Back To Top accelerators for a AI development and deployment

Abzooba AI Methodology


Business understanding

Identify use cases

Feasibilty study

Data Management

Data understanding

Data sources

Data preparation

Cognitive AI

Learning algorithms

Model building / training

Model Experimentation


Model evaluation

Model accuracy

Model Serving


Container Driven / Cluster

Offilne / Online

Feasibilty CI / CD


Pre-Tranied Model

Realted Models

Parallel Pipelines

Library of Models


End User Application

Micro Services

Real Time



Model Prediction

Model Visualization

Logging / monitoring

Abzooba has a systematic approach towards enterprise AI solutions using framework 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. 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 accelerators to manage your AI development and production deployment.


xpresso Data Connectivity

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.

xpresso Data Engineering

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.

xpresso Data Storage

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.

xpresso Cognitive Framework

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.

xpresso Solution Components

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 framewor.

xpresso Infrastructure

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)


Enables a streamlined and structured methodology with cognitive framework to embark on a successful AI transformation.

Quick integration of reusable components to accelerate robust AI solution deployment.

Availability of each and every components of AI in the framework.

Easily configurable and deployable in any cloud (AWS and Azure) or in-premise infrastructure.

Feedback loop for continuous improvement.

Flexibility to plug-in open source AI packages and libraries.

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