As enterprises look to exploit a broader set of automation tools to mitigate functional and process silos, there is a clear focus on achieving end-to-end process automation. We have developed our hyperautomation services portfolio based on the best practices, proprietary framework, and implementation methodologies derived from scaled engagements delivered to some of the largest companies spread across the globe.

Hyperautomation, as an automation approach leads to a solution architecture comprising a range of tools and capabilities, including but not limited to Robotic Process Automation (RPA), Intelligent Document Processing (IDP), Intelligent Business Process Management Suites (iBPMS), Integration Platform-As-A-Service (iPaaS), process mining, and Artificial Intelligence (AI). Low-code and configuration-driven, intuitive development and citizen user-friendly user experience (UX) are other key attributes of the architectural components of hyperautomation.


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  • Identify the right team and people
  • Identify the “right candidate” processes
  • Process assessment and building the strategy roadmap
  • Architecture consultation
  • Identification of right tools for automation
  • Framework and best practices
  • Compliance and governance models


Implementation & CoE

  • Process re-engineering
  • Proof-of-concept (PoC) development
  • License management
  • Deployment model setting up of hyperautomation tools
  • Implementation of initial phase
  • Project and resource management
  • End-user training
  • CoE setup and parallel development


Support & Maintenance

  • Implementation support (maintenance of software bots)
  • Change management
  • Continuous feedback and improvement
  • Product Support
  • Version management and upgrades


Trusted provider with decades of experience

We have requisite expertise in industry- and function-specific processes, have developed our own intellectual property (IP) in terms of RPA, IDP, and AI/ML products, along with best practices and proprietary framework & implementation methodologies derived from large-scale engagements.

Customer success- and value-driven

We have automated complex business processes at scale, ranging from dozens to hundreds of bots deployed in production per customer. Have developed holistic solution architectures combining RPA, IDP, iBPMS and AI/ML products for complex processes and use cases.

One-stop solution

Get rid of the friction involved in integrating disparate technologies and phases in your automation initiatives and gain faster time-to-value at a lower cost of ownership with a single partner.


Achieving success with process automation initiatives calls for synergies between People, Process, and Technology facets. RPA skills shortage, poor change management, lack of business-IT alignment, ill-defined success criteria, and disregard for infrastructure management considerations are frequently-cited factors leading to failure of automation initiatives. Our proprietary framework for hyperautomation implementation, best practices, and implementation methodologies ensure scale and resiliency irrespective of the underlying tools and deployment model.


Robotic Process Automation

  • NAlliancetech TruBot
  • UiPath
  • Automation Anywhere

Intelligent Document Processing

  • NAlliancetech TruCap+

Intelligent Business Process Management Suites

  • NAlliancetech iPM
  • Pegasystems
  • IBM
  • Ultimus
  • K2

Process Mining and Discovery

  • Celonis
  • UiPath

Artificial Intelligence

  • NAlliancetech TruAI
  • Google AI
  • Microsoft Azure AI


Read about the automation use cases from different industries and how Robotic Process Automation is helping to achieve operational excellence and increase customer satisfaction.

Frequently Asked Questions

Artificial Intelligence solutions enable the automation of medium to complex processes and take Robotic Process Automation to the next level. It powers predictive maintenance of heavy machines thus reducing operational costs. AI enables pattern identification with vast data volumes thus augmenting human intelligence and decision making. It improves the analytics domain to the nth extent. While Natural Language Programing (NLP) algorithms read and augmedata nt to produce analytical reports, Natural Language Generation (NLG) algorithms write sentences.

AI joins the dots from disparate data sources and discovers interesting patterns that are less visible to the human eye and intellect. Consistent variations, clustering of data points, dependencies, etc., bring forth interesting stories from the stagnant data resources. Using data, AI-enabled information mining generates hypotheses, verifies them, and deduces information. Information Mining forms the basis of Predictive Analytics and Machine Learning.

AI-powered Data Mining enables users to perform different levels of tasks –

  • Anomaly detection, where unusual patterns or outliers are thrown up, which may be errors or require deeper investigation
  • Associations, where the relationship between two or more variables are brought forth, and is frequently used in forensic investigations
  • Clustering, where data points that are similar in more than one ways are discovered
  • Classification, where known structures are generalized in order to apply to new data points; for example: Records or File Classification
  • Regression, where a function is found, which models the data and estimates the relationship between different data points
  • Summarization, where a concise summary is generated based on weighted keywords. It is popularly used for generating audit reports and executive summaries.

AI simplifies Document Management to a great extent. It enables automatic file classification or categorization as well as summarization. The files may include spreadsheets, documents, emails, PDFs, video files, audio files, social media, news, and other data types.

AI algorithms generate intelligence from high data volumes by correlating disparate data points. This data may comprise structured, unstructured, and multi-structured data.

A recent feature is AI-enabled Topic Modelling. It allows screening of huge data sets, such as reviews, emails, social media snippets, etc., and segregating them as per the predominant sentiment.

AI algorithms use weighted keywords and correlate them to generate concise summaries of lengthy documents, news articles, research papers, agreements, books, tweets, etc.

It uses two methods – Extractive and Abstractive. Extractive Summarization extracts several portions of the text and stacks them to create a summary. Abstractive Summarization uses NLP algorithms and generates a new summary.

AI-powered document summarization is used in audits, research study scenarios, social media listening, government services, etc.



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Amit Mahajan
N Alliance Tech Commercial Director

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