Digital Automation & AI

Digital Twins Technology

Oil & Gas Company Fuels the Future with Digital Twins

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Client:

Our client, a global leader in the oil and gas industry (OGC), operating across 80 countries, plays a vital role in supplying fuel, energy, retail services, and petrochemical products. 

Challenge:

The oil and gas industry embraces advanced technologies like AI, IoT, Big Data, and cloud computing. The emergence of ‘Digital Twins’ offers unparalleled insights into operations—a dynamic, virtual representation of physical assets or systems created using real-time data. Despite recognizing its potential, OGC faces challenges in bridging the gap between digital replicas and reality due to complex manual data processing. 

In the ideal scenario, OGC envisions capturing facilities using a high-resolution 3D system and importing them into a comprehensive application platform as the ultimate single source of truth. However, manual processes hinder reaching this state, involving navigating through various source documents and applications, making the alignment of digital information a challenge. 

Action:  

Enter the autonomous pipeline—a system developed by Prolifics using AI to automate manual activities. While full autonomy is a long-term goal, the current approach involves leveraging AI to autonomously perform most process steps, reducing reliance on extensive human resources, expediting reconciliation, and paving the way for future enhancements. 

Prolifics actively engages in: 

  • Proof-of-Concept (POC): Utilizing AI accelerators as reconciliation tools for visual digital twins. 
  • Incremental Feature Development: Enhancing AI tools for specific requirements within OGC’s operations. 
  • Limited Iterations: Testing and refining in two phases, focusing on specific facilities and a restricted scope of assets. 
  • Scaling Efforts: Aiming to extend autonomous efforts across all OGC’s assets and digital sources. 
  • Comprehensive Support: Providing assistance in data integration, real-time connectivity, user training, scalability planning, and cybersecurity. 

Results:

The tangible results include immediate hard savings in time and money, empowering better planning, improved worker experiences, precise simulations, enhanced failure predictions, and more efficient cost reduction. Achieving a single source of truth aligns with OGC’s sustainability goals, enabling efficient facility planning and anticipation through simulation. The technology’s versatility contributes to unlocking various use cases, potentially saving hundreds of millions of dollars for OGC. 

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Elevating Mortgage Lending: A Testing Center of Excellence Transformation 

Elevating Mortgage Lending: A Testing Center of Excellence Transformation 

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Client

A mortgage lender (ML) specializing in technology-driven and personalized solutions for homebuyers. 

Background: 

In the highly regulated mortgage sector, precision and compliance are paramount. Enter the Testing Center of Excellence (TCoE) and Test Automation – critical strategies reshaping the industry. TCoE centralizes testing, fostering collaboration and raising software quality. Meanwhile, Test Automation turbocharges processes, reducing errors and costs. It’s a game-changer that ensures adaptability in a dynamic market. This case study comes from Prolifics Test Automation Solutions and explores how TCoE and Test Automation are transforming mortgage lending.  

Challenge

ML embarked on a strategic mission to elevate the quality of their software products, reduce costs, streamline operations, and enhance the efficiency of their IT and development functions. However, they faced several testing challenges: 

  • Understaffed Testing Team: ML faced the challenge of having too few testing experts, prompting the need to optimize testing processes. 
  • Developer Involvement in Testing: They aimed to reduce developer involvement in testing through more efficient testing methods. 
  • Scalability Concerns: ML recognized the need for scalable solutions to support their growing team and complex projects in line with their operational streamlining objectives. 

ML partnered with Prolifics to strategically enhance their capabilities and improve operational efficiency. This transformation centered around the creation of a Testing Center of Excellence (TCoE), a cornerstone for increasing testing efficiency, standardizing processes, and leveraging automation. The TCoE represents their commitment to streamlining testing efforts, ensuring robust and secure software releases with every step forward. 

Approach

We will collaborate with ML to establish a Testing Center of Excellence (TCoE) – a centralized framework that focuses on optimizing and standardizing software testing and quality assurance processes. The primary goal of this TCoE is to enhance the quality of ML’s software products while reducing costs and improving efficiency. 

 

Key Components of the Prolifics Testing Center of Excellence Approach: 
  • Standardized Processes: We establish best practices, methodologies, and standardized testing processes to ensure consistency across all ML projects and teams. 
  • Resource Centralization: We centralize testing resources, including skilled testers, tools, and infrastructure, allowing efficient resource sharing across various ML projects. 
  • Test Automation: Our approach includes implementing and managing test automation strategies to enhance efficiency and reduce manual testing efforts. 
  • Knowledge Sharing: We facilitate knowledge sharing and collaboration among testers and teams to foster continuous learning and improvement. 
  • Metrics and Reporting: Our approach involves developing metrics and reporting mechanisms to measure the effectiveness of testing efforts and provide valuable insights for decision-making. 
  • Tool Selection and Management: We identify and manage testing tools and technologies that align with ML’s specific needs and goals. 
  • Quality Assurance: Our approach ensures that testing aligns with quality assurance principles, encompassing risk assessment, defect tracking, and validation against ML’s business requirements. 
  • Training and Skill Development: We provide training and skill development opportunities for testing professionals to keep them updated with industry trends and best practices. 
  • Governance and Compliance: Our approach guarantees that testing processes adhere to industry standards, regulations, and compliance requirements, particularly crucial for highly regulated industries such as finance.  

 

Results

By establishing a TCoE, ML will achieve greater testing efficiency, reduce manual tasks, enhance collaboration, and deliver higher quality experiences to their employees and customers. It also allows them to adapt more effectively to changing business needs and technological advancements. This shift promises to bring about the following key advantages: 

 

Efficiency and Process Optimization:
  • Improved Testing Efficiency: Streamlined processes, faster testing cycles, and reduced duplication of efforts and resources. 
  • Alignment with Release Model: Alignment with ML’s unique weekly release model, eliminating the need for repetitive testing with each release. 
  • Better Test Automation: Enhanced automation strategies for higher coverage, faster execution, and improved script maintenance. 
  • Resource Optimization: Efficient allocation of testing resources based on project priorities and requirements. 
Quality Assurance:
  • Enhanced Test Quality: Standardized practices leading to higher-quality deliverables and fewer defects reaching production. 
  • Increased Test Coverage: Comprehensive test coverage, focusing on critical business functionalities and customer use cases. 
  • Reduced Escalations: With automated testing ensuring the stability of each release, the client expects a decrease in customer escalations related to software issues. 
  • Improved Customer Experiences: The enhanced efficiency and reliability of automated testing are poised to lead to improved customer experiences, as software releases become more dependable. 
Cost Reduction and Risk Management: 
  • Cost Reduction: Optimized resource allocation, resulting in lower testing costs and economies of scale. 
  • Risk Mitigation: Proactive identification and mitigation of testing-related risks, with improved project visibility. 
Team Development and Stakeholder Confidence:
  • Skilled Testing Teams: Training and upskilling lead to a more proficient workforce and knowledge sharing. 
  • Stakeholder Confidence: Clear communication and reporting on testing progress, results, and alignment with business goals. 
  • Continuous Improvement: Establishment of a culture of continuous improvement, regularly adapting to evolving needs and trends. 
Compliance and Standardization:
  • Regulatory Compliance: Ensuring industry-specific compliance through standardized testing processes.
     

About Prolifics Test Automation Solutions

At Prolifics, we empower you with Test Automation and TestOps services tailored to your needs – so you can stay ahead in the fast-paced software world. We offer end-to-end automation testing and seamlessly integrate TestOps into your development cycle. Accelerate your testing, enhance software quality, save costs, and speed up your time-to-market. Lead in software innovation with our dedicated support.  Connect with an expert today! 

 

Fintech Company Onboards Customers Quicker and Cheaper with AI and Bots

Fintech Company Onboards Customers Quicker and Cheaper with AI and Bots

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Our client

This US-based fintech company offers payment processing and point-of-sale (POS) solutions, payroll, insurance coverage, gift and reward cards, and other financial tools for small businesses. This success story falls under Prolifics’ Intelligent Process Innovation and Digital Automation & AI offerings. We acquired this client through our Intelligent Automation Workshop.  

Challenge

Our client targets small businesses with two main offerings – payment processing / point-of-sale (POS) solutions and payroll processing. For both offerings our client provides equipment and training, as well as a line of credit. Our client ensures that a small business is worth the client’s investment of equipment, time and financial risk through a rigorous onboarding and underwriting process. 

Unfortunately, much of this onboarding and underwriting involves intensive manual steps and procedures. The small businesses are sending in applications through our client’s website or as document attachments in emails. Our client needs personnel behind the scenes just to download application packages to place into their homegrown workflow system. Client “on-boarders” processing the applications have been taking a long time to go through the forms, which are often hard to read and are missing pages, which then requires connecting back to the business. The client currently has three on-boarders, who are only able to process about 260 applications in a month. This doesn’t include the time needed for underwriting, which is handled by different personnel. In addition, the client had no real processes or procedure for storing application information in a database – it went in as a “blob.”   

Our client is looking to implement an aggressive growth plan, with a target of 1,600 applications a month. However, with the inability to scale the existing staff, this would mean adding about 19 new people. The client looked to us for technological help. 

Action

We conducted our Intelligent Automation Workshop with our client. The Workshop pairs client personnel with our automation experts to create a strategy, roadmap and solution that leverages intelligent automation. It will provide continuous process improvement that maps to the client’s business outcomes. Workshop activities include interactive sessions to refine and score opportunities for improvement, validate business drivers and develop an action plan for next steps. 

 Workshop goals include: 

  • Understanding strategic objectives and business outcomes 
  • Leveraging Prolifics methodology to map business outcomes to key performance indicators (KPIs) 
  • Identifying insights for opportunities for improvement that directly improve KPIs 
  • Building and implementing a business case for improvement through leveraging different automation capabilities 

 Workshop take-aways include:  

  • Implementation roadmap 
  • Process transformation outlined through Prolifics Business Benefit graphs 
  • Assessment of business drivers, pain points, objectives, and benefits from use case information 

Result:

The client found that its investment in terms of time dedicated to the workshop was a success and that the approach led to concrete conclusions. Prolifics VP Salem Hadim heads both our Intelligent Process Innovation and Digital Automation & AI offerings. “Our client’s CEO had the key mandate to grow the organization without adding operational costs,” said Hadim. “In the workshop we quickly identified the use case where automation and process innovation will help our client. We clearly established the pain points, the improvement opportunities, and – most importantly – we gave him an ROI that meets his mandates. The CEO was tremendously happy with the workshop and its take aways.” 

The improvement opportunities derived from the workshop included a “quick win” pilot program that: 

  • Begins with using Intelligent Document Processing (IDP). IDP is a process that leverages artificial intelligence (AI) to read text and extract the needed information. With infused AI, IDP reads the applications (whether through the website or email attachments) just as a human is able to read them – only faster, cheaper and with a much lower error rate.
  • Once IDP captures the information from the submitted application, robotic process automation (RPA or “bots”) is used to complete all the manual work that the on-boarders had to deal with. With bots doing much of the work, the underwriters got all the information they needed neatly prepared and packaged for them.
  • We identified and gave the client options on ways to store the application documents, and how to search those documents for audit and compliance purposes. 

We’ve estimated that this quick win program will save them more than $750,000 annually, with a much faster application throughput rate. Hadim notes, though, that a quick win program decision, especially when implementing automation, should not be made in a vacuum – it’s part of an overall, holistic approach that differentiates Prolifics from competitors.  

“When you automate, it must be the correct adoption of automation,” said Hadim. “You must make sure you look at your process end-to-end, and then use automation the right way. It’s combining a tactical approach to make a quick win, but also looking at things strategically so that the quick win is not thrown away. Many vendors will automate something that’s not needed – the company should be changing and innovating the larger processes. Automation must be part of your roadmap, part of your enterprise-wide adoption.” 

As a fintech company, our client is part of the larger banking, financial services and insurance (BFSI) industry that Prolifics serves on a regular basis. Hadim said, “Recently we’ve implemented bots at a mortgage company to help them scale; implemented application automation at a financial clearing house; and configured the entire infrastructure automation for a credit card processor’s new data center. BFSI companies are ripe for process innovation and RPA.” 

More on our Intelligent Process Innovation and Digital Automation & AI offerings

Prolifics Intelligent Process Innovation looks at an organization’s processes from both the top down and the bottom up. We use this holistic view to identify opportunities for new competitive advantages and outcomes, including reduced operational costs, higher levels of employee productivity and customer experience, and greatly improved compliance. 

With Digital Automation & AI, our focus is utilizing robotic process automation (RPA / bots), artificial intelligence (AI), conversational AI, generative AI and data science to automate and scale your operation’s low-value, manual and repetitive tasks. These processes are now more productive in terms of time, cost and quality, and your human knowledge workers can now handle other, high-value innovative projects. 

About Prolifics

At Prolifics, the work we do with our clients matters. Whether it’s literally keeping the lights on for thousands of families, improving access to medical care, helping prevent worldwide fraud or protecting the integrity and speed of supply chains, innovation and automation are significant parts of our culture.  While our competitors are throwing more bodies at a project, we are applying automation to manage costs, reduce errors and deliver your results faster.       

 Let’s accelerate your transformation journeys throughout the digital environment – Data & AI, Integration & Applications, Business Automation, DevXOps, Test Automation, and Cybersecurity. We treat our digital deliverables like a customized product – using agile practices to deliver immediate and ongoing increases in value.  Visit prolifics.com to learn more. 

Navidating the Integration Maze: Challenges in Incorporating LLM's into Your Application

​​​Navigating the Integration Maze: Challenges in Incorporating LLM’s into Your Application​

Written by Rajeev Sharma, Senior Architect, Digital Experience and Intelligent Automation, Prolifics 

The rise of Large Language Model (LLM) APIs has opened exciting possibilities for natural language processing in applications. Leveraging pre-trained LLMs through APIs empowers developers to perform limitless language-related tasks with ease. However, integrating these powerful APIs into your application comes with its own set of challenges. This blog is intended for those who are planning to integrate LLM APIs and want to build an application or service around it. I would like to highlight some common challenges and obstacles that might come during this journey. A proper planning and suitable architectural decisions are a must to consider in advance for such scenarios to attain an overall rewarding outcome.

Let’s explore some of the common challenges.

1. Choosing the suitable LLM API

It is a very daunting task to choose the most appropriate LLM for your application. With various APIs available, each offering distinct features and pricing structures, it is essential to select one that aligns best with your application’s requirements and budget.  Some factors to consider when choosing an LLM API provider include the size of the model, the training data, the price, and the ease of integration.

2. Quota and Rate Limit Restriction

Many LLM APIs impose rate limits or usage quotas to manage server loads and prevent abuse. Integrating an LLM API without considering these limitations could lead to unexpected interruptions in your application’s functionality. Solutions like imposing per user deliberate delay and implementing intelligent throttling at the server level can assist in building a seamless experience.

3. Contextual Memory and Context Limitation

By design LLMs are stateless – meaning each incoming request is processed independently of other interactions. It is crucial for some applications like chatbots to have an idea of context and previous interactions. Though there is no perfect solution available currently to handle this, different vendors have started implementing a concept of conversational memory in their frameworks. It’s crucial to keep the application architecture flexible to accommodate these advancements seamlessly and to keep an eye on improvisations happening in this area and accommodate them.

4. Templating

In real time use cases the prompt submitted to LLM will contain hard-coded text and data from other data sources. Using a templating framework will help organize your prompts and avoid cumbersome string concatenations in the primary code base. This helps in creating the application neat and readable for others. While not a significant challenge, it’s essential to ensure efficient code structure and make it scalable for future changes.

5. Handling Errors and Failures

Integration with external APIs introduces the possibility of network failures, timeouts, or other errors. How your application handles such scenarios can significantly impact user experience and overall application stability. The best way to sort this out is the old school way of implementing robust error-handling mechanisms to gracefully handle API failures. Provide clear error messages and fallback strategies to prevent application crashes or data loss.

6. Testing and Fine-tuning

To reach a satisfactory level of accuracy from a LLM model, a significant amount of testing is required. Most of the times it’s just prompt engineering with lot of trial and errors and fine-tuning based on the user feedback. In my opinion there are two key methods which can help achieve effective testing and stable system:

  • As part of the application planning and design, setup a process for gathering feedback from users, tracking mechanism and close the loop.
  • For testing, you would want a method by which the QA Engineer could change the templates, enrich them with suitable data and execute the prompt to quickly validate that we’re getting what is expected.

7. Implementing Caching Strategy

The LLM API calls comes with a cost. If you are handling requests on a large scale, you will incur high charges on the API calls, you may hit rate limits, and your app performance might degrade. If the inputs repeat over multiple calls, its always advisable to maintain a cache and save the pre-processed LLM output there, thereby serving it directly from the cache preventing extra network hop. The challenge here is deciding a cache provider, devising cache strategy and monitoring. Though it is not that hard to implement this, it adds another layer to the overall architecture of the system.

8. User Privacy and Data Security

This is the most challenging part of this process. LLM APIs might require sending user data to external servers for processing. Ensuring data privacy and protection against potential breaches is a significant concern. You also need to be mindful that no sensitive proprietary data or personally identifiable information (PII) traverses through the wire. Some suggested solutions for this can be:

  • Adopt encryption protocols to transmit data to the API.
  • Consider anonymizing or tokenizing user data via a filter layer in the application.
  • Enforce audit logging of all actions as part of the core design.
  • Perform security and penetration (pen) testing of the application before making it live over Internet.

Integrating Large Language Model (LLM) APIs holds immense potential for building user-friendly, robust applications. Embracing the challenges head-on, thoughtful strategies and a proactive approach, you can navigate the integration maze and unlock the full potential of LLM APIs, providing your application with an unparalleled language processing prowess.

As you venture into harnessing the power of LLMs, partnering with an experienced and innovative technology company can make all the difference in overcoming these obstacles seamlessly. Contact us today to embark on an exciting journey of AI solutions and innovations.

 

 

About the Author:

Rajeev Sharma is a Senior Architect and Competency Lead for the Prolifics Digital Experience and Intelligent Automation India Practices. Throughout his career, he has led several teams of varying sizes, and has been responsible for the design and delivery of various Enterprise products and applications. His strength lies in his ability to closely engage with clients, understanding their precise business needs, and translating them into robust software systems.