endorsedCompletedAI SaaS Platform for Federal Capture Automation
Services Covered:
Industry
Information Technology
Duration
14 months
Budget
200K - 999K
Start Date
1 July 2025
Team Size
6-10
Engagement Model
Offshore
Project Capability Score (PCS)
75/100
Good
Project Capability Score (PCS) estimates how capable Valere is of successfully delivering a project like this, based on its past experience, track record, and ability to handle similar work. Learn more about PCS
Client
WinMoreBD.ai
Gaithersburg, United States of AmericaProject Summary
Valere designed and built an AWS-native, multi-tenant AI SaaS platform for a business development firm, creating three coordinated codebases (TypeScript backend, Python AI pipeline, React/Next.js frontend) deployed via infrastructure as code. The platform is live and generating revenue; it automates the federal capture lifecycle, produces capture intelligence reports in about one hour (work that previously took four to six weeks), and has performed well in production while serving as the foundation for a human-AI partnership in business development.
Key Challenges
- Build a production-ready, AWS-native, multi-tenant AI SaaS platform that automates the federal capture lifecycle.
- Ensure strict tenant isolation and handle sensitive data across competing tenants.
- Architect the system aligned with a FedRAMP Equivalency roadmap from day one.
- Implement an AI layer built natively on AWS managed services for a deeply specialized and high-precision business process.
Project Deliverables
- TypeScript backend codebase
- Python AI-pipeline codebase
- React/Next.js frontend (server-rendered) containerized on ECS/Fargate
- Multi-tenant architecture with strict tenant isolation across Aurora Serverless v2, Neptune Serverless, OpenSearch, DynamoDB, and S3
- Tenant-scoped authentication via Amazon Cognito and authorization enforced at the API edge
- Document ingestion and AI pipeline orchestrated by AWS Step Functions using Amazon Textract and vector embeddings
- Conversational Bid Assistant built as a multi-stage RAG pipeline on Amazon Bedrock with runtime model/prompt configuration via AWS AppConfig
- Event-driven backbone using SQS, SNS, and EventBridge
- Infrastructure-as-code deployments across staged environments
- Complete UX/UI design delivered in Figma
Project Solution
Valere designed and built three coordinated codebases on AWS: a TypeScript backend, a Python AI-pipeline codebase, and a React/Next.js frontend. Everything was deployed via infrastructure-as-code across staged environments with production isolated in its own VPC. Key deliverables included a multi-tenant architecture with strict tenant isolation across Aurora Serverless v2, Neptune Serverless, OpenSearch, DynamoDB, and S3; tenant-scoped authentication via Amazon Cognito; a document ingestion and AI pipeline orchestrated by AWS Step Functions using Amazon Textract and vector embeddings; a conversational Bid Assistant implemented as a multi-stage RAG pipeline on Amazon Bedrock with runtime model and prompt configuration via AWS AppConfig; an event-driven backbone using SQS, SNS, and EventBridge; a server-rendered Next.js frontend containerized on ECS/Fargate; and complete UX/UI design delivered in Figma.
Project Outcome
- The platform is live and generating revenue.
- Enterprise government contractors are using it to improve capture decisions and win more work.
- The system generates capture intelligence reports in approximately one hour versus the prior four to six weeks.
- Architectural decisions are performing well in production and the platform serves as a foundation for a human-AI partnership in business development.
Platforms
Cloud
Web
SaaS
Tech Stack
Amazon Aurora
Amazon DynamoDB.svg&w=48&q=75)
Amazon Web Services (AWS)
Next.js
Python
React.js
TypeScript
Amazon ECS
OpenSearch
S3
Client Endorsement
Overall Review Rating
4.88
5 out of 5 stars
5 out of 5 starsTimeliness
Cost Rating
Willing to Refer
Quality of Deliverables
“The platform generated revenue; Valere's architectural decisions performed well in production; and the platform served as the foundation for the client's vision of a true human-AI partnership in business development. The team delivered a group of intelligent, creative, and opinionated developers.”
Note: This endorsement is based on publicly available client feedback from external review sources.
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