Retail & E-Commerce
What Technspire builds for retail and e-commerce teams: demand forecasting and inventory optimisation on Azure Machine Learning, personalisation engines, customer-service automation, and the data platform underneath them. This page describes our offerings; talk to us about applying them in your organisation.
reference architecture, not a client story
Retail margins are thin and volume is volatile. Inventory carrying costs, stockout-driven lost sales, and personalisation that does not actually personalise are the visible symptoms of a deeper problem: the data is there but the models that act on it are not. Modern AI on Azure turns existing transactional, behavioural, and external signal data into the forecasting and recommendation surfaces that move the business metrics.
Technspire builds production-shaped AI for retail: forecasting that respects local seasonality and promotional events, personalisation that handles the cold-start problem honestly, and customer-service automation that escalates to humans when it should.
Time-series forecasting pipelines that respect store-level seasonality, promotional calendars, weather effects, and external events. Inventory optimisation built on the forecasts, with safety-stock policies tuned per product class and replenishment recommendations integrated into the ordering system.
Stack: Azure Machine Learning, Microsoft Fabric where applicable, Azure Synapse for data preparation, Power BI for operational dashboards.
Recommendation systems for product discovery, search ranking, and cross-sell. Honest cold-start handling for new customers and new products, GDPR-respecting data flows, and evaluation rigour that catches recommendation drift before it costs revenue.
Stack: Azure Personalizer, Azure AI Search for catalogue retrieval, Azure Cosmos DB for user state, Application Insights for in-flight evaluation.
AI-driven catalogue enrichment: image tagging, automated attribute extraction from product descriptions, multi-language translation for cross-market expansion, and de-duplication at scale.
Stack: Azure AI Vision, Azure OpenAI for description enrichment, Azure Translator, Azure AI Search for catalogue indexing.
Chatbots and email automation that handle order status, returns, and product questions. Grounded in your knowledge base via RAG, escalating to human agents when the query is genuinely complex or emotionally weighted.
Stack: Azure OpenAI, Azure AI Search, Microsoft Copilot Studio, omnichannel integration (web, mobile, email, voice).
Retail AI workloads cross GDPR, PSD2 (for payment-adjacent flows), DSA for marketplace operations, and the EU AI Act limited-risk transparency requirements for personalisation and chatbots. We architect the data residency, consent management, and customer-rights surfaces that keep these obligations satisfied without slowing the team down.
Discovery (2–4 weeks). Data audit, model-feasibility check, integration mapping, EU AI Act risk classification, and scoped proposal with named engineers.
First vertical slice (4–8 weeks). One end-to-end model in production or staging, a single category forecast, a single recommendation surface, a single chatbot intent. Real telemetry, real evaluation set.
Sustained delivery. Iterative expansion across the catalogue, channels, and markets. Documentation, evaluation suites, and on-call runbooks land alongside the code.
Short conversations are free. Bring an architecture question, a forecasting accuracy target, or a personalisation problem you have not been able to solve.