{"id":159859,"date":"2026-11-09T11:17:56","date_gmt":"2026-11-09T11:17:56","guid":{"rendered":"https:\/\/teeptrak.com\/jemba-industrial-machine-learning\/"},"modified":"2026-11-09T11:17:56","modified_gmt":"2026-11-09T11:17:56","slug":"jemba-industrial-machine-learning","status":"publish","type":"page","link":"https:\/\/teeptrak.com\/en\/jemba-industrial-machine-learning\/","title":{"rendered":"Jemba, industrial machine learning by TEEPTRAK"},"content":{"rendered":"\n<div class=\"ttpp ttpp-jemba\"><style>.ttpp{font-family:'IBM Plex Sans',\"Segoe UI\",\"Helvetica Neue\",Arial,sans-serif;color:#22211F;--r:#EB352A;--a:#FF674C;--c:#22211F;--m:#8A8580;--l:#E6E3DF;--p:#F6F5F3}\n.ttpp *{box-sizing:border-box}\n.ttpp .wrap{max-width:1160px;margin:0 auto;padding:0 24px}\n.ttpp section{padding:72px 0}\n.ttpp .hero{background:#1A1A1A;color:#fff;padding:80px 0}\n.ttpp .hero h1{font-size:44px;line-height:1.08;letter-spacing:-.02em;margin:12px 0 18px;max-width:760px}\n.ttpp .hero h1 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class=\"wrap\"><span class=\"eyebrow\">Jemba \u00b7 Industrial machine learning<\/span><h1>Machine learning for the shop floor, <em>without a data scientist<\/em>.<\/h1><p class=\"sub\">Anomaly detection and process optimisation on the data your plant already produces. Two weeks to an answer, at plant scale, nothing installed on your network.<\/p><div class=\"actions\"><a class=\"btn red\" href=\"https:\/\/teeptrak.com\/en\/request-a-demo\/\">Request a demo<\/a><a class=\"btn ghost\" href=\"#how\">How it works<\/a><\/div><\/div><\/section><section id=\"what\"><div class=\"wrap\"><div class=\"head\"><span class=\"eyebrow\">What it computes<\/span><h2>Two questions, answered from your own data<\/h2><\/div><div class=\"g3\"><div class=\"card\"><h3>Anomaly detection<\/h3><p>&quot;What is going wrong?&quot; Every signal is evaluated continuously. Thresholds, drifts and alarms are generated automatically, and each alert is linked to the variables that explain it.<\/p><\/div><div class=\"card\"><h3>Process optimisation<\/h3><p>&quot;How do we produce better?&quot; The influence of each variable on your target is quantified and returned as setpoint ranges the line can use, not as models.<\/p><\/div><div class=\"card\"><h3>Written for the shop floor<\/h3><p>Data from any source and format is ingested, cleaned and structured by Jemba. The model is selected automatically, trained in one click and checked for reliability before anything is shown.<\/p><\/div><\/div><\/div><\/section><section id=\"how\" class=\"paper\"><div class=\"wrap\"><div class=\"head\"><span class=\"eyebrow\">Proof of value<\/span><h2>Two weeks to a decision, one way or the other<\/h2><\/div><div class=\"steps\"><div class=\"step\"><span class=\"k\">01<\/span><h3>Week 1 \u00b7 Scope and data<\/h3><p>One line, one target variable, one export. Jemba analyses your extract within a week, before any commitment on your side.<\/p><\/div><div class=\"step\"><span class=\"k\">02<\/span><h3>Week 2 \u00b7 Models on your data<\/h3><p>Results are reviewed with your process lead as they come in, on your lines and your history.<\/p><\/div><div class=\"step\"><span class=\"k\">03<\/span><h3>Then \u00b7 A decision<\/h3><p>A validated case, or a documented no that you keep. Either way you know what your data can and cannot tell you.<\/p><\/div><\/div><\/div><\/section><section class=\"proofsec\"><div class=\"wrap\"><div class=\"head\"><span class=\"eyebrow\">Proven on real lines<\/span><h2>Results measured, not projected<\/h2><\/div><div class=\"proof\"><div class=\"pr\"><b>273 h<\/b><p>Stops anticipated over six months on a 4-machine line of an aerospace and defence supplier, 45 % of the line total, from the run\/stop log already collected. No new sensor, no work on the machines, confirmed by backtest.<\/p><\/div><div class=\"pr\"><b>+9.5 pts<\/b><p>Material transformation rate on a textile coil line of a rubber and polymer supplier: 977 columns reduced to 10 levers, 78.1 % in the recommended zone against 68.6 % over the whole history, measured on 33 runs the line had already made. Zero customer man-days.<\/p><\/div><\/div><\/div><\/section><section class=\"factsec\"><div class=\"wrap\"><div class=\"head\"><h2>Security and data<\/h2><\/div><ul class=\"facts\"><li>Own servers in France and the United States, no third-party cloud subcontractor<\/li><li>No customer data used to train models shared with other customers<\/li><li>Permanent deletion of data and derived models on request<\/li><li>SAML SSO and role-based access<\/li><\/ul><\/div><\/section><section class=\"cta\"><div class=\"wrap\"><h2>Name a line and a target variable.<\/h2><p class=\"lead\">A TEEPTRAK engineer takes it from there: scope, export, first results within a week. Jemba is part of the TEEPTRAK group.<\/p><div class=\"actions\"><a class=\"btn red\" href=\"https:\/\/teeptrak.com\/en\/request-a-demo\/\">Request a demo<\/a><\/div><\/div><\/section><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Jemba \u00b7 Industrial machine learning Machine learning for the shop floor, without a data scientist. Anomaly detection and process optimisation on the data your plant already produces. Two weeks to an answer, at plant scale, nothing installed on your network. Request a demoHow it works What it computes Two questions, answered from your own data [&hellip;]<\/p>\n","protected":false},"author":384731,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"Jemba, industrial machine learning by TEEPTRAK","_yoast_wpseo_metadesc":"Anomaly detection and process optimisation on the data your plant already produces, without a data scientist. Two weeks to an answer. 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