Automated CI/CD pipelines
- Fully automated Bitbucket Pipeline, triggered on merge to the target branch
- Smooth, consistent deployments across Development, Testing and Production
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Customer Story · Cloud & DevOps
Travel, automated: DevOps + AWS for an AI booking bot
How Adroitent gave a travel-tech innovator a fully automated DevOps and AWS foundation — turning an LLM-powered email booking bot from manual deployments into a scalable, one-click cloud platform.
Adroitent Case FileTravel TechAWS · DevOps · CI/CD3 min read
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01
The inflection point
Success outgrew manual infrastructure
The customer built a sophisticated LLM-powered email booking bot — email a request like “Fly from Hyderabad to New York next Monday” and it books flights, hotels and ground transport. Moving from development to production exposed hard infrastructure limits.
- 01Infrastructure management: managing complex backend APIs and LLM models on AWS manually was unsustainable
- 02Deployment bottlenecks: no formalized CI/CD pipeline slowed the release of new AI features
- 03Environment consistency: code behaved differently across development, testing and production
- 04Architectural diversity: inefficient management of a hybrid estate spanning AWS Lambda, ECS and EC2
Automate the entire lifecycle — for high availability and rapid scale.
02
The intervention
End-to-end DevOps & cloud modernization
Adroitent architected and implemented a mission-critical DevOps and AWS infrastructure, automating the AI application's lifecycle for high availability and rapid scalability. (hover a card)
Infrastructure as Code
- AWS CDK + CloudFormation codified the entire infrastructure for one-click deployment
- Every resource — S3 to networking — version-controlled for consistency and traceability
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Optimized hybrid compute
- ECS & EC2 for heavy LLM processing; Lambda for event-driven booking tasks
- Step Functions orchestrate Flight → Hotel → Cab; SageMaker trains and manages the models
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The game changer
Serverless-to-GPU hybrid compute on AWS
- AWS ECS & EC2 for heavy-duty LLM processing and persistent backend services
- AWS Lambda for serverless, event-driven tasks within the booking flow
- AWS Step Functions to orchestrate multi-step booking logic (Flight → Hotel → Cab)
- AWS SageMaker to train and manage the backend LLM models
03
The payoff
Ship in minutes, scale on demand
Faster time-to-market
Enhanced operational efficiency
Stronger governance & compliance
Scalability & agility
In detail — tap to open
Deployment cycles dropped from hours or days to minutes, enabling quicker feature releases and faster response to business needs.
Automated, consistent deployments minimized manual errors, resulting in more stable releases and fewer production issues.
Automation reduced manual effort, freeing teams to focus on innovation and core development.
Lower operational overhead and reduced rework optimized infrastructure and support costs.
Version-controlled infrastructure ensured full traceability, auditability and adherence to compliance standards.
On-demand environment provisioning enabled rapid scaling and greater flexibility for evolving demands.
Simplified, one-click deployments boosted developer productivity and accelerated onboarding.
The stack behind it
Tools & technology
AWS EC2
AWS ECS
AWS Lambda
AWS S3
CloudWatch
SageMaker
Step Functions
AWS CDK
CloudFormation
Bitbucket Pipelines
LLM Models
Python / Node.js APIs
FAQ
Frequently asked questions
Infrastructure as Code manages and provisions computing resources through machine-readable definition files instead of manual setup. Tools like AWS CDK, CloudFormation and Terraform let teams version, review and reproduce entire environments consistently, cutting errors and enabling one-click, repeatable deployments.
A CI/CD pipeline automatically builds, tests and deploys code whenever changes are merged. By removing manual steps it shortens release cycles from days to minutes, catches problems earlier, and delivers more frequent, more reliable releases with less risk.
Serverless functions like AWS Lambda suit short, event-driven tasks with variable load and no servers to manage. Containers on ECS or EC2 fit long-running, resource-intensive or stateful workloads — such as heavy AI/LLM processing — where you need more control over compute and memory.
A common pattern uses SageMaker to train and host models, containers (ECS/EC2 or GPU instances) for inference-heavy work, Lambda for lightweight tasks, and Step Functions to orchestrate multi-step logic — all provisioned via Infrastructure as Code so the stack scales on demand.
DevOps automation delivers faster time-to-market, more reliable and repeatable deployments, lower operational cost, stronger governance through version-controlled infrastructure, and easier scalability — while freeing engineers to build features instead of managing environments.
Make your platform deploy-in-minutes.
Automate your cloud lifecycle — from code to production, at scale.
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