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    Real Estate RFP Intelligence Solution

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    AWS-native solution that automates extraction and structuring of critical information from real estate RFP documents, enabling faster evaluation, consistent interpretation, and informed bid decisioning across projects and stakeholders.

    Overview

    Real Estate RFP Intelligence Solution addresses the challenge of manually reviewing large, complex proposal documents containing critical bid information. Traditional RFP analysis is time-intensive, error-prone, and inconsistent, often delaying decision-making or risking incomplete responses.

    This AWS-native solution automates the ingestion, extraction, and structuring of RFP content to streamline evaluation and improve bid accuracy. Documents are securely stored using Amazon S3, with text and layout extracted through Amazon Textract. Amazon Comprehend identifies key entities, contextual relationships, and requirements, while large language models hosted on Amazon Bedrock or Amazon SageMaker classify content and generate structured outputs. AWS Lambda orchestrates end-to-end workflows, and Amazon DynamoDB manages extracted data and review states.

    Security and governance are enforced through IAM role-based controls, encryption via AWS KMS, and network isolation using Amazon VPC. CloudWatch provides monitoring and operational visibility, while API Gateway enables integration with proposal management systems and internal review tools. By automating RFP processing, real estate teams reduce review effort, improve evaluation consistency, and focus on strategic response planning.

    Highlights

    • Extracts key requirements, timelines, compliance criteria, and evaluation factors from RFPs
    • Converts unstructured proposal content into structured, actionable datasets
    • Reduces manual review effort and interpretation errors

    Details

    Delivery method

    Deployed on AWS
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    Support

    Vendor support

    Premium service & support available . contact mayank.gubrele@successive.tech  for more details.