RFP Management and Automation Software with CI/CD Pipeline for Microservices

Despite having a strong product offering, the client faced several operational and technical roadblocks that were slowing down innovation and scaling:

• Manual Development & Deployment Process:
  They relied a great deal on manual builds, testing, and deployments during their complete development process. This led to a significant amount of production time wasted and increased opportunities for human error, resulting in delays and inconsistencies across environments.
  
   
• Constraints of Monolithic Architecture:
  The application had developed into a tightly coupled monolith that impeded scaling, rapid feature release, or responsive changes to customer requirements. The client recognized that a continued monolithic architecture would hinder their growth over time. They required a monolithic to microservices migration to enable scalability, flexibility, and modern development practices.
  
   
• Confusion Between Deployment and Version Control:
  Teams routinely struggled to track multiple versions and sometimes deployed the wrong code into production. They required a structured and automated deployment pipeline to deploy the correct code to the right environment without manual intervention.
  
   
• AI Integration Requirement:
  A unique challenge the client wanted to solve was around document intelligence for request for proposal management. Organizations uploading RFP documents require quick summaries to make informed decisions. The client wanted to leverage AI to read and summarize RFPs and display key highlights through a document summarization tool, helping agencies decide faster whether to bid.
  
   

In short, the client wanted to modernize their infrastructure, accelerate release cycles, reduce costs, and add AI-driven RFP automation to their platform.

Seaflux partnered with the client to completely transform their platform and operations. The approach combined modern CI/CD practices, microservices architecture, cloud modernization, and AI integration:

1. CI/CD Pipeline Automation with Label Tags

• We set up a fully automated deployment pipeline using Jenkins and GitHub Actions.
• Label-based triggers were introduced to streamline workflows. For example:
   
  • A push with the label feature/ would trigger feature testing.
  • A tag like v1.0-release would automatically deploy to production.
  • A v1.0-beta tag would deploy to the staging environment.
    
     
• This eliminated manual intervention, reduced errors, and made deployments predictable and consistent.

The introduction of a CI/CD pipeline for microservices further enhanced this approach, ensuring each microservice could be deployed independently with speed, accuracy, and version clarity.

 

2. Transition to Microservices & Dockerization

• The application has undergone a refactoring process to utilize individual microservices as part of the monolithic to microservices migration. 
• Each microservice has its own features, is self-contained, and executes on behalf of the parent application. 
• Each microservice has been wrapped in Docker to eliminate the environmental differences between the development, test, staging, and production environments. This allows each microservice to scale independently and utilize resources efficiently with lowered costs.
  
   

3. Upgrading Database to MongoDB Atlas

• The client migrated its existing MongoDB setup to a cloud-native database solution on MongoDB Atlas.  
• MongoDB Atlas has automated scaling, backups, and security technology while also being highly available and globally distributed.  
• As a result, the client has a more resilient application architecture without management complexity with integrated simplicity, further supporting cloud modernization.
  
   

4. AI Integration with AWS Bedrock for RFP Automation

• To address the RFP management and document automation challenge, Seaflux implemented AWS Bedrock integration into the platform.
• AWS Bedrock enabled the system to read uploaded RFPs, generate concise summaries, provide automated RFP response, and display them on the RFP landing page.
• Using a document summarization tool, this RFP automation reduced the manual effort of going through lengthy documents and helped agencies quickly evaluate opportunities.

The transformation delivered measurable improvements for the client:

• 29% Faster Time-to-Market
  Automated CI/CD pipelines and the automated deployment pipeline reduced repetitive tasks and accelerated development cycles.
   
• 27% Cost Reduction
  Microservices architecture allowed independent scaling, cutting infrastructure costs. Automated pipelines further reduced operational overhead.
   
• 7% Increase in RFP Bids
  Integrating AI summarization and RFP analysis in RFP management through AWS Bedrock enabled agencies to assess opportunities quickly, creating additional bids.
   
• Increased Reliability & Version Control
  As with the labeling tagging deployment process, the practice of building decreases human error, and confusion with versions of releases creates levels of clarity in the release process.