Avaamo is a deep-learning software company that specializes in conversational interfaces to solve specific, high impact problems in the enterprise. Avaamo is building fundamental AI technology across a broad area of neural networks, speech synthesis and deep learning to make conversational computing for the enterprise a reality.
With more than 100 large enterprise customers, Avaamo's conversational AI technology is deployed in over 40 countries and 17 languages. We've developed deep domain models for a number of industries — including health care, financial services, telecom, insurance, retail, and others — to help companies improve operational efficiencies and deliver seamless self-service experiences.
A “full stack” platform
Avaamo's proprietary NLU Engine helps process and understand complex user queries. With a specific focus on reducing false positives, the NLU engine classifies users' intents and accurately extracts key entities from users. It distills and discovers the purpose behind each user's message.
Avaamo's data science automation sifts through raw data like voice transcripts, understands top intents, and intelligently labels and categorizes your data. Our data science automation utilizes data mining and statistical analysis to determine trends and patterns in data. Data is classified into appropriate intents by using the suitable algorithms.
Avaamo's platform incorporates the techniques of advanced data science to categorize the unstructured data. It eliminates many traditional machine learning pipeline activities using a combination of intelligent preprocessing, query identification, and unsupervised classification.
Avaamo's knowledge graph can ingest company knowledge sources like documents and websites and instantly enable your virtual assistant to learn and respond to a customer's natural language queries about that knowledge.
Avaamo's platform dynamically constructs a knowledge graph based on documents and other assets. The knowledge graph is the basis for responses to natural language queries. Avaamo's platform uses the knowledge graph to store data flexibly and in a way that allows the platform to understand the meaning of information in the complete context of their relationships.
Avaamo's conversational AI platform has been optimized for Intel technologies and is built to address the traditional cold-start problem in AI by:
Ingesting unclassified data
Performing unsupervised machine learning (ML) model creation
Optimizing the model for runtime execution
Enhancing the ML model with customer-specific knowledge resources
Avaamo can scale with a 72-core configuration of a single server to address up to 100 concurrent sessions. This provides immense flexibility for large enterprises to share powerful Intel hardware across standard and AI-specific computing workloads.
(For more information, see datasheet: http://avaamo.ai/wp-content/uploads/2019/02/AI_Builders_Avaamo.pdf)
Description: Learn about Avaamo's conversational AI capabilities and how enterprises can leverage Avaamo's conversational AI platform to accelerate growth.Webinars: Human in the Loop: Teaching Your Virtual Assistant to Teach Itself
Description: Human-computer interaction drastically enhances the performance of natural language systems. In this session, we'll explore how to use human judgement to create a virtuous testing and training cycle that drives continuous learning in virtual assistants.Deconstructing Reality: Turning Customer Interactions Into Conversational Intelligence
Description: Prior to developing a truly competent virtual assistant, we must first teach it more about our world. Join us in this session to learn how to make sense of raw conversational data and create specific models within industry verticals.Building an Understanding: Teaching a Virtual Assistant to Take Meaning
Description: For a virtual assistant to conduct tasks on your behalf it must comprehend your intent. NLP/NRU technologies permits such capability but requires a model to drive this understanding This session will demonstrate how to build understanding into an virtual assistant by creation of a domain model.