7 Targets goes Serverless in AWS Cloud to achieve AI-based Email Assistant with scalable and working solution for send and receive emails.
Client Information
7Targets AI Sales Assistant
7Targets is a SaaS B2B company in the Sales and Marketing domain focused on lead nurturing. It has a flagship product ‘AI Sales Assistant’ which has the capability to write emails and do follow-ups with the leads. As well, to read the received responses and act accordingly.
Customer challenge
Being an email-based AI Assistant and SAAS Product, the 7Targerts team were performing manual deployments in their environments. This made the development process highly prone to human errors, reduced efficiency and affected the code quality.
So 7Targets team was looking was faster, efficient upgrade and rollback process in the application deployments.
Specific project-level challenges were:
- Increased development and deployment time with increased backlog.
- Increased release cycles for production upgrades
- Process did not allow optimal utilization of resources
7targets AI Assistants:
- AI Sales assistants that help sales teams win more customers
- AI assistant automatically follows up with all your leads to convert more leads into Hot leads so that you can focus on closing more deals
Location:
Creaticity, Off Airport Rd, Yerawada, Pune, Maharashtra 411006
How the solution was deployed to meet the challenge
Applied AI Consulting worked on a solution
- The client was using Gitlab as their version control system for which Gitlab CIs were implemented for each environment that would get triggered whenever any commit was made to the corresponding branch of each repository.
- GitLab flow model was implemented for branching strategy.
- As soon as the code was merged into develop and main branch, Gitlab CI would get triggered and pull the code from repository and trigger the build the code.
- Based on the repo type, GitLab CI was generating the Zip files and container images and uploaded those to AWS S3 and AWS ECR respectively for respective development and production environment.
- Upon successful build, Another Gitlab CI job would get triggered and it would Deploy the artifacts ( Containers and Lambda Zip files ) to the Lambda and ECS environments.
- At the end series of automated tests were executed to verify deployments and Gitlab CI rollbacks were implemented to deploy the previously stable running version of the application.
AWS services used as part of the solution
AWS S3, AWS ECR, AWS Lambda, AWS ECS Farget,
Go Live Date
May 2020
Outcome(s)/results
- With fully automated CI/CD solution, any scope of human prone errors was removed which increased fastened the entire
development cycle. - With automation in the development cycle, the time to market of this SAAS application was reduced to 80% with three to five upgrades on daily basis.
- As the development lifecycle was automated, developers now spend more time development rather than operations.
Architecture diagrams of the specific customer deployment
Architecture diagrams of the specific customer deployment
AAIC is technologyservices company that provide high-end engineering services and is proficient in enterprise class for IT-delivery.AAIC focus on Automation in Cloud and DevOps
