The building blocks of Agentic AI, optimised for enterprise use cases.
Uses data to tailor experiences and recommendations to individuals based on preferences and behaviour. Delivers highly relevant interactions that drive user engagement and satisfaction.
Uses machine learning to suggest content or items based on historical user behaviour. Improves decisions and engagement with timely, personalised recommendations.
Identifies outliers and irregularities in data that differ from expected behaviour. Helps detect fraud, faults, or issues through automated anomaly monitoring.
Simulates human-like conversation using AI to handle inquiries and tasks. Enables fast, scalable support through natural and interactive dialogue.
Interprets user language and intent using NLP and contextual processing. Improves machine understanding of complex inputs for better responses.
Automatically converts text or speech between multiple global languages. Bridges communication gaps with real-time, accurate language translation.
Analyses and understands image or video content using visual recognition. Extracts visual data to automate classification, detection, and insights.
Forecasts future behaviours, outcomes, or trends using historical data. Supports proactive decision-making and strategic planning accuracy.
Automates repetitive digital tasks using AI-driven workflows and tools. Boosts operational efficiency while reducing manual workload and error.
Converts written content into natural, lifelike spoken voice output. Enhances digital accessibility and user experience across channels.
Extracts written or symbolic information from visual image content. Transforms images into searchable and processable digital text.
Groups users by behaviour, demographics, or needs for tailored outreach. Enables targeted communication, product offers, and engagement.
Converts spoken language into written text with voice processing AI. Powers voice commands, transcription, and real-time voice input.
AI agents assist users via voice or chat interfaces to complete tasks. Enables intuitive, hands-free support and automated interactions.
Analyses user actions and journeys to uncover patterns and trends. Drives data-led experience improvements and campaign effectiveness.
Trains AI agents to learn via feedback in dynamic environments. Adapts continuously to optimise outcomes in complex scenarios.
Detects suspicious transactions and behaviour patterns with AI. Reduces risk by identifying and preventing fraud early.
Connects structured data into semantic networks of entities and links. Improves data discovery, search accuracy, and relationship mapping.
Generates text, images, or media content using AI with minimal input. Enables scalable, on-demand content creation and personalisation.
Identifies emotional states through voice, facial expression, or text. Improves system responses by adapting to user emotional cues.
Recommends content based on the actions of similar user profiles. Enhances engagement through community-driven personalisation.
Overlays interactive digital elements onto real-world environments. Creates immersive user experiences combining physical and digital.
Uses blockchain for decentralised, transparent, and secure data flow. Improves trust, traceability, and integrity in digital transactions.
Translates spoken language into another in real time via AI. Supports multilingual communication in live or digital contexts.
Adjusts pricing in real time using supply, demand, and behaviour data. Maximises revenue by aligning price with market conditions.
Tags and categorises content to support structure and retrieval. Improves organisation, search, and downstream machine processing.
