[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"solutions-nav":3,"insight-behind-the-scenes-ai-training-customer-orders":105},[4,14,22,30,38,46,54,63,72,81,89,97],{"slug":5,"category":6,"sort":7,"translations":8},"food","industries",0,[9],{"languages_code":10,"meta_description":11,"breadcrumb_label":12,"nav_label":12,"nav_tagline":13},"en","Hyperfox automates order intake, validation and routing for food distributors.","Food","Automate intake and routing for high-volume food orders.",{"slug":15,"category":6,"sort":16,"translations":17},"oil-gas-chemicals",1,[18],{"languages_code":10,"meta_description":19,"breadcrumb_label":20,"nav_label":20,"nav_tagline":21},"In a sector where precision and speed are critical, manual order processing creates inefficiencies, increases errors, and adds unnecessary risk. Hyperfox automates your workflows, from email orders to contract validations, allowing you to focus on delivering value to your customers while improving operational efficiency.","Oil, gas & chemicals","Automate precision order flows for oil, gas and chemicals distribution.",{"slug":23,"category":6,"sort":24,"translations":25},"pharmaceutical",2,[26],{"languages_code":10,"meta_description":27,"breadcrumb_label":28,"nav_label":28,"nav_tagline":29},"Pharmaceutical distribution demands precision, speed, and compliance. Manual order processing increases the risk of errors, slows down operations, and creates unnecessary strain on your team. Hyperfox automates your workflows, from email orders to budget approvals, ensuring every order is accurate, compliant, and processed efficiently.","Pharmaceutical","Reduce risk and manual workload in pharmaceutical order processing.",{"slug":31,"category":6,"sort":32,"translations":33},"retail",3,[34],{"languages_code":10,"meta_description":35,"breadcrumb_label":36,"nav_label":36,"nav_tagline":37},"Retail thrives on speed, accuracy, and the ability to manage multiple sales channels. Manual order processing creates inefficiencies, introduces errors, and limits scalability. Hyperfox automates your workflows, from email orders to eCommerce integrations, so you can focus on delivering exceptional customer experiences while streamlining operations.","Retail","Automate orders across email, EDI, eCommerce and marketplaces.",{"slug":39,"category":6,"sort":40,"translations":41},"transport-logistics",4,[42],{"languages_code":10,"meta_description":43,"breadcrumb_label":44,"nav_label":44,"nav_tagline":45},"Transport and logistics depend on precision, speed, and accuracy. Manual order processing creates bottlenecks, increases costs, and leads to operational inefficiencies. Hyperfox automates your workflows, from email orders to EDI integrations, enabling your team to focus on delivering reliable, on-time services to your customers.","Transport & logistics","Streamline transport order intake from email to TMS.",{"slug":47,"category":6,"sort":48,"translations":49},"warehousing-distribution",5,[50],{"languages_code":10,"meta_description":51,"breadcrumb_label":52,"nav_label":52,"nav_tagline":53},"Efficient operations in warehousing and distribution depend on accurate and timely order processing. Manual order entry creates delays, introduces errors, and increases costs. Hyperfox automates your order processes, from inventory validation to WMS integrations, helping you streamline order operations and deliver great customer experiences.","Warehousing & distribution","Automate order entry, validation and WMS routing at scale.",{"slug":55,"category":56,"sort":57,"translations":58},"complex-b2b-orders","use-cases",6,[59],{"languages_code":10,"meta_description":60,"breadcrumb_label":61,"nav_label":61,"nav_tagline":62},"Many B2B companies don’t struggle with order volume; they struggle with order complexity. Customer-specific pricing, units, delivery rules, budgets and approvals make manual processing slow and error-prone. Hyperfox automates complex B2B order processes by validating every order against your commercial and operational rules before it reaches your ERP or TMS.","Complex B2B orders","Validate customer-specific rules without manual exceptions.",{"slug":64,"category":56,"sort":65,"translations":66},"edi-automation",7,[67],{"languages_code":10,"meta_description":68,"breadcrumb_label":69,"nav_label":70,"nav_tagline":71},"Centralise EDI and API orders, validate them before execution, and route them into ERP/TMS.","Build effortless EDI integrations with AI","EDI Automation","Centralise EDI and API orders without fragile integrations.",{"slug":73,"category":56,"sort":74,"translations":75},"process-stock-orders",8,[76],{"languages_code":10,"meta_description":77,"breadcrumb_label":78,"nav_label":79,"nav_tagline":80},"Email remains the most common way customers place B2B orders. PDFs, spreadsheets and informal messages flood the inbox, forcing back-office teams to retype, interpret and correct orders manually. Hyperfox automates email order intake with AI, validating every order and creating clean sales or stock orders directly in your ERP.","Email to ERP","Email order automation","Process email orders into ERP without manual retyping.",{"slug":82,"category":56,"sort":83,"translations":84},"process-transport-orders",9,[85],{"languages_code":10,"meta_description":86,"breadcrumb_label":87,"nav_label":87,"nav_tagline":88},"Transport orders often arrive as emails, PDFs, or notes with pickup & delivery instructions. Interpreting them manually is slow, error-prone, and costly. Hyperfox uses AI to extract transport data, validate business and delivery rules, and create clean transport orders in your TMS.","Transport order automation","Turn unstructured transport requests into clean TMS entries.",{"slug":90,"category":56,"sort":91,"translations":92},"voicemail-orders",10,[93],{"languages_code":10,"meta_description":94,"breadcrumb_label":95,"nav_label":95,"nav_tagline":96},"In many B2B environments, customers still place orders by phone. When calls are missed, orders end up in voicemail messages that must be listened to, interpreted and retyped manually. Hyperfox uses AI to convert voicemail orders into structured data, validate them against your rules, and create clean orders in your ERP or TMS.","Voicemail orders","Convert spoken orders into validated ERP or TMS entries.",{"slug":98,"category":56,"sort":99,"translations":100},"realtime-status-updates",11,[101],{"languages_code":10,"meta_description":102,"breadcrumb_label":103,"nav_label":103,"nav_tagline":104},"B2B marketplaces and enterprise customers expect full transparency on their orders. Hyperfox automates real-time status updates, ensuring customers receive accurate, up-to-date information on order progress, stock availability, and delivery timelines, without manual follow-ups.","Realtime status updates","Push live order and stock updates to customers without follow-up.",{"article":106,"related":142},{"id":107,"slug":108,"type":109,"status":110,"published_at":111,"author_name":112,"thumbnail":113,"reading_time_minutes":40,"tags":114,"translations":136},"migrated-behind-the-scenes-ai-training-customer-orders","behind-the-scenes-ai-training-customer-orders","blog","published","2025-06-01T10:00:00","Hyperfox","/cms-assets/48f29782-96a0-417e-ba35-74ea631839f1",[115,125],{"slug":116,"translations":117},"ai",{"en":118,"nl":120,"fr":121,"de":123},{"label":119},"AI",{"label":119},{"label":122},"IA",{"label":124},"KI",{"slug":126,"translations":127},"order-automation",{"en":128,"nl":130,"fr":132,"de":134},{"label":129},"Order Automation",{"label":131},"Orderautomatisering",{"label":133},"Automatisation des commandes",{"label":135},"Auftragsautomatisierung",{"en":137},{"title":138,"meta_description":139,"excerpt":140,"body":141},"How we train AI on real orders without storing the data","How does Hyperfox train AI on real customer orders without storing sensitive data? We use context modelling — no raw documents, no GDPR risk, just structure.","Improving order AI requires real customer orders. But storing that data creates privacy risks. Here is the approach we chose and why it works.","\u003Cp>Most AI solutions for order processing need to store your raw customer data to learn from it. That creates GDPR risks and ties you to a single vendor.\u003C/p>\n\u003Cp>We chose a different path: we train on structure and context, not on stored documents. In this article, we explain how context modelling works and why it consistently reaches above 95% accuracy without ever retaining sensitive order data.\u003C/p>\n\u003Ch3>What are the three approaches to AI-powered order processing?\u003C/h3>\u003Cp>Building AI for order automation requires training data. The real question is where that data comes from and what happens to it.\u003C/p>\u003Cp>In practice, we see three common approaches. Most solutions choose one of the first two. We deliberately chose the third.\u003C/p>\u003Ch5>Approach 1: OCR combined with machine learning\u003C/h5>\u003Cp>The first approach starts with OCR. A document is scanned, text is extracted and a model learns patterns over time. \u003Ca href=\"/products/ai-order-agent\">Learn more about the Hyperfox AI Order Agent\u003C/a>.\u003C/p>\u003Cp>It sounds logical. In reality it breaks quickly.\u003C/p>\u003Cp>OCR struggles with handwriting, unusual layouts and low quality scans. The machine learning layer behind it requires large volumes of labelled data before it becomes reliable.\u003C/p>\u003Cp>For B2B distributors dealing with hundreds of different order formats, from PDFs to spreadsheets to photos, this quickly becomes a maintenance challenge. Every new edge case requires retraining.\u003C/p>\u003Cp>‍\u003C/p>\u003Ch5>Approach 2: fine-tuning a large language model\u003C/h5>\u003Cp>Another approach is to train or fine-tune a large language model on internal order data.\u003C/p>\u003Cp>The idea is that the model will eventually learn the company specific context.\u003C/p>\u003Cp>But this comes with trade-offs. You need to store large volumes of raw order data, allocate significant compute resources and iterate for months before the model stabilises.\u003C/p>\u003Cp>You are also tied to a specific model architecture. When a provider releases a new version, the fine-tuning process may have to start again.\u003C/p>\n\u003Ch5>Approach 3: context modelling (what we actually do)\u003C/h5>\u003Cp>We chose a third path called context modelling.\u003C/p>\u003Cp>Instead of feeding raw orders into a training pipeline, we structure the context around the order.\u003C/p>\u003Cp>For each customer we define what a valid order looks like. That includes product catalogues, pricing agreements, delivery rules, packaging conventions and the typical exceptions handled by the team.\u003C/p>\u003Cp>This structured context is what the AI works with. Not the raw document itself.\u003C/p>\u003Cp>When an order arrives, whether it is a PDF, Excel file, email body or even a voice note, the process works like this.\u003C/p>\u003Cp>The AI reads the document and extracts the relevant fields.\u003Cbr>The system validates those fields against the customer specific rules.\u003Cbr>A human reviews the exceptions.\u003Cbr>Feedback from that review improves the system over time.\u003C/p>\u003Cp>What we do not retain are raw orders, identifiable customer data, pricing details or delivery locations.\u003C/p>\u003Cp>What we keep is the structure, the validation rules and the edge cases that help improve accuracy.\u003C/p>\u003Cp>In short, we learn from patterns, not from stored documents.\u003C/p>\n\u003Ch5>Why does context modelling matter in practice?\u003C/h5>\u003Cp>This approach creates several advantages that reinforce each other over time.\u003C/p>\u003Cp>\u003Cstrong>Fast onboarding\u003C/strong>\u003C/p>\u003Cp>Because we do not train a new model for every customer, implementation takes days rather than months.\u003C/p>\u003Cp>The system learns from structure rather than volume. Once the customer context layer is configured with rules, mappings and exceptions, the AI can start processing orders immediately.\u003C/p>\u003Cp>\u003Cstrong>Model independence\u003C/strong>\u003C/p>\u003Cp>Context modelling means we are not tied to a single model provider.\u003C/p>\u003Cp>If a better model becomes available tomorrow, we can switch without rebuilding everything. The intelligence lives in the structured context layer.\u003C/p>\u003Cp>\u003Cstrong>Privacy as a design principle\u003C/strong>\u003C/p>\u003Cp>We do not need to store sensitive order data to improve accuracy.\u003C/p>\u003Cp>For some customers, this approach has pushed accuracy above 95 percent over time. Not by compromising privacy, but by designing the system around it.\u003C/p>\u003Cp>‍\u003C/p>\u003Cp>This philosophy also powers our Codex feature.\u003C/p>\u003Cp>Codex is a structured knowledge layer where customer specific interpretation rules become explicit and machine readable. The business logic that normally lives in someone's head becomes part of the system.\u003C/p>\u003Cp>That is how a line like \"4x mayonnaise\" can automatically translate to pallets for one customer and buckets for another.\u003C/p>\n\u003Ch5>The real difference\u003C/h5>\u003Cp>Many vendors in the order automation space claim AI powered processing.\u003C/p>\u003Cp>In practice this often means OCR with a cleaner interface, or models that need months of data collection before they deliver value.\u003C/p>\u003Cp>We believe the better question is not how smart the AI is.\u003C/p>\u003Cp>The real question is how well the system understands your business.\u003C/p>\u003Cp>That understanding does not require storing customer orders. It requires structuring the rules, the context and the exceptions and validating every order before it reaches your ERP.\u003C/p>\u003Cp>That is what context modelling enables.\u003C/p>\u003Cp>If you want to see how this works with your order types, book a demo with our team. We will gladly walk you through a live example.\u003C/p>\n\u003Cp>\u003Cstrong>Ready to see how this works for your business?\u003C/strong> \u003Ca href=\"/book-a-demo\">Book a demo\u003C/a> and we'll walk you through a live example with your own order data.\u003C/p>",[143,180,207],{"id":144,"slug":145,"type":109,"published_at":146,"author_name":147,"thumbnail":148,"reading_time_minutes":91,"tags":149,"translations":175},"insight-which-channel-automate-first","which-order-channel-to-automate-first","2026-04-15T10:00:00","Stephen Henckaerts","/cms-assets/20d034d2-fc54-4c8a-899b-9ab3e7eb5df1",[150,156,164],{"slug":126,"translations":151},{"en":152,"nl":153,"fr":154,"de":155},{"label":129},{"label":131},{"label":133},{"label":135},{"slug":157,"translations":158},"edi",{"en":159,"nl":161,"fr":162,"de":163},{"label":160},"EDI",{"label":160},{"label":160},{"label":160},{"slug":165,"translations":166},"b2b-logistics",{"en":167,"nl":169,"fr":171,"de":173},{"label":168},"B2B Logistics",{"label":170},"B2B-logistiek",{"label":172},"Logistique B2B",{"label":174},"B2B-Logistik",{"en":176},{"title":177,"meta_description":178,"excerpt":179},"EDI vs. Email Orders vs. Customer Portal: Which Channel Should You Automate First?","Compare EDI, email, and portal order channels. A practical decision framework to help manufacturers and distributors pick the right automation starting point.","Your orders arrive from everywhere. You can't automate all channels at once. Here's a data-backed framework to help you pick the right starting point based on your order mix.",{"id":181,"slug":182,"type":109,"published_at":146,"author_name":147,"thumbnail":183,"reading_time_minutes":83,"tags":184,"translations":202},"insight-cost-manual-order-entry","the-real-cost-of-manual-order-entry","/cms-assets/5ebe0344-94dc-4604-a85a-bbbea3560e56",[185,191],{"slug":126,"translations":186},{"en":187,"nl":188,"fr":189,"de":190},{"label":129},{"label":131},{"label":133},{"label":135},{"slug":192,"translations":193},"erp-integration",{"en":194,"nl":196,"fr":198,"de":200},{"label":195},"ERP Integration",{"label":197},"ERP-integratie",{"label":199},"Intégration ERP",{"label":201},"ERP-Integration",{"en":203},{"title":204,"meta_description":205,"excerpt":206},"The Real Cost of Manual Order Entry in B2B Distribution","Manual order entry costs B2B distributors far more than they realize. Use our 5-minute framework to calculate your true cost and build a case for automation.","Most distributors know manual order entry is slow. Fewer have calculated what it actually costs. Here is a framework to quantify the visible and hidden costs.",{"id":208,"slug":209,"type":109,"published_at":210,"author_name":112,"thumbnail":211,"reading_time_minutes":24,"tags":212,"translations":225},"migrated-ai-in-the-back-office-10-challenges-for-automation-in-the-ordering-process","ai-in-the-back-office-10-challenges-for-automation-in-the-ordering-process","2025-05-15T10:00:00","/cms-assets/e629a518-5e53-4b39-b0fe-1db5ca2ac70f",[213,219],{"slug":116,"translations":214},{"en":215,"nl":216,"fr":217,"de":218},{"label":119},{"label":119},{"label":122},{"label":124},{"slug":126,"translations":220},{"en":221,"nl":222,"fr":223,"de":224},{"label":129},{"label":131},{"label":133},{"label":135},{"en":226},{"title":227,"meta_description":228,"excerpt":229},"AI in the Back Office: Automation Challenges","What are the 10 biggest challenges food distributors face when automating order processing? Download our whitepaper for practical AI solutions.","10 challenges for automation in the ordering process. Download whitepaper."]