How low-code empowers mission-critical end users

Industrializing business-critical end-user compute-based applications using low-code platforms

Low-code platforms enable rapid conversions to technology-managed applications that provide end users with rich interfaces, powerful configurations, easy integrations, and enhanced controls.




    Many large and small enterprises utilize business-managed applications (BMAs) in their value chain to supplement technology-managed applications (TMAs). BMAs are applications or software that end users create or procure off-the-shelf and implement on their own; these typically are low-code or no-code software applications. Such BMAs offer the ability to automate or augment team-specific processes or information to enable enterprise-critical decision-making.

    Technology teams build and manage TMAs to do a lot of heavy lifting by enabling business unit workflows and transactions and automating manual processes. TMAs are often the source systems for analytics and intelligence engines that drive off data warehouses, marts, lakes, lake-houses, etc. BMAs dominate the last mile in how these data infrastructures support critical reporting and decision making. 

    While BMAs deliver value and simplify complex processes, they bring with them a large set of challenges in security, opacity, controls collaboration, traceability and audit. Therefore, on an ongoing basis, business-critical BMAs that have become relatively mature in their capabilities must be industrialized with optimal time and investment. Low-code platforms provide the right blend of ease of development, flexibility and governance that enables the rapid conversion of BMAs to TMAs with predictable timelines and low-cost, high-quality output. 

    Read our Perspective Paper for more insights on using low-code platforms to convert BMAs to TMAs that provide end users with rich interfaces, powerful configurations, easy integrations, and enhanced controls.

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      Conversational assistant boosts AML product assurance

      BANKING

      Conversational assistant boosts AML product assurance

      Gen AI powered responses improve the turnaround time to provide technical support for recurring issues, resulting in a highly efficient product assurance process.

      Client
      A large global bank
      Goal
      Improve turnaround time to provide technical support for the application support and global product assurance teams
      Tools and Technologies
      React, Sentence–Bidirectional Encoder Representations from Transformers (S-BERT), Facebook AI Similarity Search (FAISS), and Llama-2-7B-chat
      Business Challenge

      The application support and global product assurance teams of a large global bank faced numerous challenges in delivering efficient and timely technical support as they had to manually identify solutions to recurring problems within the Known Error Database (KEDB), comprised of documents in various formats. With the high volume of support requests and limited availability of teams across multiple time zones, a large backlog of unresolved issues developed, leading to higher support costs.

      Solution

      Our team developed a conversational assistant using Gen AI by:

      • Building an interactive customized React-based front-end
      • Ringfencing a corpus of problems and solutions documented in the KEDB
      • Parsing, formatting and extracting text chunks from source documents and creating vector embeddings using Sentence–Bidirectional Encoder Representations from Transformers (S-BERT)
      • Storing these in a Facebook AI Similarity Search (FAISS) vector database
      • Leveraging a local Large Language Model (Llama-2-7B-chat) to generate summarized responses
      Outcomes

      The responses generated using Llama-2-7B LLM were impressive and significantly reduced overall effort. Future enhancements to the assistant would involve:

      • Creating support tickets based on information collected from users
      • Categorizing tickets based on the nature of the problem
      • Automating repetitive tasks such as access requests / data volume enquiries / dashboard updates
      • Auto-triaging support requests by asking users a series of questions to determine the severity and urgency of the problem
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      Gen AI powered summarization boosts compliance workflow

      INSURANCE

      Gen AI powered summarization boosts compliance workflow

      Gen AI enabled conversational assistant substantially simplifies access to underwriting policies and procedures across multiple, complex documents.

      Client
      A leading specialty property and casualty insurer
      Goal
      Improve underwriters’ ability to review policy submissions by providing easier access to information stored across multiple, voluminous documents.
      Tools and Technologies
      Azure OpenAI Service, React, Azure Cognitive Services, Llama-2-7B-chat, OpenAI GPT 3.5-Turbo, text-embedding-ada-002 and all-MiniLM-L6-v2
      Business Challenge

      The underwriters working with a leading specialty property and casualty insurer have to refer to multiple documents and handbooks, each running into several hundreds of pages, to understand the relevant policies and procedures, key to the underwriting process. Significant effort was required to continually refer to these documents for each policy submission.

      Solution

      A Gen AI enabled conversational assistant for summarizing information was developed by:

      • Building a React-based customized interactive front end
      • Ringfencing a knowledge corpus of specific documents (e.g., an insurance handbook, loss adjustment and business indicator manuals, etc.)
      • Leveraging OpenAI embeddings and LLMs through Azure OpenAI Service along with Azure Cognitive Services for search and summarization with citations
      • Developing a similar interface in the Iris-Azure environment with a local LLM (Llama-2-7B-chat) and embedding model (all-MiniLM-L6-v2) to compare responses
      Outcomes

      Underwriters significantly streamlined the activities needed to ensure that policy constructs align with applicable policies and procedures and for potential compliance issues in complex cases.

      The linguistic search and summarization capabilities of the OpenAI GPT 3.5-Turbo LLM (170 bn parameters) were found to be impressive. Notably, the local LLM (Llama-2-7B-chat), with much fewer parameters (7 bn), also produced acceptable results for this use case.

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      How developer portals help you win in the API economy

      How to win in the API economy with API Developer Portals

      In an increasingly API-driven economy, an all-inclusive API Developer Portal can differentiate an enterprise from its competitors.




        The evolution and adoption of enterprise digital transformation have made APIs critical for integration within and across enterprises as well as for product/service innovation. As APIs grow in scale and complexity, establishing a developer portal would significantly ease the process of their roll-out and adoption. This perspective paper explores the significance of an API Developer Portal in the modern digital landscape driving the API economy.

        A Developer Portal makes it easier to understand APIs, reduces integration time, and supports developers in training and resolving API-related issues. This provides significant business value by improving agility and enhancing customer experience. With the help of a Portal, enterprises can efficiently publish and consume APIs and enable their integration with incremental API versions. This will ensure benefit from all digital investments.

        In an increasingly API-driven economy, an all-inclusive API Developer Portal can differentiate an enterprise from its competitors, help build trust with partners, and achieve long-term success. Depending on the API platforms being used, enterprises could adopt a built-in platform or develop a custom one. Developing a custom API Portal would be easy at the start. However, developing enhanced features would entail a significant investment of time and resources. Hence, to make the right decisions and succeed in the broader API implementation/integration journey, a well-thought-out approach is necessary.

        To learn more about the key drivers, components and features, implementation options and potential benefits of API Developer Portals, download the perspective paper here.

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          Automated financial analysis reduces manual effort

          BANKING

          Automated financial analysis reduces manual effort

          Analysts in a large North American bank's commercial lending and credit risk operations can source intelligent information across multiple documents.

          Client
          Commerical lending and credit risk units of large North American bank
          Goal
          Automated retrieval of information from multiple financial statements enabling data-driven insights and decision-making
          Tools and Technologies
          OpenAI API (GPT-3.5 Turbo), LlamaIndex, LangChain, PDF Reader
          Business Challenge

          A leading North American bank had large commercial lending and credit risk units. Analysts in those units typically refer to numerous sections in a financial statement, including balance sheets, cash flows, and income statements, supplemented by footnotes and leadership commentaries, to extract decision-making insights. Switching between multiple pages of different documents took a lot of work, making the analysis extra difficult.

          Solution

          Many tasks were automated using Gen AI tools. Our steps:

          • Ingest multiple URLs of financial statements
          • Convert these to text using the PDF Reader library
          • Build vector indices using LlamaIndex
          • Create text segments and corresponding vector embeddings using OpenAI’s API for storage in a multimodal vector database e.g., Deep Lake
          • Compose graphs of keyword indices for vector stores to combine data across documents
          • Break down complex queries into multiple searchable parts using LlamaIndex’s DecomposeQueryTransform library
          Outcomes

          The solution delivered impressive results in financial analysis, notably reducing manual efforts when multiple documents were involved. Since the approach is still largely linguistic in nature, considerable Prompt engineering may be required to generate accurate responses.

          Response limitations due to the lack of semantic awareness in Large Language Models (LLMs) may stir considerations about the usage of qualifying information in queries.

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          Next generation chatbot eases data access

          BROKERAGE & WEALTH

          Next generation chatbot eases data access

          Gen AI tools help users of retail brokerage trading platform obtain information related to specific needs and complex queries.

          Client
          Large U.S.-based Brokerage and Wealth Management Firm
          Goal
          Enable a large number of users to readily access summarized information contained in voluminous documents
          Tools and Technologies
          Google Dialogflow ES, Pinecone, Llamaindex, OpenAI API (GPT-3.5 Turbo)
          Business Challenge

          A large U.S.-based brokerage and wealth management firm has a large number of users for its retail trading platform that offers sophisticated trading capabilities. Although extensive information was documented in hundreds of pages of product and process manuals, it was difficult for users to access and understand information related to their specific needs (e.g., How is margin calculated? or What are Rolling Strategies? or Explain Beta Weighting).

          Solution

          Our Gen AI solution encompassed:

          • Building a user-friendly interactive chatbot using Dialogflow in Google Cloud
          • Ringfencing a knowledge corpus comprising specific documents to be searched against and summarized (e.g., 200-page product manual, website FAQ content)
          • Using a vector database to store vectors from the corpus and extract relevant context for user queries
          • Interfacing the vector database with OpenAI API to analyze vector-matched contexts and generate summarized responses
          Outcomes

          The OpenAI GPT-3.5 turbo LLM (170 bn parameters) delivered impressive linguistic search and summarization capabilities in dealing with information requests. Prompt engineering and training are crucial to secure those outcomes.

          In the case of a rich domain such as a trading platform, users may expect additional capabilities, such as:

          • API integration to support requests requiring retrieval of account/user specific information, and
          • Augmentation of linguistic approaches with semantics to deliver enhanced capabilities.
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          The state of Central Bank Digital Currency

          The state of Central Bank Digital Currency

          Innovations in digital currencies could redefine the concept of money and transform payments and banking systems.




            Central banking institutions have emerged as key players in the world of banking and money. They play a pivotal role in shaping economic and monetary policies, maintaining financial system stability, and overseeing currency issuance. A manifestation of the evolving interplay between central banks, money, and the forces that shape financial systems is the advent of Central Bank Digital Currency (CBDC). Many drivers have led central banks to explore CBDC: declining cash payments, the rise of digital payments and alternative currencies, and disruptive forces in the form of fin-tech innovations that continually reshape the payment landscape.

            Central banks are receptive towards recent technological advances and well-suited to the digital currency experiment, leveraging their inherent role of upholding the well-being of the monetary framework to innovate and facilitate a trustworthy and efficient monetary system.

            In 2023, 130 countries, representing 98% of global GDP, are known to be exploring a CBDC solution. Sixty-four of them are in an advanced phase of exploration (development, pilot, or launch), focused on lower costs for consumers and merchants, offline payments, robust security, and a higher level of privacy and transparency. Over 70% of the countries are evaluating digital ledger technology (DLT)-based solutions.  

            While still at a very nascent stage in terms of overall adoption for CBDC, the future of currency promises to be increasingly digital, supported by various innovations and maturation. CBDC has the potential to bring about a paradigm shift, particularly in the financial industry, redefining the way in which money, as we know it, exchanges hands.

            Read our perspective paper to learn more about CBDCs – the rationale for their existence, the factors driving their implementation, potential ramifications for the financial landscape, and challenges associated with their adoption.

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              Cloud Migration, Challenges and Solutions

              Cloud migration challenges and solutions

              Insights into the top challenges and their mitigations in the cloud journey.




                Selecting an appropriate path for an application or a portfolio of applications is one of the most critical decision points in a cloud journey. Assessing the nature and criticality of an existing application is usually the starting place. Another critical factor to consider is the implementation (migration) cost and time for each path to cloud. The four cloud adoption options are re-host, re-platform, re-factor and re-write in the order of increasing cost, effort, cloud benefits, and TCO reduction. Out of these, re-host usually does not involve code change and is relatively simple.

                Mapping cloud operating metrics into a 3x3 matrix is a good starting point on planning for a cloud journey. In this matrix, the cloud operating metrics would move to the right if they are critical for customer intelligence applications; that would be an X factor. Another critical dimension while planning cloud migration is identifying the interface dependencies between selected application(s) and others – both inbound and outbound. These could be synchronous, asynchronous or batch.

                Understanding the application architecture, its internal organization, and inter-dependencies are critical before migration. This can be a very complex and labor-intensive task if done manually and can be error prone. Not fully understanding the existing code can lead to issues related to transactions, data corruption, session handling, and performance.

                To read more on the top challenges and their mitigations in the cloud journey, download the perspective paper here.

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                  Containerized microservices optimize infrastructure

                  Containerized microservices optimize infrastructure

                  Migration of legacy interfaces from Oracle SOA to AWS EKS increases scalability, availability and maintainability and establishes a single version of truth across systems and functions for a global publishing house.




                    A leading global publishing house with significant operations in the U.S. had multiple Systems of Record (SOR) with a point-to-point integration between them and various operational and analytics marts built on legacy technologies. This led to the divergence of critical operational data across functions, delays in month-end and quarter-end processing as well as scalability and performance issues.

                    Iris Software’s team collaborated with the client to accelerate the migration of their legacy interfaces and services to AWS EKS through a phased approach that was best suited to meet the client’s need for a much faster deployment time to market. It involved:

                    • Defining the microservices and containerization technology stack for the migration of services.
                    • Developing containerized microservices using Spring Boot with externalized configuration to deploy into AWS EKS.
                    • Registering services hosted on AWS EKS with Kong API Gateway, enabling service discovery, auto-scaling and self-healing, as well as consumption of materialized views and response preparation.
                    • The migration from Oracle SOA to a loosely coupled set of capabilities with microservices architecture on AWS EKS enabled the delivery of a single version of truth to various consuming applications and channels, resulting in reduced operational issues and maintenance costs. It also ensures that the business can meet future demand spikes, minimize downtime, and maintain optimal performance.

                    Learn more by downloading the full success story here.

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                      API platform migration increases capacity by 6x

                      Platform migration increases API capacity by 6x

                      Migration of legacy API platform to Apigee and GCP and API productizing led to 6x higher capacity and 70% reduction in support tickets, enhancing business growth and customer satisfaction for a global logistics firm.




                        Our client, a leader in truck transportation and logistics services with more than 50,000 customers across 33 countries, had developed customer-facing APIs using earlier generations of API platforms. These APIs connected their transportation management system with several other critical systems such as GPS tracking, warehouse management, and real-time customer portals. Scalability and reliability issues were plaguing the client’s API management system due to the legacy infrastructure and increasing numbers of APIs, leading to poor customer satisfaction and decreased competitiveness.

                        The client sought a technology partner who could understand the complex business logic within the existing API structure and execute a seamless migration and modernization that would improve the performance and scalability of the APIs for its customers. A team of Apigee experts at Iris Software addressed the challenges with a comprehensive, customized four-step approach that consisted of:

                        1. Outlining the migration strategy to move 18+ APIs to a more robust API Gateway
                        2. Automating the migration from the legacy API Gateway to Apigee to ease customer transitions
                        3. Balancing internal system loads to increase scalability and throughput
                        4. Implementing Apigee analytics for improved traceability and faster mitigation of issues

                        Iris’ solution provided multi-market, multi-channel and multi-partner integration as well as other positive outcomes for the client:

                        • 70% reduction in support tickets related to shipment delays
                        • 6x increase in API throughput
                        • New revenue streams from the creation of four new API products

                        Learn more by downloading the full success story here.

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