Sasken White Papers

Product-as-a-Service (PaaS) Business Model for OEMs | Sasken

Vivek Madathil — Tue, 25 Nov 2025 16:19:58 GMT

Many OEMs in Industrial Equipment domain are moving from product selling model to outcome-based model, where equipment is leased out to customers based on either production-based selling or outsourcing. This new business model is called Equipment-as-a-Service (EaaS) or Product-as-a-Service (PaaS) model. In this whitepaper, we will discuss the impact of PaaS business model on OEM’s existing operations and understand how OEMs can reap the benefits through the adoption of the PaaS proposition.

Beyond the Timestamp: A Strategic Guide to Selecting Your Time Series Database

Praful Khandelwal — Tue, 25 Nov 2025 11:05:52 GMT

This whitepaper provides a critical exploration of time series data, detailing its unique characteristics like high volume, seasonality, and autocorrelation. It outlines a structured framework to evaluate and select the optimal time series database by aligning specific business needs with database capabilities such as write/query throughput, scalability, and real-time analytics. Cut through the clutter of options and make an informed decision to build a robust, efficient, and future-proof data strategy.

White Paper - Cloud Migration & Risk Mitigation | Sasken

Vivek Madathil, Amit Verma — Tue, 25 Nov 2025 10:53:22 GMT

This whitepaper outlines Sasken’s phased methodology for cloud migration, designed to mitigate risks and ensure a smooth transition. It covers key stages, Discovery, Planning, and Implementation-addressing challenges such as dependency mapping, data architecture, and security. With real-world success stories, reusable templates, and a clear migration strategy, organizations can achieve cost optimization, faster time-to-market, and scalable, secure cloud deployments.

Vivek Madathil
Chief Architect, Digital Business Amit Verma
Senior Solutions Architect, Digital Business

Decoding the RFID Landscape: Exploring Ecosystem, Challenges, and Use Cases

Praful Khandelwal — Thu, 28 Nov 2024 12:55:29 GMT

RFID technology is revolutionizing the way businesses track, control, and enhance their assets. Its ability to simultaneously read data from multiple items, even when not in direct view, significantly increases speed, precision, and effectiveness in operations. However, this doesn't make it a simple solution—it presents its own set of difficulties.

This white paper offers a straightforward guide to understanding RFID systems, overcoming their obstacles, and realizing their full potential.

What's Included?

Beyond the Timestamp: A Strategic Guide to Selecting Your Time Series Database

Praful Khandelwal — Tue, 26 Nov 2024 09:12:47 GMT

Monolithic to Microservices Architecture

Praful Khandelwal — Wed, 17 Jul 2024 05:54:06 GMT

This white paper explores the evolution from monolithic to microservices architecture, highlighting key benefits and challenges.

It provides a structured approach for effective adoption and migration, covering essential aspects such as defining service boundaries, strategies for migration, methods for inter-service communication, managing distributed transactions, handling data management and integrity, techniques for efficient scaling, ensuring security, and implementing comprehensive logging and monitoring solutions. Alongside these guidelines, the paper provides practical insights and pros and cons analysis of different options to support successful implementation.

Revolutionizing Two-wheelers using ARAS

Mrinal Sarkar — Tue, 09 Jul 2024 05:57:04 GMT

The economic growth of a country is directly influenced by the efficient transportation ecosystem. Along with rapid technological changes, the world is moving towards faster road transportation. 2-wheelers (also known as motorcycle or scooter) are evolving as one of the most preferred modes of road transportation in both developing and developed countries. India is one of the biggest markets for the 2-wheelers (44.5% of on-road vehicles are 2-wheelers), due to its affordability, high fuel efficiency and finally easy mobility even in heavy traffic conditions.

As 2-wheelers are becoming a preferred mode of transportation, motorists stand at the precipice of a greater risk of accidents. These accidents are fatal in nature as the design and build of a 2-wheeler may not be able to withstand severe crashes.

According to recent studies, deaths or fatality caused by 2-wheeler crashes are 30X more than that of a car. Out of all 2-wheeler accidents, almost 50% of them are caused due to reckless, careless, or distracted driving, such as over speeding, violation of traffic rules, failing to pay proper attention to the road/weather condition, not wearing adequate safety gears by riders, and/or inexperienced riding.

Thus, all automotive industry leaders are focusing on software enabled systems in 2-wheelers that offer various mechanisms and connectivity to enhance safety not only for the rider, but also for pedestrians.

Software enabled safety system for 2-wheeler includes Advanced Rider Assistance Systems (ARAS) which is based on two important technologies available as of today – Computer Vision by Machine Learning and Vehicle to Everything (V2X/CV2X) communication.
The aim of ARAS is to enhance 2-wheeler safety by using advanced algorithms that combine computer vision by machine learning and V2X/CV2X wireless communication with nearby vehicles or infrastructure (like traffic lights) with real-time information, and thereby detect safety risks and imminent crash/danger to the rider. ARAS analyzes real-time camera feeds from the front and rear cameras along with inputs from different other sensors (Radar, Gyro, Accelerometer, Tire pressure monitor, GPS/GNSS etc.) mounted on the 2-wheeler to create a computer vision.

Sasken views on Generative AI at the Edge

Debashis Panigrahi — Tue, 12 Mar 2024 06:09:19 GMT

It is early 1960s. Sales, customer success, and lead management teams are struggling to keep up with the incoming products and/or services queries. The first instance where Generative AI models took shape of robotic conversation assistance to tackle queries and suggest solutions based on algorithms. We’ve interacted with Generative AI models for decades. However, since 2014, we’ve witnessed it in action through Generative Adversarial Networks (GAN). The GAN could create images, videos, and audio. But, what’s the difference between Generative AI models of 2014 to 2024? Download our White Paper “Generative AI at the Edge” where we explore the opportunities and challenges of this technology specifically for the Automotive Industry.

What’s Inside this White Paper?

The whitepaper acts a comprehensive analysis of the implications and potential applications of deploying Gen AI models (edge/on-device) within the automotive industry. It explores the myriad opportunities that this technology presents, including enhanced customer experiences, efficient resource allocation, sales growth, and accelerated design iterations. It also addresses the data privacy concerns, computation constraints, and the need for robust infrastructure through relevant examples.

What to expect from this White Paper?

Introduction to Generative AI and Edge Computing
Historical Nuances of the Generative AI Application
Generative AI applications for the Automotive industry
Challenges of running Generative AI models at the Edge
Well-structured approach to develop, deploy, and maintain Large Language Models (LLMs) and Language Vision Models (LVMs) utilizing an LLM-ops framework.

White Paper - Total Cost of Ownership: GMS/Non-GMS | Sasken

Senthil Kumar Subramani & Debashis Panigrahi — Wed, 24 Jan 2024 09:14:50 GMT

This whitepaper aims to explore and highlight the key distinctions between Android Open-Source Project (AOSP) and Google Mobile Services (GMS) devices. The primary focus of exploration is on their impact on the total cost of ownership (TCO) in an enterprise setting across key aspects, including security, flexibility, productivity, sustainability, & the ecosystem. While AOSP devices offer a baseline open-source platform, GMS devices may provide a more comprehensive and user-friendly experience through the integration of Google’s widely used applications. The inclusion of GMS in enterprise devices can positively influence productivity, collaboration, and overall user satisfaction. The whitepaper concludes by establishing a comprehensive framework for TCO calculation and facilitating a comparative analysis of AOSP and GMS devices, factoring in the various costs involved throughout the device’s lifetime.

Data Integration Platform Characteristic Trait

Bhabani Prasad Upadhyaya — Wed, 03 May 2023 07:36:35 GMT

In recent times, for every business, the associated data volume has been quite large and complex in nature. The sources that generate these data are quite important and need to be understood by businesses, and according to the data pattern, businesses need to realign or take important decisions. However, the data types originated from diverse sources are not similar; hence, every organization needs its data handling capacity to grow up to use the heterogeneous data source by implementing a modern data integration tool that is able to optimize the data management capabilities and strategies. The data generated from scattered sources are heterogeneous in nature and holds different formats of data; hence, it is quite complex to apply business rules and perform data analysis. Hence, choosing the right data integration tool to keep up with the changing pace and apply automation features is quite important to driving the data integration space. These features should be able to reduce the overall project development time and easily outline complex business areas. Data integration tools are capable of moving sets of data from various sources; they sometimes apply themselves to a few business rules and other times they don’t change, but the metadata around the data changes from source to target system. Meeting these norms is quite complex and tedious sometimes where integration tools came to a savior.