{"id":3210,"date":"2026-09-23T10:08:16","date_gmt":"2026-09-23T09:08:16","guid":{"rendered":"https:\/\/todoesdata.com\/?page_id=3210"},"modified":"2026-09-23T10:08:16","modified_gmt":"2026-09-23T09:08:16","slug":"machine-learning","status":"publish","type":"page","link":"https:\/\/todoesdata.com\/en\/products\/machine-learning\/","title":{"rendered":"Machine Learning"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"","protected":false},"author":8,"featured_media":0,"parent":3156,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"tpls\/MarketingNew.php","meta":{"_acf_changed":false,"_yoast_wpseo_focuskw":"machine learning consulting","_yoast_wpseo_title":"Machine Learning consulting: predictive models %%sep%% %%sitename%%","_yoast_wpseo_metadesc":"Machine Learning consulting: demand forecasting, late-payment prediction and predictive maintenance, trained on your data and built into your operations.","footnotes":""},"class_list":["post-3210","page","type-page","status-publish","hentry"],"acf":{"title":"Machine","title2":"Learning","short_desc":"

Machine Learning consulting and predictive models.<\/h1> Your company has spent years storing sales, orders, breakdowns and payments. Somewhere in there are the next stock-out, the customer who is about to stop paying and the machine that is about to fail.\n

\nWe train models on your history and build them into your operations<\/strong> so they warn you before it happens.","line_mark":null,"text_block":"","blocks":null,"logo_slider_title":"","logo_slider":null,"3column_title":"","3column_block":null,"secciones":[{"acf_fc_layout":"definicion","fondo":"blanco","kicker":"Machine Learning","titulo":"What Machine Learning means for a business","lead":"Models trained on your company's historical data that predict what is going to happen, and that work inside your operations, not in a report.","resaltado":"predict what is going to happen","cuerpo":"A machine learning model learns from what has already happened: sales by item and week, invoices that were paid late, sensor readings before a breakdown. It then returns a figure about what comes next. How many units will sell next week, how likely this customer is to stop paying, how many days that bearing has left.\n\nWhat sets this kind of predictive analytics apart from a spreadsheet full of statistics is that the model finds patterns nobody programmed<\/strong>. The seasonality of each shop, the effect of a promotion on the product next to it, the combination of signals that comes before a missed payment. And what sets it apart from the AI you already use is that it is trained only on the data it needs to predict, with no noise around it.","datos":[{"cifra":"1","etiqueta":"business decision per model, agreed before training"},{"cifra":"24 h","etiqueta":"between new data and the updated prediction"},{"cifra":"0","etiqueta":"decisions made on gut feeling alone"}]},{"acf_fc_layout":"triptico","fondo":"naranja","titulo":"Three quick questions","intro":"Three questions from the first meeting, with short answers.","items":[{"etiqueta":"What it is","pregunta":"Isn't this the same as ChatGPT?","respuesta":"No. ChatGPT writes and summarises; this calculates how much, when and with what probability. The two complement each other, but they do not solve the same problem."},{"etiqueta":"Who it is for","pregunta":"Does it make sense for my company?","respuesta":"Yes, if you have two or three years of history in your ERP and a decision that is currently made by eye. How much to buy, who to give credit to, when to stop the machine."},{"etiqueta":"When","pregunta":"How long until it is up and running?","respuesta":"The first model, evaluated on your data, takes 6 to 10 weeks. In production and writing to your systems, around three months. We start with the prediction that moves the most money."}]},{"acf_fc_layout":"tarjetas_icono","fondo":"gris","titulo":"How to tell you need it","intro":"People come to machine learning because of one specific decision that is currently made by eye. It is usually one of these four.","items":[{"icono":"capas","titulo":"You buy by eye and pay for it both ways","texto":"The buyer orders 'same as last year, plus a bit'. The result is a warehouse full of what does not sell and stock-outs in what does.","enlace":null},{"icono":"alerta","titulo":"You hear about a bad debt once it is already bad","texto":"The customer who stops paying had been sending signals for months: smaller orders, longer and longer delays, a new contact person. Nobody put them together in time.","enlace":null},{"icono":"engranaje","titulo":"The machine stops when it wants, not when it should","texto":"Maintenance runs on a calendar or on breakdowns. Both are expensive: one replaces healthy parts, the other stops the line on the worst day of the month.","enlace":null},{"icono":"diana","titulo":"Promotions are decided on instinct","texto":"The offer goes out, sales go up and nobody knows whether margin was gained or lost. The next promotion is decided the same way.","enlace":null}]},{"acf_fc_layout":"tarjetas_imagen","fondo":"blanco","titulo":"What we build","filtro":false,"intro":"Six pieces. The first two are the model and the data it learns from. The other four turn that model into a system the company uses every day.","items":[{"imagen":3164,"etapa":"","titulo":"The model","texto":"Trained on your history, it returns a figure or a probability for each case: units to sell, risk of late payment, a customer about to leave, a machine about to fail. Its margin of error is measured against the method you use today, not estimated.","enlace":null},{"imagen":3211,"etapa":"","titulo":"The ETL for your source data","texto":"Every night the data from the ERP, the CRM or the sensors is collected, cleaned and joined up. It ends up in the table the model learns from. This is the piece that takes the most work, and the one that decides whether the model gets it right.","enlace":null},{"imagen":3213,"etapa":"","titulo":"Built into your operations","texto":"The prediction arrives where the decision is made: a column in the ERP, a proposed order, an alert to maintenance. Nobody has to open a notebook or export anything.","enlace":null},{"imagen":3215,"etapa":"","titulo":"Model accuracy dashboard","texto":"How often it is right, which items it gets wrong and how much it improves on the previous method, measured on the same data. In euros of overstock avoided, not in technical metrics.","enlace":null},{"imagen":3217,"etapa":"","titulo":"Monitoring and retraining","texto":"When the business changes, the model ages. We measure drift every day, and retraining starts automatically once the agreed threshold is crossed.","enlace":null},{"imagen":3219,"etapa":"","titulo":"An explanation for every prediction","texto":"Each figure comes with its reasons: which factors weighed in and by how much. Purchasing and finance can challenge the prediction instead of accepting it blindly or ignoring it.","enlace":null}]},{"acf_fc_layout":"pasos","fondo":"gris","titulo":"How we work","intro":"Five steps, and the first one does not mention algorithms.","items":[{"titulo":"A decision that costs money","texto":"'We overbuy on a third of our items' works. 'We want to do machine learning' has no clear goal, and it will fail."},{"titulo":"Your data, as it is","texto":"How many years of history there are, what is missing and what is dirty. Within two weeks we tell you whether there is a model or whether you need to start with the data. If the answer is 'not yet', we say so."},{"titulo":"A model tested against the past","texto":"We train on the previous years and check how accurate it would have been in the last one. If it does not clearly beat the method you use today, it goes no further."},{"titulo":"In production, on a small scale","texto":"One warehouse, one product family, one customer portfolio. The prediction goes into the real system and the team uses it alongside their own judgement for a few weeks."},{"titulo":"Scale up and maintain","texto":"It is extended to the rest of the business, with automatic monitoring and retraining. The model, the code and the documentation are yours."}]},{"acf_fc_layout":"logos","fondo":"blanco","titulo":"We work with the technology you already have","muro":false,"items":[{"nombre":"Python","logo":null,"enlace":null},{"nombre":"scikit-learn","logo":null,"enlace":null},{"nombre":"XGBoost","logo":null,"enlace":null},{"nombre":"Microsoft Azure","logo":null,"enlace":null},{"nombre":"SQL Server","logo":null,"enlace":null},{"nombre":"Power BI","logo":null,"enlace":null}]},{"acf_fc_layout":"diagrama","fondo":"naranja","titulo":"Machine Learning and AI: how they differ","texto":"The generative AI you use is built on machine learning techniques, so under the bonnet they are related. In practice, though, they do quite different jobs. Machine learning predicts from numbers; generative AI reasons over language. A predictive model tells you how many units of each item will sell next week, and with what margin of error. An AI agent reads that figure, checks the stock and the supplier's lead time, and drafts the order.\n\nThey need each other. An agent without a predictive model decides on borrowed instinct; a model without an agent leaves the figure in a table, waiting for someone to look at it. The model supplies the reliable figure; the agent supplies the action.\n\nIn practice they are chained together. The late-payment model flags a customer as a likely delay; the agent prepares a note for the sales rep with the history and suggests adjusting the credit limit. A person decides in one click.","diagrama":"machine_learning","imagen":2846},{"acf_fc_layout":"slider","titulo":"Where it applies","items":[{"titulo":"Demand forecasting","texto":"How many units of each item will sell next week or next month, by shop or warehouse. It is the basis for stock that is neither too much nor too little, and for purchasing that is no longer done by eye.","imagen":2911,"enlace":null},{"titulo":"Customer scoring: late payment and churn","texto":"Which customers are most likely to pay late or stop buying, and which signal gives it away. Finance adjusts their credit, and sales picks up the phone before they are lost.","imagen":2913,"enlace":null},{"titulo":"Predictive maintenance","texto":"How long a machine has left before it fails, based on its sensors and its breakdown history. The part is replaced when it is due, not by the calendar or after the line has stopped.","imagen":2897,"enlace":null},{"titulo":"Price and promotion optimisation","texto":"Which price or promotion delivers the best margin on each product, not just the most sales. You stop promoting what was already selling, and stop cannibalising what actually made a profit.","imagen":2909,"enlace":null},{"titulo":"Anomaly and fraud detection","texto":"Transactions, consumption or readings that fall outside what is expected, detected as they happen. Billing errors, leaks, orders that do not fit the customer's usual pattern.","imagen":2875,"enlace":null}]},{"acf_fc_layout":"faq","fondo":"gris","titulo":"Frequently asked questions","intro":"The ones asked before anything is trained. If yours is missing, write to us.","enlace":{"title":"Let's talk about your project","url":"https:\/\/todoesdata.com\/en\/contact\/","target":""},"items":[{"pregunta":"How much data do you need to train a model?","respuesta":"Less than people tend to think and, sometimes, more than there is. For demand forecasting, two or three years of sales by item and week are usually enough; for late payments, a few hundred real delays. What cannot be missing is consistency: if an item has changed code three times, that has to be fixed first. The initial assessment tells you within two weeks."},{"pregunta":"What accuracy can I expect?","respuesta":"It depends on the problem and on how noisy your business is, and anyone who promises a percentage before seeing your data is misleading you. What we do guarantee is the comparison: we measure the model against the method you use today, on last year's data. If it does not clearly improve on it, it is not deployed. The exact figure comes from your history, not from a promise."},{"pregunta":"What happens when the business changes?","respuesta":"The model ages: new products, another channel, a price change, an unusual year. That is why we do not deliver a model but a system that is monitored. Every day we measure the gap between what was predicted and what happened, and once the agreed threshold is crossed it is retrained on the new data. A fundamental change, such as a new line of business, means adjusting the model, not starting a project from scratch."},{"pregunta":"How much does a machine learning project cost?","respuesta":"The cost is driven by the state of the data, not by the complexity of the algorithm. Within that, it depends on how many sources and decisions the model covers. The price is set once the two-week assessment is done, and that assessment is useful even if you decide not to go further."},{"pregunta":"Do I need a data platform first?","respuesta":"Not always. If the history sits in an accessible and reasonably clean ERP, the model can read from there. When several sources need joining up or volumes grow, we build the data platform<\/a> as part of the project. That is what lets the model retrain itself, and lets an AI agent use its prediction later on."},{"pregunta":"And what does this have to do with AI agents?","respuesta":"An AI agent that handles purchasing needs to know how much will sell; one that handles collections needs to know who is going to pay late. That figure comes from the machine learning model. Agents act; models give them the number to decide on. That is why many agent projects start here."},{"pregunta":"Which industries do you use it in?","respuesta":"It leads in none, and that says something about the product: it almost always arrives second, once there is clean data to learn from. It comes second in three industries. In travel and hospitality<\/a>, with occupancy forecasting. In manufacturing and food<\/a>, with maintenance scheduled by real risk. And in logistics and supply chain<\/a>, with stock calculated on the lead time each supplier actually meets."}]}]},"yoast_head":"\nMachine Learning consulting: predictive models | Todoesdata<\/title>\n<meta name=\"description\" content=\"Machine Learning consulting: demand forecasting, late-payment prediction and predictive maintenance, trained on your data and built into your operations.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/todoesdata.com\/en\/products\/machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning consulting: predictive models | Todoesdata\" \/>\n<meta property=\"og:description\" content=\"Machine Learning consulting: demand forecasting, late-payment prediction and predictive maintenance, trained on your data and built 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