[{"data":1,"prerenderedAt":750},["ShallowReactive",2],{"uc-b2b-sales-order-capture-agent":3,"uc-regulations":529},{"useCase":4,"evidence":227,"blitsAiDeployments":389,"benchmarks":390,"indicative":418,"related":421,"indexability":527,"includeUnpublished":233},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":22,"patterns":26,"channels":30,"audience":36,"autonomy":37,"adoptionStage":38,"segment":39,"problem":40,"problemStats":41,"howItWorks":54,"valueDrivers":55,"kpis":60,"indicativeValue":67,"macroEstimates":109,"feasibility":110,"implementation":125,"risk":171,"blitsAi":202,"faq":204,"related":217,"datePublished":222,"dateModified":222,"lastVerified":222,"changelog":223,"slug":226},"AI agent for B2B sales order capture from email, WhatsApp and PDF","B2B order capture","AI sales order entry for distributors","AI reads orders sent by email, WhatsApp, voicemail and PDF and enters them in the ERP. North West Meat Company went from 3 hours to under 1 hour a day.","published","A B2B sales order capture agent is AI that reads customer orders arriving by email, WhatsApp, voicemail, PDF or fax and turns them into sales orders in the ERP. Staff review the exceptions it flags instead of retyping every line.",[12,13,14,15,16],"AI order entry","sales order automation","purchase order capture","order taking agent","email to ERP order automation",[18,19,20,21],"cross-industry","logistics-and-transportation","retail-and-ecommerce","manufacturing",[23,24,25],"sales","operations","customer-service",[27,28,29],"document-processing","agentic-workflow","classification-and-routing",[31,32,33,34,35],"email","whatsapp","voice","api","internal-tools","back-office","supervised-agent","early-adopters","wholesale and distribution","Wholesale customers order the way that suits them, not the way the supplier's system expects.\nA restaurant sends a list on WhatsApp, a retailer emails a purchase order as a PDF or in the body\nof the message, a buyer faxes a sheet, a chef leaves a voicemail. Swaledale Butchers, a Yorkshire\nbutcher that supplies chefs across the UK, took 70% of its orders through WhatsApp and typed every one into its butchery\nsoftware, about two minutes each. At North West Meat Company in Chicago, someone processed the\nday's restaurant orders every evening, up to three hours a night, against a catalog of more than\n1,300 SKUs.\n\nThe typing is slow and the knowledge behind it is fragile. Orders are informal (\"more steak\", \"five\nof the usual\"), and every customer has its own part numbers, pack sizes, cut specifications and\nnegotiated prices. Mistakes show up later as\nwrong picks, credit notes and urgent redeliveries, and orders that sit in a salesperson's inbox or\nphone are invisible to the rest of the company. Colony Foods described reps keying bulk orders into\nthe system from their cars before a cut off.\n\nAsking every customer to move to a portal or EDI helps only the customers who agree. The\ndeployments on this page take orders in whatever format arrives, extract and validate the lines,\nand write a draft sales order into the ERP for a person to approve or, once trusted, for the agent\nto post. This page covers inbound customer orders. Questions about an existing order are covered\nby the order status and returns page, and supplier invoices by the supplier invoice processing page.",[42,47,52],{"statement":43,"sourceTitle":44,"sourceUrl":45,"year":46},"WizCommerce, a wholesale software vendor, estimates that manual order processing can take 20 minutes per order and cost between $10 and $12 per transaction.","WizCommerce Launches AI Order Entry Tool for Distributors","https:\u002F\u002Fdistributionstrategy.com\u002F2026\u002F06\u002Fwizcommerce-launches-ai-order-entry-tool-for-distributors\u002F",2026,{"statement":48,"sourceTitle":49,"sourceUrl":50,"year":51},"Choco, an order automation vendor, states that entering a single 40 line order by hand can take seven to eight minutes.","OrderAgent: The AI Order Processing Engine That's Powering the Future of Food Distribution","https:\u002F\u002Fchoco.com\u002Fus\u002Fstories\u002Fsuppliers\u002Forderagent-the-ai-order-processing-engine-thats-powering-the-future-of-food-distribution",2025,{"statement":53,"sourceTitle":49,"sourceUrl":50,"year":51},"Quoting the IFDA 2025 Foodservice Distribution Industry Technology Report, Choco states that 56% of foodservice distribution companies are adopting AI for ecommerce and ordering solutions.","1. **Capture every inbound channel.** The agent watches the shared order mailbox, a WhatsApp\n   business number and the voicemail transcripts, and picks up attachments (PDF, Excel, photos of\n   handwritten notes, faxes) and orders arriving through procurement platforms. Customers keep\n   ordering as before.\n2. **Extract the order.** It reads customer, delivery date, purchase order number, lines and\n   quantities from the message, including informal wording and several orders in one email.\n3. **Match to your catalog and to this customer.** It maps each phrase to an item using the item\n   master, the customer's own part numbers and units, and that customer's order history, so \"the\n   usual\" or \"15 pounds of sirloin\" resolves to the cut the customer buys.\n4. **Validate.** It checks prices against the customer's price list, ship to addresses, stock and\n   credit status, and flags a mismatch, an unknown item or an ambiguous quantity instead of guessing.\n5. **Review or post.** In review mode a person sees the source message next to the extracted order\n   and approves it in seconds. For customers and items with a clean record the agent can post\n   straight to the ERP and leave only exceptions for review.\n6. **Confirm and learn.** The ERP creates the sales order, the customer gets an acknowledgment, and\n   every correction a person makes is kept as a new mapping for the next order.",[56,57,58,59],"cost-to-serve","employee-productivity","speed","risk-reduction",[61,62,63,64,65,66],"processing-time-reduction","accuracy","hours-saved","cost-savings","automation-rate","error-reduction",{"referenceOrg":68,"inputs":69,"formula":104,"currency":105,"period":106,"resultLabel":107,"caveat":108},"A regional food distributor that receives 150 customer orders a day by email, WhatsApp and phone",[70,76,82,89,97],{"key":71,"label":72,"low":73,"high":73,"unit":74,"note":75},"ordersPerDay","Customer orders received per day",150,"orders per day","The reference distributor.",{"key":77,"label":78,"low":79,"high":79,"unit":80,"note":81},"daysPerYear","Order days per year",300,"days","Editorial assumption of six order days a week. Replace with your own calendar.",{"key":83,"label":84,"low":85,"high":86,"unit":87,"note":88,"sourceUrl":50},"shareCaptured","Share of orders the agent captures, with review or posted directly",0.5,0.8,"fraction of orders","Editorial assumption. The low end rests on Choco's statement in its OrderAgent article that many distributors automate more than 50% of orders. The 0.8 upper bound has no source. None of the case studies on this page discloses a share of orders automated, so replace both ends with your own measured rate.",{"key":90,"label":91,"low":92,"high":93,"unit":94,"note":95,"sourceUrl":96},"minutesSaved","Minutes saved per captured order, after review time",1.5,5,"minutes per order","Editorial assumption. Choco's Swaledale Butchers case study reports about 2 minutes by hand against 8 seconds, and Choco's OrderAgent article puts a 40 line order at seven to eight minutes by hand. Replace with a timed sample of your own orders.","https:\u002F\u002Fchoco.com\u002Fuk\u002Fstories\u002Fcase-studies\u002Fswaledale-butchers-ai-order-processing",{"key":98,"label":99,"low":100,"high":101,"unit":102,"note":103},"costPerHour","Fully loaded cost of an order entry hour",20,30,"USD per hour","Editorial assumption. Replace with your own fully loaded cost.","ordersPerDay * daysPerYear * shareCaptured * minutesSaved \u002F 60 * costPerHour","USD","per year","Order entry labor cost avoided","Gross labor cost only. It leaves out the cost of the software and its integration, the review time on exceptions beyond the minutes counted, the value of fewer picking errors and credit notes, and what the freed hours earn when they go into selling.",[],{"complexity":111,"complexityNote":112,"dataPrerequisites":113,"integrations":119},"medium","The build needs read access to the item master, customer specific part numbers and price lists and order history, plus write access to the ERP's sales order entry through an API or an import file. Mapping customer wording to items decides the accuracy more than the choice of model, and it needs a few months of past orders per customer. Choco's case study says Reach Food Group went live eight weeks after starting, and its Swaledale Butchers case study gives an initial ERP integration on July 8th and a go live on August 21st.",[114,115,116,117,118],"Item master with units of measure, pack sizes and active status","Customer specific part numbers, aliases and price lists","Order history per customer, ideally twelve months, to resolve \"the usual\"","A sample of real messy orders (voicemail transcripts, WhatsApp threads, PDFs) for testing","Customer terms that affect an order, such as credit holds, minimum order values and delivery days",[120,121,122,123,124],"ERP or order management system (sales order API or import file)","Shared mailbox and a WhatsApp Business number","Voicemail or telephony transcription","Procurement platforms and EDI feeds the customers already use","CRM or customer service system for the acknowledgment and exception handling",{"steps":126,"guardrails":145,"humanInTheLoop":151,"kpisToInstrument":152,"failureModes":158},[127,130,133,136,139,142],{"title":128,"detail":129},"Time a week of manual entry","Record orders per day, minutes per order, channel mix and error types for one week. Those numbers set the baseline for every claim later, and they show which channel to start with.",{"title":131,"detail":132},"Start with one channel and your cleanest customers","Pick the channel with the most volume (for Swaledale Butchers, WhatsApp) and the customers whose orders are regular. Run the agent in review mode so every order is approved by a person while the mappings are built.",{"title":134,"detail":135},"Build the mapping layer before tuning prompts","Load customer part numbers, aliases and past orders, and keep every correction a person makes as a new alias. Most early errors are wording the system has not seen from that customer.",{"title":137,"detail":138},"Define what may post without review","Allow direct posting only for customer and item pairs with a clean history, orders under a value limit and prices that match the list. Everything else goes to the review queue.",{"title":140,"detail":141},"Add the next channels and customers in waves","Extend to email attachments, voicemail and further customers once the error rate in review is stable, and re run the timed sample to confirm the saving.",{"title":143,"detail":144},"Close the loop with the customer","Send an acknowledgment that lists what was understood, so a wrong reading is caught by the customer before picking starts.",[146,147,148,149,150],"No order posts to the ERP when a price, address or credit check fails; it goes to a person","A value and quantity ceiling above which every order is reviewed, whatever the history","Items the agent cannot match with confidence are listed as unknown, never substituted","The original message is stored with the order so any line can be traced to its source","Personal data in messages is masked in logs and prompts, and retention follows the customer contract","Order desk staff approve every order in review mode and every flagged exception afterwards. They own new customers, new items, price disputes and any order the agent marks as ambiguous. A team lead samples a share of orders that posted directly each week and compares them with the source messages, and signs off each widening of the direct posting rules.",[153,154,155,156,157],"Share of orders posted without edits, and share needing a correction, per customer and channel","Minutes from message received to order in the ERP, median and 95th percentile","Line level accuracy on a weekly sample checked against the source message","Picking errors, credit notes and redeliveries traced to order entry","Hours of order desk time spent on exceptions versus on selling and customer calls",[159,162,165,168],{"title":160,"detail":161},"Right order, wrong customer's meaning","\"Five of the usual\" or a shared nickname for a cut means different items for different customers. Resolve through that customer's history and alias list, and ask the customer when two readings are close.",{"title":163,"detail":164},"A silent misread passes review","Reviewers who approve in seconds stop reading the source. Show the source side by side, highlight low confidence lines, and audit a sample of approved orders against the original.",{"title":166,"detail":167},"Price or terms drift","The agent posts an order at an old price because a price list changed. Validate against the live price list on every order and block the post on a mismatch.",{"title":169,"detail":170},"Messages that carry more than an order","A reply with a complaint, a cancellation or a change to an earlier order is processed as a new order. Classify the message first and route non order content to customer service.",{"euAiAct":172,"regulations":175,"guidance":179,"controls":195,"incidents":201},{"tier":173,"basis":174},"context-dependent","A back office agent that turns business customers' order messages into draft sales orders does not decide on access to services, credit or employment, which is minimal risk. Article 50(1) applies to providers of AI systems intended to interact directly with natural persons. Reading inbound orders and sending a one way acknowledgment is a weak fit, but an agent that holds clarification exchanges with customers on WhatsApp or email does interact directly with the people who write, and those people must be informed they are dealing with an AI system unless that is obvious from the context.",[176,177,178],"eu-ai-act","gdpr","uk-gdpr",[180,186,190],{"title":181,"issuer":182,"region":183,"url":184,"note":185},"Regulation (EU) 2016\u002F679 (GDPR)","European Union","europe","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","Order messages and voicemails contain names, phone numbers and delivery addresses of the customer's staff, so the supplier is the controller of that personal data and the AI vendor it uses acts as a processor.",{"title":187,"issuer":182,"region":183,"url":188,"note":189},"Article 50, transparency obligations for providers and deployers of certain AI systems","https:\u002F\u002Fartificialintelligenceact.eu\u002Farticle\u002F50\u002F","Applies to providers of systems that interact directly with people, so it matters if the agent holds clarification exchanges with customers, for example on WhatsApp. One way order acknowledgments are a weak fit.",{"title":191,"issuer":192,"region":183,"url":193,"note":194},"UK GDPR and the ICO guidance","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","Applies to UK suppliers such as Swaledale Butchers and Reach Food Group, which handle the same names, phone numbers and delivery addresses.",[196,197,198,199,200],"Order and message log that ties every ERP line to the source message and the person or rule that approved it","Role based access, so the agent can create sales orders but cannot change prices, credit limits or customer master data","Retention and deletion rules for voicemail, WhatsApp threads and attachments","Masking of personal data in prompts and logs","Weekly sample audit of posted orders against the source, with the result kept for the next review",[],{"howToBuild":203},"On Blits.ai this is an **AI agent** with **custom functions** that read and write the ERP's sales\norder API and look up customers, items and price lists (REST calls or SQL queries), plus a\n**knowledge base** for the item catalog, aliases and customer terms, including **SQL knowledge\nbases** when the item master sits in a database. Orders arrive on the **email and WhatsApp\nchannels** and, for phone orders, on **voice** with speech to text. An **agentic workflow**\nextracts and validates the lines, and **human in the loop confirmation** holds any order above a\nset value or with a failed check until a person approves it.\n\n**Guardrails** and **PII masking** protect the message content in prompts and logs, **test\nsuites** replay a library of real messy orders as regression tests after every change to the\nmappings, and **monitors** run scheduled checks against the agent. **Human handover** passes\ncomplaints and non order messages to customer service with the conversation attached. The\nplatform is model agnostic, and EU or UAE data residency applies to the message content.",[205,208,211,214],{"question":206,"answer":207},"How much of the order volume can be handled without a person?","No source on this page gives a range. Choco's OrderAgent article states that many distributors automate more than 50% of orders, and Choco's Swaledale Butchers case study says the team now oversees and verifies automated inputs at 8 seconds per order. At Howard Elliott, staff still review each order, which takes around five minutes according to WizCommerce. The figures come from vendor case studies and a vendor article, and none discloses the share of orders automated at a named company.",{"question":209,"answer":210},"Do customers need to change how they order?","Not in the deployments on this page. Swaledale Butchers took 70% of its orders through WhatsApp before go live, and Choco says OrderAgent now captures orders from WhatsApp and procurement platforms. Howard Elliott still forwards emailed and faxed orders to the assistant. The change happens at the supplier, which is why adoption does not depend on each customer agreeing to a portal.",{"question":212,"answer":213},"How long does a rollout take?","Choco's case study says Reach Food Group took eight weeks from concept to go live, and its Swaledale Butchers case study gives an ERP integration on 8 July and a go live on 21 August. Choco says Swaledale reached its results \"after a short onboarding and learning period\", so plan for review mode first.",{"question":215,"answer":216},"Is this the same as EDI or an online ordering portal?","No. EDI and portals require the customer to send structured data in an agreed format. This approach reads the orders customers already send. The two can run side by side: before go live Swaledale Butchers took 70% of its orders through WhatsApp and 30% through procurement platforms, and Choco says OrderAgent captures orders from both.",[218,219,220,221],"intelligent-document-processing","supplier-invoice-processing","order-status-and-returns-agent","sales-quote-and-estimate-generation","2026-10-14",[224],{"date":222,"note":225},"First published","b2b-sales-order-capture-agent",[228,266,291,318,343,367],{"title":229,"useCases":230,"organization":231,"vendors":236,"summary":240,"stage":241,"year":46,"channels":242,"languages":243,"metrics":245,"outcomeDisclosed":254,"sources":255,"verification":261,"grade":263,"id":264,"organizationSlug":265},"The Howard Elliott Collection: AI order entry with WizCommerce Ella",[226],{"name":232,"anonymized":233,"country":234,"region":235,"industry":20},"The Howard Elliott Collection",false,"US","north-america",[237],{"name":238,"role":239},"WizCommerce","platform","The Howard Elliott Collection, a supplier of home furnishings and decor to retailers, receives purchase orders by email and fax, as attachments or in the email body, and its customer service team used to spend three to four hours a day entering them. It now forwards orders to WizCommerce's Ella assistant, which extracts the line items, checks customer and pricing data and flags mismatches, so staff only review the result. WizCommerce reports that order entry fell from several hours a day to about 15 minutes.","production",[31],[244],"en",[246],{"kpi":61,"value":247,"unit":248,"qualifier":249,"period":250,"baseline":251,"claimant":252,"quote":253,"sourceUrl":45},90,"percent","at-least","per day","Several hours per day of manual order entry (three to four hours according to the customer story)","vendor","Among them is Howard Elliott, a supplier of home furnishings and décor products, which said it reduced order entry work from several hours per day to approximately 15 minutes daily.",true,[256,259],{"url":257,"title":258,"publisher":238},"https:\u002F\u002Fwizcommerce.com\u002Fcustomer-stories\u002Fhoward-elliott-order-entry-automation\u002F","Howard Elliott: Order Entry Cut from 4 Hours to 15 Min",{"url":45,"title":44,"publisher":260},"Distribution Strategy Group",{"level":262,"checkedAt":222},"source-verified","C","howard-elliott-wizcommerce-ella-order-entry",null,{"title":267,"useCases":268,"organization":269,"vendors":271,"summary":274,"stage":241,"year":51,"channels":275,"languages":276,"metrics":277,"outcomeDisclosed":254,"sources":285,"verification":289,"grade":263,"id":290,"organizationSlug":265},"Colony Foods: AI order entry with Choco OrderAgent",[226],{"name":270,"anonymized":233,"country":234,"region":235,"industry":19},"Colony Foods",[272],{"name":273,"role":239},"Choco","Colony Foods, a family run business in Massachusetts with more than 100 employees and 25 trucks, had used Choco's OrderAgent for a year when its owner described the result on Choco's site. Sales reps used to key bulk orders into the system in their cars before a daily cut off. OrderAgent now reads the orders, holds them during internal system outages and charges the intended prices, and the owner says saving at least two hours every day has reduced operational pressure and that reps now focus on customer relationships.",[],[244],[278],{"kpi":63,"value":279,"unit":280,"qualifier":249,"period":281,"claimant":282,"quote":283,"sourceUrl":284},2,"hours","every day","organization","Saving at least two hours every day has drastically reduced operational pressure.","https:\u002F\u002Fchoco.com\u002Fus\u002Fstories\u002Fcase-studies\u002Fcolony-foods",[286],{"url":284,"title":287,"publisher":273,"archivedUrl":288},"Colony Foods: Reps Can Now Focus On Relationships","https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20250621172138\u002Fhttps:\u002F\u002Fchoco.com\u002Fus\u002Fstories\u002Fcase-studies\u002Fcolony-foods",{"level":262,"checkedAt":222},"colony-foods-choco-order-entry",{"title":292,"useCases":293,"organization":294,"vendors":297,"summary":299,"stage":241,"year":51,"channels":300,"languages":301,"metrics":302,"outcomeDisclosed":254,"sources":312,"verification":316,"grade":263,"id":317,"organizationSlug":265},"Reach Food Group: AI order entry with Choco",[226],{"name":295,"anonymized":233,"country":296,"region":183,"industry":19},"Reach Food Group","GB",[298],{"name":273,"role":239},"Reach Food Group, a London seafood supplier serving restaurant kitchens, put Choco's AI order processing live eight weeks after starting the project. Choco's case study reports that within three weeks of go live orders were processed with 96% accuracy and that work which took five people eight hour shifts (40 hours) now takes 4 hours, at about 28 seconds per order.",[],[244],[303,309],{"kpi":62,"value":304,"unit":248,"qualifier":305,"period":306,"claimant":252,"quote":307,"sourceUrl":308},96,"exact","within three weeks of go live","96% Order Accuracy: Within 3 weeks, Reach was processing orders with unprecedented precision.","https:\u002F\u002Fchoco.com\u002Fuk\u002Fstories\u002Fcase-studies\u002Fcase-study-ai-reach-food-group",{"kpi":61,"value":247,"unit":248,"qualifier":305,"baseline":310,"claimant":252,"quote":311,"sourceUrl":308},"Five people working 8 hour shifts (40 hours)","What previously took five people working 8-hour shifts (40 man-hours) now happens in just 4 hours.",[313],{"url":308,"title":314,"publisher":273,"archivedUrl":315},"Reach Food Group: Reduced processing time from 40 to 4 hours","https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20250321001704\u002Fhttps:\u002F\u002Fchoco.com\u002Fuk\u002Fstories\u002Fcase-studies\u002Fcase-study-ai-reach-food-group",{"level":262,"checkedAt":222},"reach-food-group-choco-order-entry",{"title":319,"useCases":320,"organization":321,"vendors":323,"summary":325,"stage":241,"year":51,"channels":326,"languages":327,"metrics":328,"outcomeDisclosed":254,"sources":338,"verification":341,"grade":263,"id":342,"organizationSlug":265},"Swaledale Butchers: AI order entry with Choco OrderAgent",[226],{"name":322,"anonymized":233,"country":296,"region":183,"industry":19},"Swaledale Butchers",[324],{"name":273,"role":239},"Swaledale Butchers, a Yorkshire supplier of cuts and whole carcass butchery to chefs, received 70% of its orders through WhatsApp and the rest through procurement platforms, and typed each one into its butchery software in about two minutes. It integrated Choco's OrderAgent with its ERP on 8 July and went live on 21 August (the page gives no year). Choco's case study reports 8 seconds per order and 97% accuracy on bespoke specifications, with staff now verifying the automated entries.",[32,34],[244],[329,332],{"kpi":62,"value":330,"unit":248,"qualifier":305,"claimant":252,"quote":331,"sourceUrl":96},97,"97% accuracy, even on complex specifications",{"kpi":61,"value":333,"unit":248,"qualifier":334,"period":335,"baseline":336,"claimant":252,"quote":337,"sourceUrl":96},93,"approximately","per order","Around 2 minutes per order entered by hand","8 seconds per order processing time",[339],{"url":96,"title":340,"publisher":273},"Swaledale Butchers: 2 Mins to 8 Sec per Order with OrderAgent",{"level":262,"checkedAt":222},"swaledale-butchers-choco-order-entry",{"title":344,"useCases":345,"organization":346,"vendors":348,"summary":350,"stage":241,"year":351,"channels":352,"languages":353,"metrics":354,"outcomeDisclosed":254,"sources":361,"verification":365,"grade":263,"id":366,"organizationSlug":265},"Krystal Produce: AI order entry with Choco",[226],{"name":347,"anonymized":233,"country":234,"region":235,"industry":19},"Krystal Produce",[349],{"name":273,"role":239},"Krystal Produce, a produce supplier on Long Island, New York, uses Choco's AI to read customer orders and enter them into its system. Co owner Mike Longo says orders for one of its largest clients are processed in 13 seconds, that this saves around 15 hours a week and over $23,000 a year, and that one person now handles what would have needed five. Choco's case study adds that about 150 clients place roughly 1,500 orders a month through the Choco app and describes a \"zero issues or problems\" milestone, which is Choco's narration and not a quote from Longo.",2024,[],[244],[355],{"kpi":64,"value":356,"unit":357,"currency":105,"qualifier":249,"period":358,"claimant":282,"quote":359,"sourceUrl":360},23000,"currency","per year, for one large client","With Choco's AI we've processed orders in 13 seconds for one of our largest clients, saving us around 15 hours a week. This equates to over $23,000 in savings annually from this client alone.","https:\u002F\u002Fchoco.com\u002Fus\u002Fstories\u002Fcase-studies\u002Fkrystal-produce-success-story",[362],{"url":360,"title":363,"publisher":273,"archivedUrl":364},"Choco Success Story: Krystal Produce","https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20240229125211\u002Fhttps:\u002F\u002Fchoco.com\u002Fus\u002Fstories\u002Fcase-studies\u002Fkrystal-produce-success-story",{"level":262,"checkedAt":222},"krystal-produce-choco-order-entry",{"title":368,"useCases":369,"organization":370,"vendors":372,"summary":374,"stage":241,"year":351,"channels":375,"languages":376,"metrics":377,"outcomeDisclosed":254,"sources":384,"verification":387,"grade":263,"id":388,"organizationSlug":265},"North West Meat Company: AI order entry with Choco OrderAgent",[226],{"name":371,"anonymized":233,"country":234,"region":235,"industry":19},"North West Meat Company",[373],{"name":273,"role":239},"North West Meat Company, a Chicago meat distributor founded in 1956 that sells to restaurants and caterers, adopted Choco's OrderAgent in 2024. Restaurant orders arrive by phone, voicemail and email in free form (\"10 lbs of sirloin\") against a catalog of more than 1,300 SKUs, and the agent turns them into structured sales orders using each customer's order history. Choco's case study reports that nightly order entry fell from up to three hours to under one hour a day.",[31,33],[244],[378],{"kpi":61,"value":379,"unit":248,"qualifier":249,"period":380,"baseline":381,"claimant":252,"quote":382,"sourceUrl":383},67,"per day, after adopting OrderAgent","Up to three hours a day of manual order entry before OrderAgent","Reduced from 3 hours to under 1 hour per day","https:\u002F\u002Fchoco.com\u002Fus\u002Fstories\u002Fcase-studies\u002Fnorth-west-meat-company-saving-hours-daily-with-ai",[385],{"url":383,"title":386,"publisher":273},"North West Meat Company: Saving Hours Daily with Choco's AI",{"level":262,"checkedAt":222},"north-west-meat-company-choco-order-entry",0,[391,400,407,413],{"kpi":61,"label":392,"unit":248,"aggregate":254,"higherIsBetter":254,"n":393,"nUpTo":389,"median":247,"min":379,"max":333,"byClaimant":394,"vendorOnly":254,"points":395},"Cycle time reduction",4,{"organization":389,"vendor":393,"regulator":389,"independent":389},[396,397,398,399],{"evidenceId":342,"organization":322,"value":333,"qualifier":334,"claimant":252,"grade":263,"pooled":254},{"evidenceId":264,"organization":232,"value":247,"qualifier":249,"claimant":252,"grade":263,"pooled":254},{"evidenceId":317,"organization":295,"value":247,"qualifier":305,"claimant":252,"grade":263,"pooled":254},{"evidenceId":388,"organization":371,"value":379,"qualifier":249,"claimant":252,"grade":263,"pooled":254},{"kpi":62,"label":401,"unit":248,"aggregate":254,"higherIsBetter":254,"n":279,"nUpTo":389,"median":402,"min":304,"max":330,"byClaimant":403,"vendorOnly":254,"points":404},"Accuracy",96.5,{"organization":389,"vendor":279,"regulator":389,"independent":389},[405,406],{"evidenceId":342,"organization":322,"value":330,"qualifier":305,"claimant":252,"grade":263,"pooled":254},{"evidenceId":317,"organization":295,"value":304,"qualifier":305,"claimant":252,"grade":263,"pooled":254},{"kpi":64,"label":408,"unit":357,"currency":105,"aggregate":233,"higherIsBetter":254,"n":409,"nUpTo":389,"median":356,"min":356,"max":356,"byClaimant":410,"vendorOnly":233,"points":411},"Cost savings",1,{"organization":409,"vendor":389,"regulator":389,"independent":389},[412],{"evidenceId":366,"organization":347,"value":356,"qualifier":249,"claimant":282,"grade":263,"pooled":254},{"kpi":63,"label":414,"unit":280,"aggregate":233,"higherIsBetter":254,"n":409,"nUpTo":389,"median":279,"min":279,"max":279,"byClaimant":415,"vendorOnly":233,"points":416},"Hours saved",{"organization":409,"vendor":389,"regulator":389,"independent":389},[417],{"evidenceId":290,"organization":270,"value":279,"qualifier":249,"claimant":282,"grade":263,"pooled":254},{"low":419,"high":420},11250,90000,[422,455,480,507],{"slug":218,"title":423,"shortTitle":424,"definition":425,"status":9,"industries":426,"functions":429,"patterns":432,"audience":36,"autonomy":37,"adoptionStage":434,"evidenceCount":435,"publicEvidenceCount":436,"organizations":437,"bestGrade":450,"headline":451,"lastVerified":454,"indexable":254},"AI document intelligence for unstructured forms and documents","Intelligent document processing","Intelligent document processing is AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, and splits, classifies and extracts the required fields. Each field gets a confidence score, is validated against business rules and source systems, and only the uncertain cases go to a person before the data enters the downstream process.",[18,427,428,21],"government","automotive",[24,430,431],"case-management","finance-and-accounting",[27,433,29],"computer-vision","mainstream",14,12,[438,439,440,441,442,443,444,445,446,447,448,449],"Ancine","Banorte","CI Financial","Daman (The National Health Insurance Company)","Hirschbach Motor Lines","HM Revenue and Customs","Ministry of Human Resources and Emiratisation (MoHRE)","Pupuk Indonesia","RAKBANK","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group","B",{"kpi":61,"label":392,"unit":248,"n":93,"nUpTo":389,"kind":452,"value":453,"qualifier":305,"claimant":265,"organization":265,"vendorReported":233},"median",60,"2026-09-27",{"slug":219,"title":456,"shortTitle":457,"definition":458,"status":9,"industries":459,"functions":462,"patterns":464,"audience":36,"autonomy":37,"adoptionStage":434,"segment":36,"evidenceCount":466,"publicEvidenceCount":467,"organizations":468,"bestGrade":450,"headline":477,"lastVerified":454,"indexable":254},"AI for supplier invoice processing in accounts payable","Supplier invoice processing","AI supplier invoice processing is AI that captures supplier invoices in any format and extracts the header and line data. It matches invoices to purchase orders and goods receipts, proposes tax and cost center coding, flags duplicates and suspected fraud, and routes them for approval and posting, leaving only exceptions to accounts payable staff.",[18,460,427,20,461],"banking","energy-and-utilities",[431,463],"procurement",[27,28,465,29],"anomaly-detection",10,9,[469,470,471,472,473,474,475,447,476],"Adyen","Charter Hall","Federal Deposit Insurance Corporation","Fugro","Georgetown University","Kingfisher","Octopus Energy","Veolia",{"kpi":65,"label":478,"unit":248,"n":393,"nUpTo":389,"kind":452,"value":479,"qualifier":305,"claimant":265,"organization":265,"vendorReported":233},"Automation rate",70,{"slug":220,"title":481,"shortTitle":482,"definition":483,"status":9,"industries":484,"functions":486,"patterns":487,"audience":491,"autonomy":37,"adoptionStage":434,"evidenceCount":467,"publicEvidenceCount":467,"organizations":492,"bestGrade":450,"headline":502,"lastVerified":506,"indexable":254},"AI agent for order status, delivery changes and returns","Order status and returns","An AI order status and returns agent answers \"where is my order\", changes delivery details and arranges returns, exchanges and refunds for online and omnichannel shoppers. It does this end to end, by reading and writing to the order, carrier and returns systems within the retailer's policy, and hands exceptions such as damaged goods, disputes and upset customers to a person.",[18,20,485],"payments",[25,24],[488,28,489,490],"conversational-agent","voice-agent","rag-knowledge-assistant","customer-facing",[493,494,495,496,497,498,499,500,501],"BARK","Best Buy","Klarna","Next","NOBULL","Nuuly","Sun & Ski Sports","Vivid Seats","Woolworths Group",{"kpi":503,"label":504,"unit":248,"n":393,"nUpTo":389,"kind":452,"value":505,"qualifier":305,"claimant":252,"organization":265,"vendorReported":254},"customer-satisfaction","Customer satisfaction",92.5,"2026-09-26",{"slug":221,"title":508,"shortTitle":509,"definition":510,"status":9,"industries":511,"functions":512,"patterns":514,"audience":515,"autonomy":516,"adoptionStage":38,"evidenceCount":517,"publicEvidenceCount":517,"organizations":518,"bestGrade":263,"headline":522,"lastVerified":454,"indexable":254},"AI quote and estimate generation from customer requirements","Quote and estimate generation","Sales quote and estimate generation is AI that turns a customer's product list, drawing, roof photo or request for quotation into a draft quote for a seller or estimator. It reads the input, matches items to the catalog, calculates quantities and applies the organization's price rules, and the seller or estimator then checks and sends it.",[18,21,461,20],[23,513],"product-and-pricing",[27,28,433],"employee-facing","copilot",3,[519,520,521],"DeAcero","Enpal","The ODP Corporation",{"kpi":523,"label":524,"unit":248,"n":409,"nUpTo":389,"kind":525,"value":526,"qualifier":305,"claimant":252,"organization":520,"vendorReported":254},"handling-time-reduction","Handling time reduction","reported",87.5,{"indexable":254,"reasons":528},[],[530,535,539,547,554,558,564,571,579,586,592,598,604,610,617,623,629,635,640,647,652,657,663,669,676,681,686,691,698,703,711,717,722,728,734,739,744],{"id":176,"label":531,"issuer":182,"region":183,"url":532,"description":533,"useCases":534,"indexable":254},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",295,{"id":177,"label":536,"issuer":182,"region":183,"url":184,"description":537,"useCases":538,"indexable":254},"GDPR","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",265,{"id":540,"label":541,"issuer":542,"region":543,"url":544,"description":545,"useCases":546,"indexable":254},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",148,{"id":548,"label":549,"issuer":550,"region":235,"url":551,"description":552,"useCases":553,"indexable":254},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",115,{"id":178,"label":555,"issuer":192,"region":183,"url":193,"description":556,"useCases":557,"indexable":254},"UK GDPR","The UK's version of the GDPR, including rules on solely automated decisions.",84,{"id":559,"label":560,"issuer":182,"region":183,"url":561,"description":562,"useCases":563,"indexable":254},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",73,{"id":565,"label":566,"issuer":567,"region":183,"url":568,"description":569,"useCases":570,"indexable":254},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",53,{"id":572,"label":573,"issuer":574,"region":575,"url":576,"description":577,"useCases":578,"indexable":254},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":580,"label":581,"issuer":582,"region":575,"url":583,"description":584,"useCases":585,"indexable":254},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":587,"label":588,"issuer":182,"region":183,"url":589,"description":590,"useCases":591,"indexable":254},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",23,{"id":593,"label":594,"issuer":595,"region":543,"url":596,"description":597,"useCases":591,"indexable":254},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":599,"label":600,"issuer":601,"region":235,"url":602,"description":603,"useCases":591,"indexable":254},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":605,"label":606,"issuer":607,"region":235,"url":608,"description":609,"useCases":100,"indexable":254},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",{"id":611,"label":612,"issuer":613,"region":183,"url":614,"description":615,"useCases":616,"indexable":254},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":618,"label":619,"issuer":182,"region":183,"url":620,"description":621,"useCases":622,"indexable":254},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",15,{"id":624,"label":625,"issuer":626,"region":543,"url":627,"description":628,"useCases":622,"indexable":254},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":630,"label":631,"issuer":632,"region":235,"url":633,"description":634,"useCases":622,"indexable":254},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":636,"label":637,"issuer":182,"region":183,"url":638,"description":639,"useCases":435,"indexable":254},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",{"id":641,"label":642,"issuer":643,"region":235,"url":644,"description":645,"useCases":646,"indexable":254},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":648,"label":649,"issuer":182,"region":183,"url":650,"description":651,"useCases":646,"indexable":254},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":653,"label":654,"issuer":182,"region":183,"url":655,"description":656,"useCases":646,"indexable":254},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":658,"label":659,"issuer":660,"region":543,"url":661,"description":662,"useCases":646,"indexable":254},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":664,"label":665,"issuer":666,"region":235,"url":667,"description":668,"useCases":436,"indexable":254},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":670,"label":671,"issuer":672,"region":183,"url":673,"description":674,"useCases":675,"indexable":254},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":677,"label":678,"issuer":182,"region":183,"url":679,"description":680,"useCases":675,"indexable":254},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":682,"label":683,"issuer":182,"region":183,"url":684,"description":685,"useCases":675,"indexable":254},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":687,"label":688,"issuer":574,"region":575,"url":689,"description":690,"useCases":466,"indexable":254},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":692,"label":693,"issuer":694,"region":235,"url":695,"description":696,"useCases":697,"indexable":254},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",8,{"id":699,"label":700,"issuer":182,"region":183,"url":701,"description":702,"useCases":697,"indexable":254},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":704,"label":705,"issuer":706,"region":707,"url":708,"description":709,"useCases":710,"indexable":254},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",6,{"id":712,"label":713,"issuer":714,"region":183,"url":715,"description":716,"useCases":93,"indexable":254},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":718,"label":719,"issuer":182,"region":183,"url":720,"description":721,"useCases":393,"indexable":254},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":723,"label":724,"issuer":725,"region":183,"url":726,"description":727,"useCases":393,"indexable":254},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":729,"label":730,"issuer":731,"region":575,"url":732,"description":733,"useCases":517,"indexable":254},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":735,"label":736,"issuer":182,"region":183,"url":737,"description":738,"useCases":517,"indexable":254},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":740,"label":741,"issuer":574,"region":575,"url":742,"description":743,"useCases":517,"indexable":254},"mas-shared-responsibility-framework","MAS Shared Responsibility Framework","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fguidelines\u002Fguidelines-on-shared-responsibility-framework","Singapore's framework that assigns banks and telcos duties and loss sharing for phishing scams.",{"id":745,"label":746,"issuer":747,"region":235,"url":748,"description":749,"useCases":517,"indexable":254},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1792049858324]