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Infuse intelligence into every aspect of your contact center, from robotic process automation to advanced analytics.
Reduce average handle times (AHT), improve call deflection rates and raise CSAT scores through faster and more efficient contact center operations.
Increase agent performance through AI-driven agent assist strategies that provide next best actions and ongoing support during customer interactions.
Identify call drivers and leverage AI for sentiment analysis that enhances customer views and empowers agents to achieve faster resolutions.
OdigoTM solutions integrate with Dialogflow through an AI connector for stronger NLP capabilities, making it easier to integrate a conventional user interface into your mobile and web apps, IVRs, bots and other devices.
Via an AI connector, Odigo solutions seamlessly integrate with IBM Watson for third-party bot support that delivers effective self-service options and automatic call distribution. Working with a cutting-edge data analytics processor increases our abilities to understand language and analyze vast amounts of data.
I am confident this partnership will benefit our customers with an improved experience as Odigo has been very reactive to integrate Google Cloud CCAI capabilities to its offer and is one of the first to demonstrate their AI capability, being a visionary in the CCaaS market.
How can a healthcare provider deliver consistent, end-to-end customer experience across channels through a single platform?
Odigo, through a high-value AI connector, enables its clients to maximize their investments by integrating their AI into different channels and use cases.
Orchestrate your interactions with customers on any voice or digital channel.
Discover how 30+ years of experience in managing vocal journeys through interactive voice recognition (IVR), augmented with natural language processing (NLP), can streamline your automation-based qualification process.
Our 24+ years of natural language understanding (NLU) experience drive AI innovations that facilitate natural conversations with your customers.
AI with contextual understanding provides agents with recommendations that improve resolution rates.
Design and run intelligent outbound campaigns that encourage engagement and strengthen customer relationships.
AI connectors refer to the various components or algorithms that facilitate the integration of artificial intelligence (AI) capabilities into different systems or applications. These connectors act as bridges between AI models and the data or platforms they interact with, enabling seamless communication and functionality. Examples include APIs (Application Programming Interfaces), SDKs (Software Development Kits), libraries, and middleware.
There are many types of AI and reasons to use an AI connector. One way to categorize AI is based on the level of intelligence and functionality exhibited. This leads to four main types of AI:
Reactive machines are the simplest form of AI and operate based on predefined rules without memory or learning capabilities, making decisions solely based on current inputs or stimuli. In a contact center this could include providing responses to FAQs.
Limited memory machines can store and recall past experiences for short periods to inform decision-making but lack long-term memory retention. For example, a recommendation system that uses past interactions to personalize recommendations.
Theory of mind AI surpasses basic pattern recognition by comprehending and interpreting human mental states, emotions, and intentions. This enables the understanding and prediction of beliefs, desires, and intentions. In the contact center this would include augmented supervision functions and real-time suggestions that work alongside agents during interactions analyzing customer speech and sentiment.
Self-awareness is AI capable of introspection and consciousness and is the stuff of science fiction and Hollywood movies, at least for now.
AI is utilized in many industries and for a multitude of purposes, this means it is common to use AI connectors to integrate capabilities into larger tech stacks. Ten common applications of AI today include:
Virtual assistants and chatbots for customer service and support.
Personalized product recommendations on e-commerce websites.
Predictive analytics for personalized financial advice and budgeting apps.
Voice assistants for language translation and real-time interpretation.
Computer vision for facial recognition in smartphone apps and social media.
Healthcare apps for diagnosing common ailments and providing medical advice.
Fraud detection algorithms in banking apps and online transactions.
Smart home devices for voice-controlled lighting, thermostats, and security systems.
Delivery tracking and optimization algorithms in logistics apps.
Voice-controlled virtual assistants in cars for navigation and hands-free calling.
While the ‘four types of AI’ are categories based on the intelligence of the system, the five types of AI represent a different way to classify AI based on intended function or application. Contact centers will increasingly look to leverage all five types of AI, often using an AI connector:
Interactive AI: Systems capable of engaging in real-time interaction and conversation with users, such as virtual assistants and chatbots.
Functional AI: AI systems designed to perform specific tasks or functions autonomously, such as robotic process automation (RPA) and industrial automation.
Analytic AI: AI systems focused on analyzing and interpreting large volumes of data to extract insights and make data-driven decisions, such as predictive analytics and machine learning algorithms.
Text AI: AI systems specialized in processing and understanding natural language text, enabling tasks like sentiment analysis, language translation, and text summarization.
Visual AI: AI systems dedicated to analyzing and interpreting visual data, such as image recognition, object detection, and video analysis.
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