Xfuze Composable AI Models

Groups customers based on behaviour (e.g., repurchase, product affinity), value (e.g., LTV), and demographics, informing targeted marketing, product recommendations, and personalised experiences.

Potential LTV estimation algorithm identifies high-value customers, feeding valuable data to other AIs like product recommenders and segmentation models for targeted promotions and improved experiences.

Predicts customer repurchases, guiding product recommendations, segmentation, and lifetime value estimation for optimised customer retention.

Predicts future buying behaviour based on historical purchases, product affinity, and customer segmentation, informing targeted promotions and inventory allocation for maximised sales.

Analyses historical data, weather patterns, and marketing campaigns to predict future in-store customer traffic. Drives staff scheduling, inventory allocation, and targeted promotions.

Optimises staff allocation based on predicted footfall, historical sales data, and employee skills, ensuring efficient coverage and improved customer service.

Analyses customer purchase history, browsing behaviour, and product affinity to suggest relevant products to individual customers, driving sales and personalised experiences.

Analyses customer behaviour on products that are bought together to understand product preferences, informing targeted recommendations and enhancing other AI models.

Tailors grouped and combo product offerings based on customer insights (e.g., recommendations, segmentation, repurchasing) to maximise sales and customer satisfaction.

Optimises stock distribution based on demand forecasts (e.g., LTV estimation, repurchase propensity) and product popularity to ensure availability and minimise stockouts.

Predicts future product needs by analysing historical sales data, customer behaviour and external factors, informing inventory allocation, safety stock, and other planning models.

Predicts future revenue based on historical sales data, customer behaviour, product information, and market trends, informing targets, resource allocation, and marketing strategies.

Sets buffer inventory levels using demand forecasts and lead times to prevent stockouts and maintain smooth operations.

Monitors stock levels using historic purchasing patterns and potentially cameras or sensors, feeding into inventory allocation and demand forecasting to prevent stockouts and improve CX.

Analyses customer demand, product performance, and pricing data to optimise stock levels and pricing strategies, maximising sales and minimising excess inventory.

Identifies ideal discounts for slow-moving inventory (informed by demand forecasting, product affinity) to maximise profit margins and clear stock effectively.

Analyses product features, customer behaviour, and price sensitivity to optimise product offerings and pricing strategies, leveraging insights from other AIs (recommendations, segmentation).

Predicts future return volumes based on historical data, product information, and customer behaviour to optimise inventory management, product quality, and return policies.

Estimates the likelihood for a sales order line to be returned based on historical product and customer behaviour data, to optimise inventory, quality, and return policies.

Analyses customer data, transaction patterns, and external sources to identify suspicious activity in real-time, protecting retailers from financial loss (fake accounts, stolen cards, mismanagement).

Scans data across all single data models (inventory, demand, customer) to identify unusual patterns, flagging potential issues and informing corrective actions.

An entity resolution engine cleans up messy data by identifying and linking records that refer to the same real-world entity, improving data quality, analysis and overall system performance.

Interactively creates data dashboards as code by understanding user needs and data structures, automating dashboard development. Used internally by Xiatech Insights Analysts.

Automates data schema mapping from source systems to the internal data model, accelerating Smart Connector design and implementation for seamless integration.

Statistically predicts the value of missing data points within a dataset based on patterns and relationships in the existing data, improving quality and analytical depth.

Automates content creation by analysing competitor content, customer data, and brand guidelines to generate targeted marketing materials (ad copy, social posts, blog articles).

Analyses website structure, target audience, and brand voice to craft engaging website content (product descriptions, landing pages, blog posts), improving UX and SEO.

Leverages NLP and machine learning to answer customer questions, resolve common issues, and automate support tasks — 24/7 assistance that reduces pressure on live agents.

Engages website visitors and social followers through conversational marketing, promoting products, offering personalised recommendations and guiding customers to checkout.

Automatically classifies items into categories or suggests attributes (e.g. "vegan", "casual", "winter wear") to enrich catalogue and improve discovery.

Summarises, classifies, tags or translates vendor contracts, shipping docs and invoices to speed up back-office processes and reduce manual effort.

Provides quick overviews of customer sentiment and key points from hundreds of reviews — surfacing insight back to merchandising and product teams.

Quantifies the impact of external factors (pricing, promotions, seasonality) on sales and customer behaviour for more accurate AI-driven forecasting and strategy optimisation.

Helps clients and other models evaluate the effectiveness of each customer touchpoint across channels, enabling more precise marketing spend allocation and personalisation.

Tailors all aspects of customer behaviour (recommendations, assortments, campaigns) by simulating customer segments and regional demand patterns.

General-purpose forecasting block that handles seasonality, trend and exogenous regressors across any numeric series — sales, footfall, energy, headcount.

Reusable clustering primitive for grouping entities (customers, stores, SKUs, suppliers) by behavioural and attribute similarity.

Vector-based similarity service powering recommendations, lookalikes, duplicate detection and semantic search across any embedded dataset.

Constraint-based optimiser used by allocation, routing, pricing and scheduling models to maximise an objective under business rules.

Estimates incremental impact of an action (offer, intervention, treatment) on an outcome — separating correlation from causation.

Generates and indexes embeddings for text, images and tabular records — the substrate behind RAG, search and similarity workloads.

Combines vector retrieval with LLM generation to answer questions with cited, governed enterprise knowledge.

Extracts structured fields and tables from PDFs, scans and emails — invoices, contracts, POs, delivery notes — with confidence scoring.

Transcribes calls and voice notes, then classifies intent, sentiment and topics to feed quality, compliance and CX models.

On-brand translation of product copy, campaigns and policies across markets with glossary and tone-of-voice controls.

Profiles datasets and applies rule, statistical and ML-based checks to detect schema drift, outliers and integrity breaks before they hit production.

Generates statistically faithful synthetic datasets for testing, model training and safe data sharing without exposing PII.

Identifies and masks personal and sensitive data in documents, logs and conversations to protect privacy across AI workflows.

Constructs and maintains entity-relationship graphs from structured and unstructured sources — powering reasoning, search and explainability.

Detects, classifies and tags objects in images and video — for catalogue enrichment, shelf monitoring, loss prevention and quality control.