Training models, neural networks, and predictive algorithms that learn from data
How to build a nightly loop in a Solidus shop that reads competitors' public prices, stock, costs and elasticity, sets each product's price inside limits, moves ad budget towards profit, and stays inside UK consumer and competition law. Formulas, worked examples and Rails code.
k means, latent class analysis and RFM compared on an invented hamper shop: how to choose the number of segments, profile and name them, check that they are stable and put them to work.
What the dot com crash really looked like, what the biggest AI spenders report in their own filings, where AI genuinely earns its keep, and the hidden costs a business should price in before it spends.
How PCA and factor analysis turn 24 survey questions or 18 behavioural variables into a few dimensions you can name, score and act on, with a hotel survey and a hamper shop worked through.
How to forecast weekly demand with seasonal dummies, handle autocorrelation, judge models on a holdout and plan stock and staff against an 80% error band.
Seven in 10 carts are abandoned and a sixth of US retail sales come back. Two predictive systems, an abandonment risk scorer and a return risk predictor, built in Python and wired into Solidus, with the business case worked through properly.
How dynamic pricing actually works for a shop: estimating elasticity from your own orders, the markup rule, a demand model, the guardrails, and the UK price transparency and competition rules, with Solidus and Rails code.
Shoppers increasingly ask an assistant instead of typing keywords. What that means for a shop's product pages, descriptions and structured data, with UK dates from Google and a Rails example.
SARIMA, Prophet and gradient boosted trees compared for a shop's weekly sales: when each earns its place, how to test them honestly, and how a forecast becomes a reorder point in Rails and Solidus.
How logistic regression ranks customers, where the next brochure stops paying for itself, and how the same model turns baskets into cross sell recommendations.
Browsers have closed most of the gaps and UK law asks for a yes before you personalise. How to build personalisation on consent, your own server and data people give you on purpose, with the Rails code.
Germany and Austria share the EU AI Act and the GDPR but not the enforcers. Switzerland has neither, and a data protection act that already covers AI. What a British business needs to know, and how to build one Rails codebase for all three.
A developer's walkthrough of the processing contract, data protection by design and the impact assessment for AI features, under the UK GDPR and, for EU customers, the EU GDPR. The law first, then the Rails code.
Deleting a customer's rows is easy. Their influence on a trained model is not. What Article 17 says in the UK and the EU, what regulators have actually said about models, what machine unlearning research can do, and the Rails architecture that keeps you out of the problem.
Most marketing automation is still a timer with a mailing list. What changes when models decide instead: guarded dynamic pricing, recommendations that read context, churn prediction that acts in time, and campaign agents, built on Rails and Solidus within UK data protection rules.
Most AI projects stall on messy data, old systems and no clear first step. The right order of work, three patterns for adding AI to a Rails and Solidus stack without replacing it, what MCP changes for legacy systems, the data protection basics and five realistic places to start.
A chatbot answers your questions, an agent does your work. What really changed between 2024 and 2026, from computer use and MCP to agent payment protocols, what agents do today, and what it means for a shop on Rails and Solidus.
What an AI agent actually is, how it differs from a chatbot and an automation rule, and how to build agents for stock, support, routing and pricing on Ruby on Rails and Solidus with the guardrails that keep them safe.
How to add product recommendations to a Solidus shop: co occurrence, collaborative filtering with ALS, content based filtering with TF IDF and a hybrid of the two, with working Python and Rails code, an A/B test and the UK consent rules, using an invented Bristol tea merchant.
What Test Driven and Behaviour Driven Development are, which Rails tools do which job, what to test before every release, and how to use AI for tests without handing it the judgement.
From the naive benchmark to exponential smoothing, SARIMA, Prophet, regression and structured judgement: which forecasting method fits which product, how to give a range instead of a number, and how to check the forecast honestly, with worked examples and Python.
AI and machine learning get used as synonyms, but they are not. What each one is, how a model actually learns, what each cannot do, the Python toolkit, and why the label matters for your projects and under the EU AI Act.
What happens when rival shops run the same clever algorithm against each other? Bidding wars, the Nash equilibrium, why learning agents chase a moving target, and where optimisation turns into algorithmic collusion.
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