},{title:"Deep investigation and RCA",tabName:"Investigation",titleClass:"lg:max-w-[620px]",tabContentID:"generative-applications",description:"Quickly uncover root causes",leftSideContent:{title:"How PromptQL solves this:",description:["Applies the correct SLA-based definition of âon-time deliveryâ","Connects logistics events with procurement policy updates to explain carrier shifts","Surfaces true root causes, moving beyond descriptive stats to causal drivers"]},rightSideContent:'"Why did our on-time delivery rate drop in Q2, and how much of it is linked to the switch from FedEx to regional carriers?"'},{title:"Planning and forecasting",tabName:"Planning",tabContentID:"planning",description:"Predict and plan with confidence",leftSideContent:{title:"How PromptQL solves this:",description:["Learn seasonal hiring rules as from usage and data","Adjusts forecasts for labor productivity shifts from self-checkout rollout","Ensures staffing plans respect policy constraints like floor minimums"]},rightSideContent:'"Forecast Q4 store staffing needs considering seasonal hiring rules, new self-checkout rollout and floor staffing minimums"'},{title:"Reports and dashboards",tabName:"Dashboards",tabContentID:"dashboards",description:"Generate contextual reports and dashboards on-demand",leftSideContent:{title:"How PromptQL solves this:",description:["Applies the QA teamâs definition of readmission, not generic system defaults","Automates the merging of EHR, admission, and discharge logs with custom filters","Maps hospital units correctly, even when billing or facility codes donât align"]},rightSideContent:'"Build a dashboard of patient readmission rates by hospital unit, but use QA team definitionâ'}];e.default=()=>(0,n.jsxs)(o.Z,{headerBtnBgClass:p.headerBtnBgClass,headerNavBgClass:p.headerNavBgClass,children:[(0,n.jsx)(i.T,{...u}),(0,n.jsx)(r.d,{...p}),(0,n.jsx)(s.j,{...h}),(0,n.jsx)(c.Z,{title:"Full spectrum analytical capabilities",desc:"Instant answers to complex forecasts â all in one place.",tabsContent:m}),(0,n.jsx)("div",{id:"the-solution",className:"hide-sticky-tabs",children:(0,n.jsx)(l.Z,{...g})})]})}},function(t){t.O(0,[3996,1862,6635,5276,9555,4658,1562,3460,2888,9774,179],function(){return t(t.s=37947)}),_N_E=t.O()}]);
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.