69 tools with this tag
← Back to LLMOps DatabaseAxfood / Harman
Two enterprise customers, Axfood (a Swedish grocery retailer) and Harman International (an audio technology company), shared their approaches to using AI and AWS services in conjunction with their SAP environments. Axfood leveraged traditional machine learning for over 100 production forecasting models to optimize inventory, assortment planning, and e-commerce personalization, while also experimenting with generative AI for design tools and employee productivity. Harman International faced a critical challenge during their S/4HANA migration: documenting 30,000 custom ABAP objects that had accumulated over 25 years with poor documentation. Manual documentation by 12 consultants was projected to take 15 months at high cost with inconsistent results. By adopting AWS Bedrock and Amazon Q Developer with Anthropic Claude models, Harman reduced the timeline from 15 months to 2 months, improved speed by 6-7x, cut costs by over 70%, and achieved structured, consistent documentation that was understandable by both business and technical stakeholders.
Snorkel
Snorkel developed a specialized benchmark dataset for evaluating AI agents in insurance underwriting, leveraging their expert network of Chartered Property and Casualty Underwriters (CPCUs). The benchmark simulates an AI copilot that assists junior underwriters by reasoning over proprietary knowledge, using multiple tools including databases and underwriting guidelines, and engaging in multi-turn conversations. The evaluation revealed significant performance variations across frontier models (single digits to ~80% accuracy), with notable error modes including tool use failures (36% of conversations) and hallucinations from pretrained domain knowledge, particularly from OpenAI models which hallucinated non-existent insurance products 15-45% of the time.
Commonwealth Bank of Australia
Commonwealth Bank of Australia (CBA) partnered with AWS ProServe to modernize legacy Windows 2012 applications and migrate them to cloud at scale. Facing challenges with time-consuming manual processes, missing documentation, and significant technical debt, CBA developed "Lumos," an internal multi-agent AI platform that orchestrates the entire modernization lifecycle—from application analysis and design through code transformation, testing, deployment, and operations. By integrating AI agents with deterministic engines and AWS services (Bedrock, ECS, OpenSearch, etc.), CBA increased their modernization velocity from 10 applications per year to 20-30 applications per quarter, while maintaining security, compliance, and quality standards through human-in-the-loop validation and multi-agent review processes.
Western Union / Unum
Western Union and Unum partnered with AWS and Accenture/Pega to modernize their mainframe-based legacy systems using AWS Transform, an agentic AI service designed for large-scale migration and modernization. Western Union aimed to modernize its 35-year-old money order platform to support growth targets and improve back-office operations, while Unum sought to streamline Colonial Life claims processing. The solution leveraged composable agentic AI frameworks where multiple specialized agents (AWS Transform agents, Accenture industry knowledge agents, and Pega Blueprint agents) worked together through orchestration layers. Results included converting 2.5 million lines of COBOL code in approximately 1.5 hours, reducing project timelines from 3+ months to 6 weeks for Western Union, and achieving a complete COBOL-to-cloud migration with testable applications in 3 months for Unum (compared to previous 7-year, $25 million estimates), while eliminating 7,000 annual manual hours in claims management.
Thomson Reuters
Thomson Reuters evolved their AI assistant strategy from helpfulness-focused tools to productive agentic systems that make judgments and produce output in high-stakes legal, tax, and compliance environments. They developed a framework treating agency as adjustable dials (autonomy, context, memory, coordination) rather than binary states, enabling them to decompose legacy applications into tools that AI agents can leverage. Their solutions include end-to-end tax return generation from source documents and comprehensive legal research systems that utilize their 1.5+ terabytes of proprietary content, with rigorous evaluation processes to handle the inherent variability in expert human judgment.
Thomson Reuters
Thomson Reuters' Platform Engineering team transformed their manual, labor-intensive operational processes into an automated agentic system to address challenges in providing self-service cloud infrastructure and enablement services at scale. Using Amazon Bedrock AgentCore as the foundational orchestration layer, they built "Aether," a custom multi-agent system featuring specialized agents for cloud account provisioning, database patching, network configuration, and architecture review, coordinated through a central orchestrator agent. The solution delivered a 15-fold productivity gain, achieved 70% automation rate at launch, and freed engineering teams from repetitive tasks to focus on higher-value innovation work while maintaining security and compliance standards through human-in-the-loop validation.
RHI Magnesita
RHI Magnesita, facing $3 million in annual losses due to human errors in order processing, implemented an AI agent to assist their Customer Service Representatives (CSRs). The solution, developed with IT-Tomatic, focuses on error reduction, standardization of processes, and enhanced training. The AI system serves as an operating system for CSRs, consolidating information from multiple sources and providing intelligent validation of orders. Early results show improved training efficiency, standardized processes, and the transformation of entry-level CSR positions into hybrid analyst roles.
42Q
42Q, a cloud-based Manufacturing Execution System (MES) provider, implemented an intelligent chatbot named Arthur to address the complexity of their system and improve user experience. The solution uses RAG and AWS Bedrock to combine documentation, training videos, and live production data, enabling users to query system functionality and real-time manufacturing data in natural language. The implementation showed significant improvements in user response times and system understanding, while maintaining data security within AWS infrastructure.
Geminus
Geminus addresses the challenge of optimizing large industrial machinery operations by combining traditional ML models with high-fidelity simulations to create fast, trustworthy digital twins. Their solution reduces model development time from 24 months to just days, while building operator trust through probabilistic approaches and uncertainty bounds. The system provides optimization advice through existing control systems, ensuring safety and reliability while significantly improving machine performance.
UCLA
UCLA Anderson School of Management partnered with Kindle to address the challenge of helping MBA students navigate their intensive two-year program more effectively. Students were overwhelmed with coursework, career decisions, club activities, and internship searches, receiving extensive information without clear guidance. The solution involved digitizing over 2 million paper records and building an AI-powered application that provides personalized, prescriptive roadmaps for students based on their career goals. The system integrates data from multiple sources including student records, career placement systems, clubs, and course catalogs to recommend specific courses, internships, clubs, and target companies. The project took approximately 8 months (December 2023 to August 2024) and demonstrates how educational institutions can leverage agentic AI frameworks to deliver better student experiences while maintaining data security and privacy standards.
Thomson Reuters
Thomson Reuters faced the challenge of modernizing over 400 legacy .NET Framework applications comprising more than 500 million lines of code, which were running on costly Windows servers and slowing down innovation. By adopting AWS Transform for .NET during its beta phase, the company leveraged agentic AI capabilities powered by Amazon Bedrock LLMs with deep .NET expertise to automate the analysis, dependency mapping, code transformation, and validation process. This approach accelerated their modernization from months of planning to weeks of execution, enabling them to transform over 1.5 million lines of code per month while running 10 parallel modernization projects. The solution not only promised substantial cost savings by migrating to Linux containers and Graviton instances but also freed developers from maintaining legacy systems to focus on delivering customer value.
Slack
Slack faced the challenge of migrating 15,500 Enzyme test cases to React Testing Library to enable upgrading to React 18, an effort estimated at over 10,000 engineering hours across 150+ developers. The team developed an innovative hybrid approach combining Abstract Syntax Tree (AST) transformations with Large Language Models (LLMs), specifically Claude 2.1, to automate the conversion process. The solution involved a sophisticated pipeline that collected context including DOM trees, performed partial AST conversions with annotations, and leveraged LLMs to handle complex cases that traditional codemods couldn't address. This hybrid approach achieved an 80% success rate for automated conversions and saved developers 22% of their migration time, ultimately enabling the complete migration by May 2024.
Swisscom
Swisscom, Switzerland's leading telecommunications provider, developed a Network Assistant using Amazon Bedrock to address the challenge of network engineers spending over 10% of their time manually gathering and analyzing data from multiple sources. The solution implements a multi-agent RAG architecture with specialized agents for documentation management and calculations, combined with an ETL pipeline using AWS services. The system is projected to reduce routine data retrieval and analysis time by 10%, saving approximately 200 hours per engineer annually while maintaining strict data security and sovereignty requirements for the telecommunications sector.
Wix
Wix developed AirBot, an AI-powered Slack agent to address the operational burden of managing over 3,500 Apache Airflow pipelines processing 4 billion daily HTTP transactions across a 7 petabyte data lake. The traditional manual debugging process required engineers to act as "human error parsers," navigating multiple distributed systems (Airflow, Spark, Kubernetes) and spending approximately 45 minutes per incident to identify root causes. AirBot leverages LLMs (GPT-4o Mini and Claude 4.5 Opus) in a Chain of Thought architecture to automatically investigate failures, generate diagnostic reports, create pull requests with fixes, and route alerts to appropriate team owners. The system achieved measurable impact by saving approximately 675 engineering hours per month (equivalent to 4 full-time engineers), generating 180 candidate pull requests with a 15% fully automated fix rate, and reducing debugging time by at least 15 minutes per incident while maintaining cost efficiency at $0.30 per AI interaction.
Palo Alto Networks
Palo Alto Networks' Device Security team faced challenges with reactively processing over 200 million daily service and application log entries, resulting in delayed response times to critical production issues. In partnership with AWS Generative AI Innovation Center, they developed an automated log classification pipeline powered by Amazon Bedrock using Anthropic's Claude Haiku model and Amazon Titan Text Embeddings. The solution achieved 95% precision in detecting production issues while reducing incident response times by 83%, transforming reactive log monitoring into proactive issue detection through intelligent caching, context-aware classification, and dynamic few-shot learning.
Various
The researchers present aCLAr (Demonstrate, Execute, Validate framework), a system that uses multimodal foundation models to automate enterprise workflows, particularly in healthcare settings. The system addresses limitations of traditional RPA by enabling passive learning from demonstrations, human-like UI navigation, and self-monitoring capabilities. They successfully demonstrated the system automating a real healthcare workflow in Epic EHR, showing how foundation models can be leveraged for complex enterprise automation without requiring API integration.
Various
A panel discussion featuring leaders from Bank of America, NVIDIA, Microsoft, and IBM discussing best practices for deploying and scaling LLM systems in enterprise environments. The discussion covers key aspects of LLMOps including business alignment, production deployment, data management, monitoring, and responsible AI considerations. The panelists share insights on the evolution from traditional ML deployments to LLM systems, highlighting unique challenges around testing, governance, and the increasing importance of retrieval and agent-based architectures.
Dust.tt
Dust.tt evolved from a developer framework competitor to LangChain into a horizontal enterprise platform for deploying AI agents, achieving remarkable 88% daily active user rates in some deployments. The company focuses on building robust infrastructure for agent deployment, maintaining its own integrations with enterprise systems like Notion and Slack, while making agent creation accessible to non-technical users through careful UX design and abstraction of technical complexities.
Untold Studios
Untold Studios developed an AI assistant integrated into Slack to help their visual effects artists access internal resources and tools more efficiently. Using Amazon Bedrock with Claude 3.5 Sonnet and a serverless architecture, they created a natural language interface that handles 120 queries per day, reducing information search time from minutes to seconds while maintaining strict data security. The solution combines RAG capabilities with function calling to access multiple knowledge bases and internal systems, significantly reducing the support team's workload.
ADP
ADP, a major HR and payroll services provider, is developing ADP Assist, a generative AI initiative to make their platforms more interactive and user-friendly while maintaining security and quality. They're implementing a comprehensive AI strategy through their "One AI" and "One Data" platforms, partnering with Databricks to address key challenges in quality assurance, IP protection, data structuring, and cost control. The solution employs RAG and various MLOps tools to ensure reliable, secure, and cost-effective AI deployment across their global operations serving over 41 million workers.
Various
A comprehensive overview of how enterprises are implementing LLMOps platforms, drawing from DevOps principles and experiences. The case study explores the evolution from initial AI adoption to scaling across teams, emphasizing the importance of platform teams, enablement, and governance. It highlights the challenges of testing, model management, and developer experience while providing practical insights into building robust AI infrastructure that can support multiple teams within an organization.
Windsurf
Codeium's journey in building their AI-powered development tools showcases how investing early in enterprise-ready infrastructure, including containerization, security, and comprehensive deployment options, enabled them to scale from individual developers to large enterprise customers. Their "go slow to go fast" approach in building proprietary infrastructure for code completion, retrieval, and agent-based development culminated in Windsurf IDE, demonstrating how thoughtful early architectural decisions can create a more robust foundation for AI tools in production.
WEX
WEX, a global commerce platform processing over $230 billion in transactions annually, built a production agentic AI system called "Chat GTS" to address their 40,000+ annual IT support requests. The company's Global Technology Services team developed specialized agents using AWS Bedrock and Agent Core Runtime to automate repetitive operational tasks, including network troubleshooting and autonomous EBS volume management. Starting with Q&A capabilities, they evolved into event-driven agents that can autonomously respond to CloudWatch alerts, execute remediation playbooks via SSM documents exposed as MCP tools, and maintain infrastructure drift through automated pull requests. The system went from pilot to production in under 3 months, now serving over 2,000 internal users, with multi-agent architectures handling both user-initiated chat interactions and autonomous incident response workflows.
Slack
Slack implemented AI features by developing a secure architecture that ensures customer data privacy and compliance. They used AWS SageMaker to host LLMs in their VPC, implemented RAG instead of fine-tuning models, and maintained strict data access controls. The solution resulted in 90% of AI-adopting users reporting increased productivity while maintaining enterprise-grade security and compliance requirements.
DTDC
DTDC, India's leading integrated express logistics provider, transformed their rigid logistics assistant DIVA into DIVA 2.0, a conversational AI agent powered by Amazon Bedrock, to handle over 400,000 monthly customer queries. The solution addressed limitations of their existing guided workflow system by implementing Amazon Bedrock Agents, Knowledge Bases, and API integrations to enable natural language conversations for tracking, serviceability, and pricing inquiries. The deployment resulted in 93% response accuracy and reduced customer support team workload by 51.4%, while providing real-time insights through an integrated dashboard for continuous improvement.
eBay
eBay tackled the challenge of incorporating LLMs into their e-commerce platform by developing e-Llama, a domain-adapted version of Llama 3.1. Through continued pre-training on a mix of e-commerce and general domain data, they created 8B and 70B parameter models that achieved 25% improvement in e-commerce tasks while maintaining strong general performance. The training was completed efficiently using 480 NVIDIA H100 GPUs and resulted in production-ready models aligned with human feedback and safety requirements.
Articul8
Articul8 developed a generative AI platform to address enterprise challenges in manufacturing and supply chain management, particularly for a European automotive manufacturer. The platform combines public AI models with domain-specific intelligence and proprietary data to create a comprehensive knowledge graph from vast amounts of unstructured data. The solution reduced incident response time from 90 seconds to 30 seconds (3x improvement) and enabled automated root cause analysis for manufacturing defects, helping experts disseminate daily incidents and optimize production processes that previously required manual analysis by experienced engineers.
Uber
Uber developed DragonCrawl, an innovative AI-powered mobile testing system that uses a small language model (110M parameters) to automate app testing across multiple languages and cities. The system addressed critical challenges in mobile testing, including high maintenance costs and scalability issues across Uber's global operations. Using an MPNet-based architecture with a retriever-ranker approach, DragonCrawl achieved 99%+ stability in production, successfully operated in 85 out of 89 tested cities, and demonstrated remarkable adaptability to UI changes without requiring manual updates. The system proved particularly valuable by blocking ten high-priority bugs from reaching customers while significantly reducing developer maintenance time. Most notably, DragonCrawl exhibited human-like problem-solving behaviors, such as retrying failed operations and implementing creative solutions like app restarts to overcome temporary issues.
Factory
Factory.ai built an enterprise-focused autonomous software engineering platform using AI "droids" that can handle complex coding tasks independently. The founders met at a LangChain hackathon and developed a browser-based system that allows delegation rather than collaboration, enabling developers to assign tasks to AI agents that can work across entire codebases, integrate with enterprise tools, and complete large-scale migrations. Their approach focuses on enterprise customers with legacy codebases, achieving dramatic results like reducing 4-month migration projects to 3.5 days, while maintaining cost efficiency through intelligent retrieval rather than relying on large context windows.
Barclays
A senior leader in industry discusses the key challenges and opportunities in deploying LLMs at enterprise scale, highlighting the differences between traditional MLOps and LLMOps. The presentation covers critical aspects including cost management, infrastructure needs, team structures, and organizational adaptation required for successful LLM deployment, while emphasizing the importance of leveraging existing MLOps practices rather than completely reinventing the wheel.
IBM
IBM's Watson X platform addresses enterprise LLMOps challenges by providing a comprehensive solution for model access, deployment, and customization. The platform offers both open-source and proprietary models, focusing on specialized use cases like banking and insurance, while emphasizing API optimization for LLM interactions and robust evaluation capabilities. The case study highlights how enterprises are implementing LLMOps at scale with particular attention to data security, model evaluation, and efficient API design for LLM consumption.
Cisco
At Cisco, the challenge of integrating LLMs into enterprise-scale applications required developing new DevSecOps workflows and practices. The presentation explores how Cisco approached continuous delivery, monitoring, security, and on-call support for LLM-powered applications, showcasing their end-to-end model for LLMOps in a large enterprise environment.
Smartling
Smartling operates an enterprise-scale AI-first agentic translation delivery platform serving major corporations like Disney and IBM. The company addresses challenges around automation, centralization, compliance, brand consistency, and handling diverse content types across global markets. Their solution employs multi-step agentic workflows where different model functions validate each other's outputs, combining neural machine translation with large language models, RAG for accessing validated linguistic assets, sophisticated prompting, and automated post-editing for hyper-localization. The platform demonstrates measurable improvements in throughput (from 2,000 to 6,000-7,000 words per day), cost reduction (4-10x cheaper than human translation), and quality approaching 70% human parity for certain language pairs and content types, while maintaining enterprise requirements for repeatability, compliance, and brand voice consistency.
Marsh McLennan
Marsh McLennan, a global professional services firm, implemented a comprehensive LLM-based assistant solution reaching 87% of their 90,000 employees worldwide, processing 25 million requests annually. Initially focused on productivity enhancement through API access and RAG, they evolved their strategy from using out-of-the-box models to incorporating fine-tuned models for specific tasks, achieving better accuracy than GPT-4 while maintaining cost efficiency. The implementation has conservatively saved over a million hours annually across the organization.
Toyota
Toyota implemented a comprehensive LLMOps framework to address multiple production challenges, including battery manufacturing optimization, equipment maintenance, and knowledge management. The team developed a unified framework combining LangChain and LlamaIndex capabilities, with special attention to data ingestion pipelines, security, and multi-language support. Key applications include Battery Brain for manufacturing expertise, Gear Pal for equipment maintenance, and Project Cura for knowledge management, all showing significant operational improvements including reduced downtime and faster problem resolution.
Principal Financial
Principal Financial implemented Amazon Q Business to address challenges with scattered enterprise knowledge and inefficient search capabilities across multiple repositories. The solution integrated QnABot on AWS with Amazon Q Business to enable natural language querying of over 9,000 pages of work instructions. The implementation resulted in 84% accuracy in document retrieval, with 97% of queries receiving positive feedback and users reporting 50% reduction in some workloads. The project demonstrated successful scaling from proof-of-concept to enterprise-wide deployment while maintaining strict governance and security requirements.
Hitachi
Hitachi's journey in implementing AI across industrial applications showcases the evolution from traditional machine learning to advanced generative AI solutions. The case study highlights how they transformed from focused applications in maintenance, repair, and operations to a more comprehensive approach integrating LLMs, focusing particularly on reliability, small data scenarios, and domain expertise. Key implementations include repair recommendation systems for fleet management and fault tree extraction from manuals, demonstrating the practical challenges and solutions in industrial AI deployment.
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The U.S. federal government agencies are working to move AI applications from pilots to production, focusing on scalable and responsible deployment. The Department of Energy (DOE) has implemented Energy GPT using open models in their environment, while the Department of State is utilizing LLMs for diplomatic cable summarization. The U.S. Navy's Project AMMO showcases successful MLOps implementation, reducing model retraining time from six months to one week for underwater vehicle operations. Agencies are addressing challenges around budgeting, security compliance, and governance while ensuring user-friendly AI implementations.
Kantar Worldpanel
Kantar Worldpanel, a market research company, needed to modernize their product description matching system to better link paper receipt descriptions with product barcode names. They leveraged Databricks Mosaic AI to experiment with various LLMs (including Llama, Mistral, and GPT models) to generate high-quality training data, achieving 94% accuracy in matching product descriptions. This automated approach generated 120,000 training pairs in just hours, allowing them to fine-tune smaller models for production use while freeing up human resources for more complex tasks.
OpenAI
OpenAI's Forward Deployed Engineering (FDE) team, led by Colin Jarvis, embeds with enterprise customers to solve high-value problems using LLMs and deliver production-grade AI applications. The team focuses on problems worth tens of millions to billions in value, working with companies across industries including finance (Morgan Stanley), manufacturing (semiconductors, automotive), telecommunications (T-Mobile, Klarna), and others. By deeply understanding customer domains, building evaluation frameworks, implementing guardrails, and iterating with users over months, the FDE team achieves 20-50% efficiency improvements and high adoption rates (98% at Morgan Stanley). The approach emphasizes solving hard, novel problems from zero-to-one, extracting learnings into reusable products and frameworks (like Swarm and Agent Kit), then scaling solutions across the market while maintaining strategic focus on product development over services revenue.
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A comprehensive analysis of three enterprise GenAI implementations showcasing the journey from pilot to profit. The cases cover a top 10 automaker's use of GenAI for manufacturing maintenance, an aviation entertainment company's predictive maintenance system, and a telecom provider's sales automation solution. Each case study reveals critical "hidden levers" for successful GenAI deployment: adoption triggers, lean workflows, and revenue accelerators. The analysis demonstrates that while GenAI projects typically cost between $200K to $1M and take 15-18 months to achieve ROI, success requires careful attention to implementation details, user adoption, and business process integration.
NTT Data
An international infrastructure company partnered with NTT Data to evaluate whether GenAI could improve their work order management system that handles 500,000+ annual maintenance requests. The POC focused on automating classification, urgency assessment, and special handling requirements identification. Using a privately hosted LLM with company-specific knowledge base, the solution demonstrated improved accuracy and consistency in work order processing compared to the manual approach, while providing transparent reasoning for classifications.
Agoda
Agoda integrated GPT into their CI/CD pipeline to automate SQL stored procedure optimization, addressing a significant operational bottleneck where database developers were spending 366 man-days annually on manual optimization tasks. The system provides automated analysis and suggestions for query improvements, index recommendations, and performance optimizations, leading to reduced manual review time and improved merge request processing. While achieving approximately 25% accuracy, the solution demonstrates practical benefits in streamlining database development workflows despite some limitations in handling complex stored procedures.
Accenture
Accenture's Industry X division conducted extensive experiments with generative AI in manufacturing settings throughout 2023. They developed and validated nine key use cases including operations twins, virtual mentors, test case generation, and technical documentation automation. The implementations showed significant efficiency gains (40-50% effort reduction in some cases) while maintaining a human-in-the-loop approach. The study emphasized the importance of using domain-specific data, avoiding generic knowledge management solutions, and implementing multi-agent orchestrated solutions rather than standalone models.
Microsoft
A case study detailing Microsoft's experience implementing LLMOps in a restricted network environment using Azure Machine Learning. The team faced challenges with long-running evaluations (6+ hours) and network restrictions, developing solutions including opt-out mechanisms for lengthy evaluations, implementing Git Flow for controlled releases, and establishing a comprehensive CI/CE/CD pipeline. Their approach balanced the needs of data scientists, engineers, and platform teams while maintaining security and evaluation quality.
Anthropic
Anthropic faced the challenge of managing an explosion of LLM-powered services and integrations across their organization, leading to duplicated functionality and integration chaos. They solved this by implementing a standardized MCP (Model Context Protocol) gateway that provides a single point of entry for all LLM integrations, handling authentication, credential management, and routing to both internal and external services. This approach reduced engineering overhead, improved security by centralizing credential management, and created a "pit of success" where doing the right thing became the easiest thing to do for their engineering teams.
Mendix
Mendix, a low-code platform provider, faced the challenge of integrating advanced generative AI capabilities into their development environment while maintaining security and scalability. They implemented Amazon Bedrock to provide their customers with seamless access to various AI models, enabling features like text generation, summarization, and multimodal image generation. The solution included custom model training, robust security measures through AWS services, and cost-effective model selection capabilities.
CommBank
Commonwealth Bank of Australia (CBA), Australia's largest bank serving 17.5 million customers, faced the challenge of modernizing decades of rich data spread across hundreds of on-premise source systems that lacked interoperability and couldn't scale for AI workloads. In partnership with HCL Tech and AWS, CBA migrated 61,000 on-premise data pipelines (equivalent to 10 petabytes of data) to an AWS-based data mesh ecosystem in 9 months. The solution leveraged AI and generative AI to transform code, check for errors, and test outputs with 100% accuracy reconciliation, conducting 229,000 tests across the migration. This enabled CBA to establish a federated data architecture called CommBank.data that empowers 40 lines of business with self-service data access while maintaining strict governance, positioning the bank for AI-driven innovation at scale.
Five Sigma
The given text appears to be a PDF document with binary/encoded content that needs to be processed and analyzed. The case involves handling PDF streams, filters, and document structure, which could benefit from LLM-based processing for content extraction and understanding.
First Orion
First Orion, a telecom software company, implemented Amazon Q to address the challenge of siloed operational data across multiple services. They created a centralized solution that allows cloud operators to interact with various data sources (S3, web content, Confluence) and service platforms (ServiceNow, Jira, Zendesk) through natural language queries. The solution not only provides information access but also enables automated ticket creation and management, significantly streamlining their cloud operations workflow.
Capgemini
Capgemini developed an accelerator called "amplifier" that transforms automotive software development by using LLMs deployed on AWS Bedrock to convert whiteboard sketches into structured requirements and test cases. The solution addresses the traditionally lengthy automotive development cycle by enabling rapid requirement generation, virtual testing, and scalable simulation environments. This approach reduces development time from weeks to hours while maintaining necessary safety and regulatory compliance, effectively bringing cloud-native development speeds to automotive software development.
Doordash
DoorDash implemented two major LLM-powered features during their 2025 summer intern program: a voice AI assistant for verifying restaurant hours and personalized alcohol recommendations with carousel generation. The voice assistant replaced rigid touch-tone phone systems with natural language conversations, allowing merchants to specify detailed hours information in advance while maintaining backward compatibility with legacy infrastructure through factory patterns and feature flags. The alcohol recommendation system leveraged LLMs to generate personalized product suggestions and engaging carousel titles using chain-of-thought prompting and a two-stage generation pipeline. Both systems were integrated into production using DoorDash's existing frameworks, with the voice assistant achieving structured data extraction through prompt engineering and webhook processing, while the recommendations carousel utilized the company's Carousel Serving Framework and Discovery SDK for rapid deployment.
Mercedes-Benz
Mercedes-Benz faced the challenge of modernizing their Global Ordering system, a critical mainframe application handling over 5 million lines of code that processes every vehicle order and production request across 150 countries. The company partnered with Capgemini, AWS, and Rocket Software to migrate this system from mainframe to cloud using a hybrid approach: replatforming the majority of the application while using agentic AI (GenRevive tool) to refactor specific components. The most notable success was transforming 1.3 million lines of COBOL code in their pricing service to Java in just a few months, achieving faster performance, reduced mainframe costs, and a successful production deployment with zero incidents at go-live.
Amazon (Alexa)
At Amazon Alexa, researchers tackled two key challenges in production NLP models: preventing performance degradation on common utterances during model updates and improving model robustness to input variations. They implemented positive congruent training to minimize negative prediction flips between model versions and used T5 models to generate synthetic training data variations, making the system more resilient to slight changes in user commands while maintaining consistent performance.
Intuit
Intuit, a global fintech platform, faced challenges scaling AI agents across their organization due to poor discoverability of Model Context Protocol (MCP) services, inconsistent security practices, and complex manual setup requirements. They built an MCP Marketplace, a centralized registry functioning as a package manager for AI capabilities, which standardizes MCP development through automated CI/CD pipelines for producers and provides one-click installation with enterprise-grade security for consumers. The platform leverages gRPC middleware for authentication, token management, and auditing, while collecting usage analytics to track adoption, service latency, and quality metrics, thereby democratizing secure context access across their developer organization.
Barclays
Discussion of MLOps practices and the evolution towards LLM integration at Barclays, focusing on the transition from traditional ML to GenAI workflows while maintaining production stability. The case study highlights the importance of balancing innovation with regulatory requirements in financial services, emphasizing ROI-driven development and the creation of reusable infrastructure components.
Cisco
Cisco developed an agentic AI platform leveraging LangChain to transform their customer experience operations across a 20,000-person organization managing $26 billion in recurring revenue. The solution combines multiple specialized agents with a supervisor architecture to handle complex workflows across customer adoption, renewals, and support processes. By integrating traditional machine learning models for predictions with LLMs for language processing, they achieved 95% accuracy in risk recommendations and reduced operational time by 20% in just three weeks of limited availability deployment, while automating 60% of their 1.6-1.8 million annual support cases.
Cisco
Cisco's Outshift incubation group developed a multi-agent AI system to address network change management failures in production environments. The solution combines a natural language interface, multiple specialized AI agents using ReAct reasoning loops, and a knowledge graph-based digital twin of production networks. The system integrates with ITSM tools like ServiceNow, automatically generates impact assessments and test plans, and executes validation tests using network configuration data stored in standardized schemas, significantly reducing tokens consumed and response times through fine-tuning approaches.
Totogi
Totogi, an AI company serving the telecommunications industry, faced challenges with traditional Business Support Systems (BSS) that required lengthy change request processing—typically taking 7 days and involving costly, specialized engineering talent. To address this, Totogi developed BSS Magic, which combines a comprehensive telco ontology with a multi-agent AI framework powered by Anthropic Claude models on Amazon Bedrock. The solution orchestrates five specialized AI agents (Business Analyst, Technical Architect, Developer, QA, and Tester) through AWS Step Functions and Lambda, automating the entire software development lifecycle from requirements analysis to code generation and testing. In collaboration with the AWS Generative AI Innovation Center, Totogi achieved significant results: reducing change request processing time from 7 days to a few hours, achieving 76% code coverage in automated testing, and delivering production-ready telecom-grade code with minimal human intervention.
ebay
eBay implemented a three-track approach to enhance developer productivity using LLMs: utilizing GitHub Copilot as a commercial offering, developing eBayCoder (a fine-tuned version of Code Llama 13B), and creating an internal GPT-powered knowledge base using RAG. The implementation showed significant improvements, including a 27% code acceptance rate with Copilot, enhanced software upkeep capabilities with eBayCoder, and increased efficiency in accessing internal documentation through their RAG system.
Vodafone
Vodafone implemented a comprehensive AI and GenAI strategy to transform their network operations, focusing on improving customer experience through better network management. They migrated from legacy OSS systems to a cloud-based infrastructure on Google Cloud Platform, integrating over 2 petabytes of network data with commercial and IT data. The initiative includes AI-powered network investment planning, automated incident management, and device analytics, resulting in significant operational efficiency improvements and a planned 50% reduction in OSS tools.
Windsurf
Windsurf began as a GPU virtualization company but pivoted in 2022 when they recognized the transformative potential of large language models. They developed an AI-powered development environment that evolved from a VS Code extension to a full-fledged IDE, incorporating advanced code understanding and generation capabilities. The product now serves hundreds of thousands of daily active users, including major enterprises, and has achieved significant success in automating software development tasks while maintaining high precision through sophisticated evaluation systems.
Harvey
Harvey, a legal AI platform, demonstrated their ability to rapidly integrate new AI capabilities by incorporating OpenAI's Deep Research feature into their production system within 12 hours of its API release. This achievement was enabled by their AI-native architecture featuring a modular Workflow Engine, composable AI building blocks, transparent "thinking states" for user visibility, and a culture of rapid prototyping using AI-assisted development tools. The case study showcases how purpose-built infrastructure and engineering practices can accelerate the deployment of complex AI features while maintaining enterprise-grade reliability and user transparency in legal workflows.
Articul8
Articul8, a generative AI company focused on domain-specific models (DSMs), faced challenges in training and deploying specialized LLMs across semiconductor, energy, and supply chain industries due to infrastructure complexity and computational requirements. They implemented Amazon SageMaker HyperPod to manage distributed training clusters with automated fault tolerance, achieving over 95% cluster utilization and 35% productivity improvements. The solution enabled them to reduce AI deployment time by 4x and total cost of ownership by 5x while successfully developing high-performing DSMs that outperform general-purpose LLMs by 2-3x in domain-specific tasks, with their A8-Semicon model achieving twice the accuracy of GPT-4o and Claude in Verilog code generation at 50-100x smaller model sizes.
Georgia-Pacific
Georgia-Pacific, a forest products manufacturing company with 30,000+ employees and 140+ facilities, deployed generative AI to address critical knowledge transfer challenges as experienced workers retire and new employees struggle with complex equipment. The company developed an "Operator Assistant" chatbot using AWS Bedrock, RAG architecture, and vector databases to provide real-time troubleshooting guidance to factory operators. Starting with a 6-8 week MVP deployment in December 2023, they scaled to 45 use cases across multiple facilities within 7-8 months, serving 500+ users daily with improved operational efficiency and reduced waste.
OSRAM
OSRAM, a century-old lighting technology company, faced challenges with preserving institutional knowledge amid workforce transitions and accessing scattered technical documentation across their manufacturing operations. They partnered with Adastra to implement an AI-powered chatbot solution using Amazon Bedrock and Claude, incorporating RAG and hybrid search approaches. The solution achieved over 85% accuracy in its initial deployment, with expectations to exceed 90%, successfully helping workers access critical operational information more efficiently across different departments.
Various
A panel discussion featuring Verizon, Anthropic, and Infosys executives sharing their experiences implementing LLM applications in telecommunications. The discussion covers multiple use cases including content generation, software development lifecycle enhancement, and customer service automation. Key challenges discussed include accuracy requirements, ROI justification, user adoption, and the need for proper evaluation frameworks when moving from proof of concept to production.
Honeycomb
Honeycomb shares candid insights from building Query Assistant, their natural language to query interface, revealing the complex reality behind LLM-powered product development. Key challenges included managing context window limitations with large schemas, dealing with LLM latency (2-15+ seconds per query), navigating prompt engineering without established best practices, balancing correctness with usefulness, addressing prompt injection vulnerabilities, and handling legal/compliance requirements. The article emphasizes that successful LLM implementation requires treating models as feature engines rather than standalone products, and argues that early access programs often fail to reveal real-world implementation challenges.
Doctolib
Doctolib is transforming their healthcare data platform from a reporting-focused system to an AI-enabled unified platform. The company is implementing a comprehensive LLMOps infrastructure as part of their new architecture, including features for model training, inference, and GenAI assistance for data exploration. The platform aims to support both traditional analytics and advanced AI capabilities while ensuring security, governance, and scalability for healthcare data.
