Service Offerings

Iris Automation Palette is an assorted package of services and business accelerators that enable organizations to achieve greater efficiencies in shortened timelines. We fuse a range of technologies such as Artificial Intelligence (AI), Machine Learning (ML) and Data Science to assist enterprises in attaining their business goals faster at lower costs.

Intelligent Automation

Intelligent Automation

Iris Intelligent Automation theme encompasses Accelerated and Conversational Automation solutions. Our Accelerated Automation expertise spans Robotic Process Automation (RPA) and Point Automation for rule-based processes and specific user-level tasks. Conversational Automation involves automating a conversation in the form of virtual assistants that provide information and aid in performing actions and offering recommendations with the help of natural language understanding. We supplement and enhance the Accelerated and Conversational Automation themes with cognitive extensions such as speech-to-text conversion, contextual speech and extraction from images, documents and textual content.

Quality Engineering

Quality Engineering

Iris has formulated various quality engineering approaches to enable competitive advantage and faster realization of benefits. Our Quality Engineering services for cloud involve application deployment on and for the cloud infrastructure, adopting a distributed mode of execution using cloud agents and environment containerization for test execution. We accelerate Test Automation to shorten app release cycles and eliminate costly testing bottlenecks. We follow a focused, modular approach for microservices that build a complete application and API Test Automation. Contract testing and simulators are deployed for mocking and integrating APIs using mature REST systems.

Low Code No Code

Low Code No Code

Low Code No Code (LCNC) platforms help enterprises develop business applications faster, reducing time to market from months to just a few weeks. Iris expertise spans building Low Code back-ends, No Code front-ends and advanced code accelerators. Requirements finalization tends to be a significant bottleneck for such initiatives. With LCNC, we help clients transition from waterfall to agile models smoothly. Our LCNC services also help customers reduce reliance on highly-skilled and expensive technical resources. The additional benefit to organizations includes feature-rich applications that are highly interactive and well-integrated.

Accelerators

iASQ – Intelligent Automation Accelerator

iASQ – Intelligent Automation Accelerator

Iris Automated Service Requestor, iASQ, is an accelerator built on Intelligent Automation. iASQ is a framework that provides a unified omnichannel interface to address service requests. An ML-based accelerator, iASQ stitches multiple channels like chatbots, email systems and collaboration platforms, and intelligently integrates with ITSM platforms like ServiceNow, Jira Service Desk etc. The iASQ engine is capable of handling automated triaging, adaptive capacity planning, automated resolutions, user self-service recommendations, and is evolving towards predicting and preventing future IT issues.

Template Comparator – Test Automation Accelerator

Template Comparator – Test Automation Accelerator

Iris Template Comparator is a comparison engine that compares two templates by automatically locating and highlighting different information regions in spreadsheets, word documents, comma-separated values (CSVs), or portable document files (PDFs). This template comparison utility compares the structural and textual similarity between the two templates and is capable of extracting business information and storing it in a database. The automated comparison process includes image pre-processing, data capturing & categorization, data extraction using Natural Language Processing (NLP) and Machine Learning, and data validation using pre-defined taxonomies, data dictionaries and business validation rules. The accelerator can also be used to auto-generate business rules by learning from historical data, evaluating the domain of fields and providing auto-suggestions for invalid data and missing fields.

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