Digital Transformation New

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Digital Transformation Solutions

Enhance your operational efficiencies, processes, and compliance

Why Digital Transformation ?


of companies reporting a positive business impact of digital transformation also reported higher net revenue growth.

Source: Deloitte


increase in customer satisfaction can be achieved through digital transformation with a focus on customers.

Source: Mckinsey


of companies expect that digital transformation will disrupt their industry, but only 44% are prepared for digital disruption.

Source: Deloitte

Robotic Process Automation (RPA)

Robotic Process Automation is a process where software BOTS mimic human actions and perform certain repetitive rule-based tasks at a much greater speed and efficiency when compared to that of the human workforce.

RPA implementation in an organization increases productivity and profitability. The digital workforce allows the human workforce to focus on more revenue-generating and high-end tasks that create more value for the business and the end customer.

At Valenta, our team of RPA Developers and Business Analysts work closely with you to identify your pain points, understand your business landscape, and recommend suitable RPA solutions to meet your business goals and objectives.

Benefits of Robotic Process Automation

There are several benefits of implementing RPA as follows:

Speed – It is practically impossible for human staff to operate at the speed of BOTS.
Human Error – No matter how qualified and equipped your human workforce is, they will always be prone to distraction in some form which will lead to errors in jobs being processed and this can completely be eliminated by deploying BOTS saving businesses millions of dollars annually.

Scale – For most businesses that want to scale, RPA is the greatest invention ever as it allows firms to focus on growth and utilize their human workforce to think and act differently rather than spend time on training and developing human resources to perform mundane administrative work.

High Performance – In order to achieve robotic efficiency and performance, BOTS needs to be deployed across the organization as they can process the most complex tasks in the quickest manner and ensure accuracy which enables the human workforce to review and deliver high performance through quality outputs.
Costs – By implementing RPA, firms can drastically reduce their spending on various business functions such as F&A, HR, administration, IT, contact centers, and much more. Due to the speed and delivery with RPA, organizations are reshaping their HR and business strategies and increasing their spending on RPA technology. You no longer need 10 resources in the same process. You could do with 3 while the rest are deployed in other core business functions.

Intelligent Process Automation (IPA)

When RPA is combined with Artificial Intelligence or AI, it transforms automation altogether. When software BOTS can take decisions using AI capabilities, it only changes a particular process or organization but literally an entire Industry.

RPA is purely rule-based but by plugging in AI, the BOTS become Super BOTS and new areas can be explored that were previously ignored due to several factors. High manual costs, time, and effort, and much more.

There are several components that form AI – machine learning, computer vision, and NLP (Natural Language Processing), which when integrated with RPA become Intelligent Automation. This also benefits organizations in a massive way as it reduces costs, provides an unmatched customer experience, improves productivity across the organization.

Businesses can spend more time analyzing customer data and can focus on going the extra mile. The human workforce becomes very efficient and can work on more revenue-generating activities. At Valenta, our team of RPA Developers, Machine Learning Engineers, and Data Scientists are constantly working on several areas and take Intelligent Automation to a whole different level.

Components that Form Intelligent Process Automation (IPA) :

Use of Machine Learning

Machine Learning is a system that can learn from data through self-improvement and without logic being explicitly coded by the programmer so the BOTS are auto-learning over a period of time and are able to process more data accurately.

Natural Language Processing (NLP)

Natural Language Processing or NLP is a branch of AI that helps computers to understand, interpret, and manipulate human language. NLP helps developers to organize and structure knowledge to perform tasks like translation, summarization, named entity recognition, relationship extraction, speech recognition, topic segmentation, and more. Chatbots with NLP is being used widely as they interpret, recognize, and understand user requests in the form of free language.

Computer vision

Computer vision is a field of computer science that works on enabling computers to see, identify and process images in the same way that human vision does, and then provides appropriate output.

Document understanding

Document understanding aims to unleash data trapped in documents to grant your organization much higher accuracy of the extracted data, increased productivity, and growing ROI from Robotic Process Automation (RPA). The document understanding ecosystem includes technologies that can interpret information and meaning from a wide range of document types – even handwriting, checkboxes, and stamps.

Speech Recognition

Speech Recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as Automatic Speech Recognition (ASR), Computer Speech Recognition, or Speech to Text (STT).

Voice Recognition

Voice Recognition is a Biometric technology used to identify a particular individual’s voice or for speaker identification.

Valenta and Conversational AI

Valenta has partnered with Yellow Messenger, the world’s fastest-growing Conversational AI platform, with over 500 global enterprise customers and 1B+ platform conversations quarterly. Yellow Messenger also supports over 100+ languages natively on their proprietary platform. Yellow Messenger has also been named the leading conversational AI platform by Gartner in October 2020.

Conversational AI is going to revolutionize every industry including, but not limited to health care, retail, BFSI, and FMCG. There are several use-cases across various sectors and business functions such as HR, IT, operations, sales and marketing, customer support, and much more.

Conversational AI can power both customer and employee experience. For customers, waiting in queue to speak to a live agent is now a thing of the past. Chatbots have been around for some time, however, have only been able to perform certain basic tasks which were more FAQ based whereas, with conversational AI, the new age chatbots use machine learning and natural learning processing and are able to offer customers and internal employees a whole different experience as they provide human-like conversational experiences.

Conversational AI Use Cases

While most AI chatbots and apps currently have rudimentary problem-solving skills, they can reduce time and improve cost efficiency on repetitive customer support interactions, freeing up personnel resources to focus on more involved customer interactions. Overall, conversational AI apps have been able to replicate human conversational experiences well, leading to higher rates of customer satisfaction.
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  • Order management
  • Up-Sell and cross-sell
  • Channels sales automation
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  • Smart lead generation
  • Conversational banner
  • AI powered landing pages
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Human Resources

  • New hire on boarding
  • Leave management
  • Meeting scheduler
  • Reimbursement processing
  • Payroll management
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  • Incident management
  • Smart look-ups
  • Critical alerts and monitoring
  • FAQ and knowledge management
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Customer Support

  • 24/7 chat and call support
  • Multilingual support
  • Omni-channel support
  • Live agent transfer


  • Auto trigger RPA workflows
  • Document cognition
  • Auto-escalation
  • TAT and error rates

Components of Conversational AI

Conversational AI combines natural language processing (NLP) with machine learning. These NLP processes flow into a constant feedback loop with machine learning processes to continuously improve the AI algorithms. Conversational AI has principle components that allow it to process, understand, and generate a response in a natural way.

Machine Learning (ML) is a sub-field of artificial intelligence, made up of a set of algorithms, features, and data sets that continuously improve themselves with experience. As the input grows, the AI platform machine gets better at recognizing patterns and uses it to make predictions.

Natural Language Processing is the current method of analyzing language with the help of machine learning used in conversational AI. Before machine learning, the evolution of language processing methodologies went from linguistics to computational linguistics to statistical natural language processing. In the future, deep learning will advance the natural language processing capabilities of conversational AI even further.

Machine Learning

NLP consists of four steps:

  • Input Generation
  • Input Analysis
  • Output Generation
  • Reinforcement Learning

Unstructured data transformed into a format that can be read by a computer, which is then analyzed to generate an appropriate response. Underlying ML algorithms improve response quality over time as it learns.

These four NLP steps can be broken down further below:

Input Generation

Users provide input through a website or an app; the format of the input can either be voice or text.

Input Analysis

If the input is text-based, the conversational AI solution app will use natural language understanding (NLU) to decipher the meaning of the input and derive its intention. However, if the input is speech-based, it’ll leverage a combination of automatic speech recognition (ASR) and NLU to analyze the data

Dialogue Management

During this stage, Natural Language Generation (NLG), a component of NLP, formulates a response.

Reinforcement Learning

Finally, machine learning algorithms refine responses over time to ensure accuracy.

Benefits of Conversational AI

Conversational AI is a cost-efficient solution for many business processes. The following are examples of the benefits of using conversational AI.

Cost Efficiency: Chatbots can reduce significant costs within any contact center. Not only can the chatbots work 24/7, but they also always operate at the same efficiency. Chatbots can reduce the AHT (Average Call Handling Time) significantly and reduce the burden on the human agent who is now able to focus on the more serious and priority-based queries.

There are several other cost benefits ranging from salaries, overheads such as office space, utilities, staff Welfare, pensions, superannuation, insurances, and so on.

Call centers, in general, have always had staffing issues due to the high attrition rate globally and this has led to high recruitment and training costs therefore intelligent chatbots are now seen as the new age contact center that work alongside human agents to improve C-SAT (Customer Satisfaction) and E-SAT (Employee Satisfaction)

Instant Responses and Higher Engagement: Almost every consumer uses mobile devices in their daily lives for various tasks and activities, right from buying groceries to paying utility bills, looking for new offers and discounts from various consumer brands, using ride-hailing apps, and much more. Businesses need to provide real-time information; otherwise, they will lose out on opportunities.

Intelligent chatbots or AI chatbots can provide customers with real-time information without the client having to wait in a long queue to speak to a human agent so they are able to avoid those wait times and get instant responses, which lead to increased customer satisfaction and loyalty.

AI-Powered chatbots are also able to offer personalization with the ability to provide recommendations to end-users, allowing businesses to cross-sell and up-sell products that customers may not have initially considered. A simple example could be booking a flight ticket through an online app, which then triggers further questions such as “Would you like to book a hotel or a cab?” and so on.

Ability to Scale: AI-Powered chatbots are easy to scale due to the reduced infrastructure support that is required and are also a lot cheaper when compared to hiring and training human resources. For brands that are planning to expand into different geographies and regions, there is always a challenge with the language barrier which can now be eliminated through Intelligent chatbots.

Bi-lingual and multi-lingual contact centers were a USP whereas with conversational AI today, these contact centers can operate with great efficiency and productivity leading to profitability.

Challenges with Conversational AI

This is new technology and still, at a nascent stage and as with any new technology, there are bound to be certain challenges such as:

Limited Use-Cases

While businesses have successfully deployed Intelligent chatbots in certain horizontal business functions such as HR, IT, and sales to perform certain tasks, there is still a lot that needs to be done and explored in this space. Conversational AI is growing at a rapid pace and is truly revolutionizing the way organizations conduct business.

Privacy and Security

A lot of personal and financial data is collected and processed through Conversational AI and this increases the risk of security breaches therefore organizations need to ensure that they have the highest privacy and compliance standards to protect user data.

User Experience

While customers globally are now used to conversing with chatbots, they are still very hesitant when it comes to sharing personal or financial data. A lot of customers still do not provide accurate credentials due to the fear of data being misused.

While customers are happy to interact with Intelligent chatbots and obtain information about a product or a service, they still do not prefer completing the transaction online and while the trend is changing, it is going to take time for user behavior to change and accept Intelligent chatbots as the new contact center.

It all depends on how the chatbots are developed and the kind of complexity levels they can handle. A lot of organizations have invested in this technology but have not invested in the time and resources to make the technology work. Customers usually receive a very generic response and this is mainly for information capture which can then be passed onto a human resource who can call and engage with the client whereas some organizations have built Intelligent BOTS that are not only able to respond to queries but can go 2-3 steps further and provide them with additional information-based, and this takes customer experience to a whole new level.

Advantages of Partnering with Valenta and Yellow Messenger

Enterprise-Ready Solutions :

  • AI Knowledge Management
  • Seamless Human – AI Transfers
  • RPA Integration
  • OCR Image Recognition
  • Document Cognition
  • Conversational notifications
  • Conversational Analytics
  • Bot Lifecycle Management
  • Universally multi-lingual
  • Predictive Support

Feature rich conversational AI platform :

  • Custom UI
  • Proprietary NLP Engine
  • Context Awareness
  • Intent Handling
  • Pre-built Integrations
  • Dynamic Response Generation
  • Exception Handling
  • Analytics
  • Human in the Loop

RPA Functions

Finance & Accounting

Human Resources

Information Technology

Contact Centre

Process Operations

Procurement & Administration

Sales & Marketing

Business Development

Valenta Engagement and Deployment Models

Service Based RPA

Not every business has the volumes to justify the costs associated with setting up an internal CoE (Centre of Excellence) and therefore by using Valenta’s RPA Managed Service, clients only pay a subscription fee each month This allows businesses to automate with ease and use RPA as a service and pay only for what is used.

Organizations that want to build an Internal CoE (Centre of Excellence) need to be clear on their business objectives and what they want to achieve. Setting up an internal CoE is not very economical if there isn’t enough volume to process and therefore it makes practical sense to start off by using RPA as a service which keeps costs minimal and in time, allows the business and stakeholders to soak in the technology and ensure that it is working prior to taking a giant leap and building an Internal CoE.

RPA Advisory

Businesses today are wanting to automate several processes but lack clarity in terms of where to start and this is where Valenta’s RPA Advisory service comes in. Valenta’s team of Process Automation Specialists identify gaps in an organization and bridge those gaps by deploying suitable RPA solutions.

It all starts with process consulting gradually leading to implementation either through an internal CoE (Centre of Excellence) or by using RPA as a service through an external company. Valenta’s process automation team also assists with the strategy development and identifies areas that are ripe for automation in a business to increase the success of the POC (Proof of Concept).

Firms need to make a conscious decision when deciding to go down the automation path as several RPA initiatives fail due to lack of clarity and expertise and this is where a team of external experts can analyze the various systems and processes and decide the best approach to implement.

RPA Staff Augmentation

Several organizations globally have built their own CoE (Centre of Excellence) however always struggle with retaining talent and optimizing their resources as this is not their core business and are always heavily dependent on their internal CoE and any change to this leads to severe consequences and puts a massive strain across the organization.

To overcome these hurdles, Valenta’s staff augmentation solutions work great for businesses as it allows them to scale up and downsize as per their business requirements and it also gives them access to an external team of experts who integrate with their Internal CoE and together are a much more formidable force.

To Build a CoE, there are several roles that an organization needs to recruit such as an RPA Sponsor, RPA Champions, Change Manager, Business Analyst, Solutions Architect, RPA Developers, Infrastructure Engineers, Controller & Supervisor, Service & Support and without the right team and skillset, most projects struggle to take off whereas by using a team of professionals through Valenta, this risk can be mitigated and Valenta’s B-o-T (Build-Operate-Transfer) model could also work well.

Why Valenta?

  • Team of global professionals
  • Creative commercials
  • Domain know-how across multiple sectors
  • Onshore, nearshore and offshore offerings
  • Customized solutions
  • A partner in your growth
  • Highest standards in security and compliance
  • Leader in innovation
  • Gold standard in digital transformation

Valenta’s unique RPA Journey:

Discovery / Fact Finding Day

Valenta’s team of process automation specialists will work closely with you to understand your business objectives and also analyze the current process in place and based on various observations, processes and functions are identified for RPA.


A Proof of Concept is the best place to start and the RPA feasibility can be conducted. While several processes seem easy to automate, it is only once you start to realize the complexities involved and therefore POC’s are usually offered at no cost and no obligation. This process can take anywhere between 2 weeks to 2 months depending on the process.


Once the POC is successfully completed, the next stage is to Go-Live with the process.

Quarterly / Annual Reviews

It is extremely important to review the RPA processes periodically and ensure everything is working as intended. During the periodic reviews, new areas and process improvements are identified, and systems and processes are put in place to bridge those gaps.

Continuous Improvement

RPA is purely rule-based and to provide a complete holistic automation solution, it is extremely critical to integrate RPA with other cognitive capabilities to improve the overall automation experience.

Industries that Use RPA





Oil & Gas



And More

Valenta's RPA Pricing

Implementation Fee + Monthly Fee

Fee Per Document/Process

Profit Share on Savings Achieved

What do our client's say?

  • Since engaging Valenta, we’ve been able to take on a much higher work load. We’ve been able to bring someone into the team for a lower cost, but without compromising on that quality of work. Outsourcing has been a great solution compared to hiring somebody locally. It’s a lot more cost effective and it’s easy to do.

    Natalie Gillies and Clare Harding
    Arthur J Gallagher
  • Valenta has been great, because it allows us to expand and contract our operations, depending on the time of the year, very effortlessly and inexpensively. It’s allowed us to make decisions that we know we can scale quickly on because we know that Jayesh and the Valenta team will be there for us.

    John Pollock
    Financial Gravity

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