Abzooba has a systematic approach towards enterprise AI solutions using framework xpresso.ai. This enables Abzooba to deliver AI projects using a reproducible methodology in a timely and robust manner. The enterprise AI journey starts with data intake to data preparation and cognitive modelling that leads to actionable insights.
xpresso.ai has accelerators for each phase of the journey that are built for specific cognitive goals and are enterprise-tested with our clients. Depending on the cognitive maturity of your enterprise, Abzooba leverages the appropriate xpresso.ai accelerators to manage your AI development and production deployment.
Platform Overview Video
Our data Connectivity seamlessly connects any data source available in structured, un-structured and streaming formats. Enables easy creation of custom connectors for any APIs, databases or file-based formats which leverages existing open source plugins and connectors.
Data engineering and management architecture capable of consuming various data sources in a fast and inexpensive manner. Multiple internal and external data feeds within enterprises from various sources can be processed in parallel and merge a wide variety of data coming in at high velocity and high volume.
All the data sources are funnelled into the data storage layer after systematized validation and cleansing. The storage landscape with different storage types and extreme flexibility is built-in to manipulate, filter, select, and co-relate different data formats. Various data adapters are available through a common catalogue of services which simplifies interoperability and scalability concerns, enable APIs and abstract all the technical complexities from the service consumer.
Leverage the latest Machine Learning/Deep Learning/Big Data libraries to build a flexible cognitive framework with ability to integrate external packages through API and flexibility to handle automated feature engineering, model parameter optimization and get actionable visual insights.
Offers a range of modularized solution components like Natural Language understanding, Semantic Knowledge engine, recommendation engine, information retrieval engine, summarization engine etc in a microservice architecture framework
Ability to process large volume of data in parallel with high flexibility and scalability framework. Dockerized components make these easy to deploy in production environment (on-premise or in the cloud)