Delivering intelligence with speed and scale

Delivering intelligence with speed and scale

Data Science Engineering and Data & ML Ops are key to enable scaling of the intelligence part of the data monetization lifecycle in cloud.




    Delivering intelligence with speed and scale

    Do you trust your data?

    Data driven organizations are ensuring that their Data assets are cataloged and a lineage is established to fully derive value out of their data assets.

    Through a number of digital initiatives over the past decade, organizations have collated a lot of information. In addition to structured data, they are collating unstructured and semi-structured formats, e.g., digitized contracts and audio/video of customer interactions. The opportunities to apply established and emerging AI/ML techniques and models to this wide variety of information and derive intelligence and enhanced insights have significantly increased.

    Cloud and the evolving technologies around Data Engineering, Data & ML Ops, Data Science, and AI/ML (e.g., Generative AI) offer a significant opportunity to overcome the limitations and deliver intelligence with speed and at scale. While the number and sophistication of AI/ML models available have increased and become easier to deploy, train/tune, and use, they need information at scale to be transformed to features. Delivering intelligence in scale would require more than just data lakes and lake-houses. It also requires the overall ability to support multiple modeling/data science teams working on multiple problems/opportunities concurrently. Data Science Engineering and Data & ML Ops are key to enable scaling of the intelligence part of the data monetization lifecycle. Teams need to understand data science/modeling lifecycles to effectively scale intelligence.

    In conclusion, organizations demand intelligence in scale and at speed. Emerging technologies like Generative AI demand more powerful infrastructure (e.g., GPU farms). Cloud technologies and services enable these. With support for Python across the intelligence lifecycle, it has become easier to bring together data engineering and data science teams that are easier to provision and use in cloud.

    To know more about the benefits, challenges, and best practices for scaling various stages of deriving intelligence from data on cloud environments, read the perspective paper here.

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      How Low-code Empowers Mission-critical End Users

      How Low-code Empowers Mission-critical End Users

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




        How Low-code Empowers Mission-critical End Users

        Do you trust your data?

        Data driven organizations are ensuring that their Data assets are cataloged and a lineage is established to fully derive value out of their data assets.

        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

          Gen AI For Software Engineers

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          AI-powered summarization boosts compliance workflow

          INSURANCE

          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.

          Gen AI For Software Engineers

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          Comprehensive solutions with seamless system integration

          Agile, feature-rich and scalable API platforms to stay competitive, secure and compliant

          Comprehensive solutions with seamless system integration

          Agile, feature-rich and scalable API platforms to stay competitive, secure and compliant

          Service Offerings

          Value We Provide

          Enabling Secure, Scalable and Resilient Operations

          Our expertise and domain knowledge enable us to create agile platforms that deliver value across multiple parameters

          Versatile Systems with Seamless Optimization

          Our tech teams create holistic solutions that can be adapted across business volumes, regions and environments

          Flexibility to Maximize User Experience

          Our nimble approach allows clients to customize user features from smooth customer on-boarding to intuitive dashboards and complete life-cycle management

          Success Stories

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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.




              How developer portals help you win in the API economy

              Do you trust your data?

              Data driven organizations are ensuring that their Data assets are cataloged and a lineage is established to fully derive value out of their data assets.

              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.

              Download Perspective Paper




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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.

                Gen AI For Software Engineers

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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 client 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.

                Gen AI For Software Engineers

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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.




                  The State of Central Bank Digital Currency

                  Do you trust your data?

                  Data driven organizations are ensuring that their Data assets are cataloged and a lineage is established to fully derive value out of their data assets.

                  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.

                  Download Perspective Paper




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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.




                      Cloud Migration, Challenges and Solutions

                      Do you trust your data?

                      Data driven organizations are ensuring that their Data assets are cataloged and a lineage is established to fully derive value out of their data assets.

                      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.

                      Download Perspective Paper




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