DevOps

Powering AI-Driven Travel Experiences with Robust DevOps and AWS Infrastructure Provisioning and Support About the Customer The customer is a leading travel management company and an industry-innovator in integrating artificial intelligence into the traveler’s journey. As a forward-thinking organization within the travel tech space, they sought to revolutionize how users book trips through an advanced AI-powered Email Bot Customer Business Need The customer developed a sophisticated application using Large Language Models (LLMs). The bot allows users to simply email a request (e.g., “Fly from Hyderabad to New York next Monday”), and the system automatically fetches airport lists, selects optimal routes, books tickets, secures hotel rooms, and arranges ground transportation. However, the customer while moving from development to production faced significant challenges with: Infrastructure management: Unsustainable management of complex backend APIs and LLM models on AWS manually Deployment bottlenecks: Lack of a formalized CI/CD pipeline slowed down the release of new AI features. Environment consistency: Faced challenges with code behavior across the environments of development, testing, and production. Architectural diversity: Inefficient management of hybrid environment ranging from serverless AWS Lambda, containerized ECS, and traditional EC2 The Solution in detail Adroitent Solution: End-to-End DevOps & Cloud Infrastructure Modernization Adroitent partnered with the customer to architect and implement a mission-critical DevOps and AWS Infrastructure provisioning. Focused on automating the lifecycle of the AI application to ensure high availability and rapid scalability. Solution Overview Automated CI/CD Pipelines: Teams transitioned the deployment process to a fully automated Bitbucket Pipeline consisting of: Automated build: After code merge into the target branch in the Bitbucket repository, the pipeline automatically triggers the build and deployment process. Integration across environments: Enabled smooth and consistent deployments across key environments, including Development, Testing, and Production. Infrastructure as Code (IaC) with AWS CDK: To eliminate manual errors, the team leveraged AWS CDK (Cloud Development Kit) and CloudFormation to implement infrastructure as code, enabling: Fully Scripted Environments: Entire infrastructure was codified, allowing one-click deployment of complex environments. Version-Controlled Infrastructure: All AWS resources—from S3 buckets to networking components—were version-controlled, ensuring consistency, traceability, and repeatability across deployments. Optimized Hybrid Compute Architecture: AI solution was deployed using AWS services to balance performance and cost that consisted of: AWS ECS & EC2: For heavy-duty LLM processing and persistent backend services AWS Lambda: To handle serverless, event-driven tasks within the booking flow. AWS Step Functions: To orchestrate the complex multi-step booking logic (Flight -> Hotel -> Cab). AWS SageMaker: Utilized for training and managing the backend LLM models. Business ROI Faster Time-to-Market Enhanced operational efficiency Stronger governance & compliance Scalability & agility Tools & Technology Leveraged Cloud Platform: AWS (EC2, ECS, Lambda, S3, CloudWatch, SageMaker, Step Functions) DevOps & Automation: AWS CDK, CloudFormation, Bitbucket Pipelines Backend & AI: LLM Models, Python/Node.js APIs Business Outcomes Faster Time-to-Market: Deployment cycles were reduced from hours/days to minutes, enabling quicker feature releases and faster response to business needs. Improved deployment reliability: Automated, consistent deployments minimized manual errors, resulting in more stable releases and fewer production issues. Enhanced operational efficiency: Automation reduced manual effort, allowing teams to focus more on innovation and core development activities. Cost optimization: Lower operational overhead and reduced rework led to optimized infrastructure and support costs. Stronger governance & compliance: Version-controlled infrastructure ensured full traceability, auditability, and adherence to compliance standards. Scalability & agility: On-demand environment provisioning enabled rapid scaling and greater flexibility to support evolving business demands. Improved developer experience: Simplified, one-click deployments enhanced developer productivity and accelerated on boarding. Talk To Our Experts

Travel AI Platform: DevOps & AWS Automation | Adroitent Home/ Customer Stories/ Software Engineering Customer Story · Travel Tech · Software Engineering Powering AI-Driven Experiences for a Travel Agency with Robust DevOps and AWS Infrastructure Provisioning and Support The customer is a leading travel management company and an industry-innovator in integrating artificial intelligence into the traveler’s journey. As a forward-thinking organization within the travel tech space, they sought to revolutionize how users book trips through an advanced AI-powered Email Bot. Adroitent Case FileTravel TechAWS · DevOps · CI/CD3 min read The inflection pointFrom development to production The customer developed a sophisticated application using Large Language Models (LLMs). The bot allows users to simply email a request (e.g., “Fly from Hyderabad to New York next Monday”), and the system automatically fetches airport lists, selects optimal routes, books tickets, secures hotel rooms, and arranges ground transportation. However, while moving from development to production, the customer faced significant challenges with: Infrastructure management: Unsustainable management of complex backend APIs and LLM models on AWS manually Deployment bottlenecks: Lack of a formalized CI/CD pipeline slowed down the release of new AI features Environment consistency: Faced challenges with code behavior across the environments of development, testing, and production Architectural diversity: Inefficient management of hybrid environment ranging from serverless AWS Lambda, containerized ECS, and traditional EC2 instances Automate the entire lifecycle — for high availability and rapid scalability. The interventionEnd-to-End DevOps & Cloud Infrastructure Modernization Adroitent partnered with the customer to architect and implement a mission-critical DevOps and AWS Infrastructure provisioning — focused on automating the lifecycle of the AI application to ensure high availability and rapid scalability. Automated CI/CD Pipelines After code merge into the target branch, the pipeline automatically triggers the build and deployment process Enabled smooth and consistent deployments across Development, Testing and Production Infrastructure as Code (IaC) with AWS CDK AWS CDK and CloudFormation codified the entire infrastructure, allowing one-click deployment of complex environments All AWS resources — from S3 buckets to networking — version-controlled for consistency, traceability and repeatability Optimized Hybrid Compute Architecture ECS & EC2 for heavy-duty LLM processing; Lambda for serverless, event-driven booking tasks Step Functions orchestrate the Flight → Hotel → Cab logic; SageMaker trains and manages the LLM models The game changer Optimized hybrid compute on AWS AWS ECS & EC2 for heavy-duty LLM processing and persistent backend services AWS Lambda to handle serverless, event-driven tasks within the booking flow AWS Step Functions to orchestrate the complex multi-step booking logic (Flight → Hotel → Cab) AWS SageMaker for training and managing the backend LLM models ECSEC2LambdaStep FunctionsSageMakerS3CloudWatch By the numbers Faster time-to-market Improved reliability Stronger governance Scalability & agility By the numbers Faster time-to-market Improved reliability Stronger governance Scalability & agility The payoffFaster, reliable, scalable delivery Faster Time-to-Market Deployment cycles were reduced from hours/days to minutes, enabling quicker feature releases and faster response to business needs. Improved deployment reliability Automated, consistent deployments minimized manual errors, resulting in more stable releases and fewer production issues. Enhanced operational efficiency Automation reduced manual effort, allowing teams to focus more on innovation and core development activities. Cost optimization Lower operational overhead and reduced rework led to optimized infrastructure and support costs. Stronger governance & compliance Version-controlled infrastructure ensured full traceability, auditability, and adherence to compliance standards. Scalability & agility On-demand environment provisioning enabled rapid scaling and greater flexibility to support evolving business demands. Improved developer experience Simplified, one-click deployments enhanced developer productivity and accelerated onboarding. The stack behind itTools & technology AWS EC2AWS ECSAWS LambdaAWS S3CloudWatchSageMakerStep FunctionsAWS CDKCloudFormationBitbucket PipelinesLLM ModelsPython / Node.js APIs This engagement was delivered as part of Adroitent’s Software Engineering Services practice. Good to knowFrequently asked questions What is Infrastructure as Code (IaC)? Infrastructure as Code manages and provisions cloud resources through version-controlled definition files instead of manual setup. Tools such as AWS CDK, CloudFormation and Terraform let teams review, reproduce and one-click-deploy entire environments, improving consistency, traceability and speed. How does a CI/CD pipeline speed up software delivery? A CI/CD pipeline automatically builds, tests and deploys code whenever changes are merged. Removing manual steps shortens release cycles from days to minutes, reduces human error, and produces consistent, reliable deployments across development, testing and production. When should you use serverless (AWS Lambda) versus containers (ECS/EC2)? Serverless functions like AWS Lambda suit short, event-driven tasks with variable load and no servers to manage. Containers on ECS or EC2 suit long-running, resource-intensive or stateful workloads — such as heavy LLM inference — where you need persistent compute and control. How do you deploy and scale an LLM application on AWS? A common pattern combines SageMaker to train and host models, GPU-backed containers (ECS/EC2) for inference, Lambda for lightweight tasks, and Step Functions to orchestrate multi-step workflows — with autoscaling and Infrastructure as Code to handle demand and keep environments consistent. What are the main benefits of DevOps automation? DevOps automation streamlines building, testing, deploying and monitoring software. It shortens release cycles, reduces manual errors, improves reliability and governance, and frees engineers to focus on new features rather than repetitive operational work. Make your platform deploy-in-minutes. Automate your cloud lifecycle — from code to production, at scale. Start a conversation Explore Software Engineering Keep reading Related customer stories Software engineering Driving digital excellence for a North American automotive leader Read story Software engineering Modernization of healthcare applications in the US Read story Software engineering End-to-end support for a North American AutoTech leader Read story More from Adroitent: all customer stories · insights & blog · adroitent.ai