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2020 Blog, Blog, Featured

The recent COVID-19 virus outbreak has driven many unanticipated changes in the way we do business. In particular, many people who usually go into the office are now required to work from home, which can mean different access methods (e.g. VPN Access) and permissions. These changing conditions have the potential to overwhelm service desks.

We have seen this with our clients in terms of a dramatic increase in service requests. We are pleased with the role our automation has played during these trying times. Our intelligent BOTs have enabled Relevance Lab to respond to upticks in service requests instantly.

Figure 1: Intelligent Automation Eliminated Service Desk Impact

With one of our significant clients with a large NYC footprint, daily inbound tickets increased 92% (Fig.1) during the March Peak Day (vs Feb avg) as the effects of the Corona virus forced people to work-from-home. Fortunately, nearly all those tickets are being managed using RL’s Intelligent BOTs, and we were able to handle the increased volume with no delay. Our Intelligent BOTs handled two and a half times (Figure 2)) their normal daily workload so that our service desk team could maintain focus on other critical business needs.

Figure 2: Dramatic Ticket Spike as People Prepared to Work-from-Home

Over the last year, we have increased the coverage and complexity of our Intelligent Automation to achieve 70-80% inbound ticket automation with an equivalent reduction in human efforts. Having a robust platform with standardized processes, BOTS driven automation and reliable Automation Analytics have helped better prepare for the unknowns.

As we all continue to weather this crisis together, please stay safe. We wish everyone the best.

For more information feel free to contact


2020 Blog, Blog, Featured

As per multiple market research sources, AIOps market will grow at a CAGR of 33% till 2024. Though the markets attribute the growth factor to the hybrid readiness of the AIOps technologies clubbed with RPA, we at Relevance lab believe that RPA alone may not solve the problem in its totality.  Hence our AIOps platform is driven by Intelligent Automation.

AIOps is gaining traction because of its ability to provide real-time data by eliminating silos, better tracking and management and automate problem-solving. As per one of the private research firms, about 87% of the organizations are creating value by using AIOps platforms.

What business challenges do you solve using an efficient AIOps Platform?

  • Improve Operational Efficiency
  • Reduce Customer Churn

With increasing complexity and dynamism of IT environments, including infrastructure and applications, the ability to provide a holistic platform is critical today. So how do you go about evaluating the right platform?

Problem Identification:

Ability to identify the problem on a timely basis by analyzing the infrastructure and application behavior coupled with Digitization and cloud migration. This rising trend is to drive the revenue growth of the market over the next five years.


Does the platform seamlessly integrate with ITSM, ITOM, Cloud and Automation platforms? One of the most significant challenges seen in enterprises today is the silos. This is where systems, skills, data and infrastructure are ‘owned’ by a specific team or department which works in isolation from other parts of the organization. Any solution being considered must be technology, location, vendor, data and domain agnostic.

Data Normalization:

Ability to collect unstructured and fragmented data to provide actionable insights. Ideally, before you make any decisions on IT solutions, you have analyzed and understood what data you have, where it is stored, who ‘owns’ it and what value it has to the business.

Relevance Lab’s Agile Analytics helps global organizations with solutions that run across the entire organization with speed, responsiveness and flexibility.

Leverage AI-based Technology for RCA:

The introduction of cheap compute resources, the ubiquity of data and the adoption of new technologies such as Artificial Intelligence (AI) all mean that software-based RCA techniques can and should be implemented as a priority. AI, and especially Machine Learning, can process vast amounts of data to detect anomalies in real-time and to predict potential issues and uncover trends.

Real-time & Pro-active:

The introduction of Artificial Intelligence-based technologies has seen the ability to analyze huge amounts of data to detect anomalies in real-time and to perform predictive analysis to prevent service outages. Combined with the ability to automate actions, these technologies allow the organization to move from reactive to proactive.

Flexibility, Openness and Future-ready:

Often IT Solutions that you are discussing are complicated and expensive. Therefore, evaluating a solution which is not; way below the benchmark nor way too above the benchmark or beyond reach is critical. Hence Carefully reviewing the design and architecture of your IT solution to ensure that it is ‘future-proof’ is recommended.

  • Is the product built on proprietary technology that may be difficult to maintain and support in the future?
  • If the solution uses 3rd party products or services, can these be easily swapped out for others?
  • Is the solution ‘open’ with well-defined APIs and integrations?
  • Will the solution be able to perform and scale to your growth plans?

Implementing ITOM, ITSM and AIOps are now the need of the hour wanting to improve service, customer satisfaction levels and the overall operational efficiency by removing silos and leveraging Intelligent Automation.

There is a plethora of choice out there in the market. However, choosing your technology wisely is recommended.

For more information feel free to contact


2020 Blog, BOTs Blog, DevOps Blog, Blog, Featured

Stories are quite very compelling and a way of life. All of us need a story to believe in and with no exception to the CXO’s of the tech and enterprise giants. They need a reason to believe that Digital Transformation can be frictionless with the choice of right platforms and partners.

With growing interest & investments in new concepts like Automation and Artificial Intelligence, the common dilemma for enterprises is how to scale these for significant impacts to their relevant context. It is easy to do a small proof of concept but much harder to make broader impacts across the landscape of Hybrid Infrastructure, Applications and Service Delivery models. Even more complex is Organizational Change Management for underlying processes, culture and “Way of Working”. There is no “Silver bullet” or “cookie-cutter” approach that can give radical changes but it requires an investment in a roadmap of changes across People, Process and Technology.

Relevance Lab has been working closely with leading enterprises from different verticals of Digital Learning, Health Sciences & Financial Asset Management on creating a common “Open Platform” that helps bring Automation-First approach and a maturity model to incrementally make Automation more “Intelligent”.

Relevance Lab offers RLCatalyst – An AIOps platform driven by Intelligent Automation paves way for a faster and seamless Digital Transformation Journey. RLCatalyst Product is focused on driving “Intelligent” AUTOMATION.

AUTOMATION is the core functionality including:
  • DevOps Automation targeting Developer & Operations use cases
  • TechOps Automation targeting IT Support & Operations use cases
  • ServiceOps Automation targeting ServiceDesk & Operations use cases
  • SecOps Automation targeting Security, Compliance & Operations use cases
  • BusinessOps Automation targeting RPA, Applications/Data & Operations use cases)

Driving Automation to be more effective and efficient with “Intelligence” is the key goal and driven by a maturity model.
“Intelligence” based Maturity model for Automation
Level-1: Automation of tasks normally assisting users
Level-2: Integrated Automation focused on Process & Workflows replacing humans
Level-3: Automation leveraging existing Data & Context to drive decisions in more complex processes leveraging Analytics
Level-4: Autonomous & Cognitive techniques using Artificial Intelligence for Automation

RLCatalyst Building Blocks for AIOps

AIOps Platforms need to have common building blocks for “OBSERVE – ENGAGE – ACT” functionality. As enterprises expand their Automation coverage across DevOps, TechOps, ServiceOps, SecurityOps, BusinessOps there is need for all three stages to Observe (with Sensors), Engage (Workflows), Act (Automation & Remediation).

RLCatalyst provides solutions for enterprises to create their version of an Open Architecture based AIOps Platform that can integrate with their existing landscape and provide a roadmap for maturity.

  • RLCatalyst Command Centre “Integrates” with different monitoring solutions to create an Observe capability
  • RLCatalyst ServiceOne “Integrates” with ITSM solutions (ServiceNow and Freshdesk) for the Engage functionality
  • RLCatalyst BOTS Engine “Provides” a mature solution to “Design, Run, Orchestrate & Insights” for Act functionality

For more information feel free to contact


2020 Blog, Blog

Probably the probability of redefining Marketing is (Read as numeric 1. P (Redefining Marketing) =1.) “One”. The paradox is; that it is inevitably certain!  It’s high time to re-define Marketing and I have made a humble attempt on it below.

Marketing is as a well-targeted, conversion-oriented, quantifiable, and interactive method of converting a prosumer into a consumer and vice-a-versa and thereby promoting new or existing products or services with the help of innovative technology as an enabler to predict needs, acquire and retain customers. Well easily said, however, it’s a mix of storytelling, data analysis, technology, customer experience design, experimentation’s, systems thinking, and of course brand management, a combination of skill set that may be hard to find.

A marketing scientist should be capable of understanding automation, data and emotions equally well to make it simpler. Well, then will humans really perish as a result of AI, Absolutely no? However, it would definitely force the community, to deviate from their conventional approach and take on a new way of working and life. A fully automated integrated marketing platform should do the following:

  • Gather Data
  • Plan and Automate
  • Increase value

While marketing scientists are capable of working with little or without any data at all with highly intuitive and psychological skills, they gather insights from experimentations like A/B testing to study content and its impact on behaviour. They use these tactics to render content based on dynamic segmentation and obviously, that would be “Segment of One” by all means.

Read Intuitive and Psychological skills as: “The machines may still need a human to do certain things, that it can’t do and therefore the “Future of jobs” may be at the dichotomy of Humanities and Science”. A gap that our educational system may have to rapidly fill in order to avoid urban depressions and suicides. However returning to the marketing scientists which may be the way to go, the scientific Methods used by a Marketing Scientist includes the following:

  • Listening
  • Framing Hypothesis
  • Experimenting and Collecting Data
  • Analysis, Inference and Conclusion:


Listen which in an otherwise traditional Market Research terminology is stated as “Observe”. Many marketers do not allocate budget for listening, which imperatively means deploying an AI-based system to listen to the existing and prospective customers on their needs across various channels which may include:

Web to map customer journey and tap behaviour which may include Frequency, Recency, Depth (Interest), Time, Source and thereby arrive at a Purchase Intent scoring. Any transactional data on their respective e-commerce engine would allow the recommendation engine to make the next best offer.

  • Mobile App / Wallets
  • Social Media
  • Email
  • Chat
  • Point of Sale (Includes Physical Store and Electronic Kiosks)
  • IVR
  • USSD

Thereby, breaking the Data Silos and creating a 360-degree customer view or a true “Omni Channel”, which today only exists in the form of presentations, while there are several tall claims.

Frame hypothesis:

Develop a hypothesis which is deeply embedded in the target audience.

Traditionally companies have been attempting at persona development. However, reinstating an earlier said statement in the current context:

The probability of identifying a persona (P (Persona) =1) is one. Having said, it simply means no two personas are identical. Every customer is different and therefore needs to be engaged differently. A simple approach that today’s recruiters or talent analysts will certainly fail on and hence identifying the kind of marketing scientist that one will require will be one of the biggest challenges of today and tomorrow.

Reason to Buy: That’s your story. The story would change from customer to customer, however, the value you offer may not change.

Measure: Deploy processes to measure both in qualitative and quantitative means.

Experimentation and Data Collection:

Experiment on channels, content, segments, spend, pricing and packaging. This experimentation for a marketing scientist is not just limited to the digital means like A/B testing.

Analysis, Inference and Conclusion:

This could be one of the most interesting aspects of a marketing scientist’s job. A few examples of Analysis and methods are mentioned below:

Attribution Modelling – Optimize ad/channel spends based on the conversion goal paths and by assigning weightages to the sources.

Cohort Analysis – Convert Data into dollars by analysing customer groups across a variety of common attributes and create engagements specific to cohorts

Transaction Analysis – Convert visits into conversions by analysing product sales potential and create engagements specific to product groups.

Product Analysis – Identify the strong and weak products and enable engagement through offers, coupons to the audience at a one to one level

Measure and fine-tune conversion goal paths – by reverse goal path Analysis based on the last URL. Timely interventions by means of engagement to avoid path diversion.

Page Analysis and Heat Maps – Identify the page performance to enable optimization

Measure your content for effectiveness – Perform split test or multivariate analysis to arrive at the right content.

Enabling email automation for conversions – The email marketer can publish and track recipients to the website actions and automate response-based email marketing for effectiveness

Sentiment Analysis – Identify the social sentiment of your brand or events across social media. Identify the key influences and trending

Think in probabilities, that’s one of the fundamentals in order to pursue a career as a marketing scientist. For those of you; who skipped your probability classes, continue learning.

About the Author:

Ajeesh is Senior Marketer who has built high-performance teams to drive revenues for new and re-positioned brands across the B2B / B2C segments. He has a blend of the right and the left brain, creative genius, occasionally crazier and yet adamantly saner than the average person.

An educational evangelist and a brand champion he loves studying competitive landscapes and designing product vision and global market strategies.


2020 Blog, AIOps Blog, Blog, Featured

With distributed assets across Cloud and non-Cloud environments covering desktops, servers and other devices enterprises are still having a fragmented approach to basic needs of patch management. This brings in unique risks from a Security and Vulnerability perspective. Even when companies do have focus on this area there is a lack of integration between asset management, vulnerability assessment, patch management and governance to ensure a comprehensive solution that leverages “Automation First” Approach and integrated workflows. This is where RLCatalyst ServiceOne brings in a solution for enterprises to leverage this in a Managed Service Model.

The solution covers all enterprise assets and helps do a discovery, vulnerability assessment and then managing the full-lifecycle of Patch Management. The reason patch management is more complicated since large enterprises commonly have modern and legacy systems covering desktops (Windows, Linux, MacOS), Servers (Redhat, Debian, Ubuntu, CentOS, Windows Servers etc.), Network Devices and others covering assets in data centres and cloud (AWS, Azure, GCP, etc.)

RLCatalyst ServiceOne Solution – Five Layers of Vulnerability & Patch Management of your Infrastructure

The whole process of Intelligence Automation of SecOps starts with the asset inventory to ensure you have complete control and visibility of your Infrastructure. Once this is put in place, the next important aspect would be to run periodic Vulnerability Scans using third party applications like Qualys, AWS Inspector etc. Based on the VA scan report, we need to put an automated patch management solution, post which we can run the SIEM tools which can give a real-time analysis of security alerts. The dashboard or the reports provide a holistic view of the health of your overall Infrastructure from a security standpoint, which the CIOs of any Organizations would be keen to see daily.

ServiceOne Patch Management Solution:

ServiceOne Patch Management Solution is a fully integrated solution with Patching, Backup & recovery. Our solution is integrated with ITSM for the overall management of the solution which can help the organizations run periodic scheduled /unscheduled/ad-hoc scans on the system to identify the missing patches and patch them using an approval process.

The IT team verifies the patches based on the periodic scans and categorise them based on the criticality and bundle them. This can then be pushed to the Application owners who can login to ServiceNow and check the available bundles against their set of servers and approve them or reject them. Once approved, basis the next available scheduled maintenance windows, this can then be automated to schedule a backup of the image of the patching servers and then patch the development servers.

The next step would be an approval process post patching to the app owners to check and confirm the application compatibility and functionality of the patches against their applications.

The app owners in this case has the option to reject the patching in ServiceNow in which case, the image which was taken as backup would be restored back to the development instance and in case of approval, the same would get scheduled automatically for patching during the next maintenance window on the production servers

With RLCatalyst ServiceOne solution we provide enterprises a combination of Consulting, Technology and Integrated Services to take care of end to end patch management needs. Customers can leverage the best of the products in the industry across service orchestration, asset discovery, vulnerability assessment, patch lifecycle management and compliance. Enterprises can get started in less than 4 weeks for onboarding, setup, initial compliance and on-going upgrades. A large global enterprise saved $0.5 Million in the first year of operations as they transitioned 5000+ assets across 10+ data centres & Cloud regions into ServiceOne Integrated Patch Management solution with Relevance Lab Managed services.

For more information feel free to contact