AI Proposal Generator

Executive summary

A generator that assembles a tailored, on-brand proposal from a lead's details, the company's service catalog and pricing, and reusable content blocks. It drafts scope, pricing, and terms, produces a polished PDF, and emails it — so sales responds while interest is hot.

Business problem

Proposals are slow and inconsistent: reps copy-paste from old docs, pricing errors creep in, and by the time a proposal goes out the prospect has cooled or chosen a competitor.

Architecture

AWS services

Data flow

1. 1

   A qualified lead's details (need, size, budget) enter the generator — often straight from the lead-qualification blueprint.

2. 2

   Lambda pulls the relevant services, pricing rules, and template blocks.

3. 3

   Bedrock drafts the scope, narrative, and terms, with pricing computed by deterministic rules (not guessed).

4. 4

   A branded PDF is rendered, stored in S3, and emailed to the prospect via SES.

5. 5

   The proposal record is tracked for follow-up and analytics.

Security considerations

• Pricing computed by auditable rules to prevent AI mispricing.
• Proposals encrypted at rest; access scoped per business.
• Approval step before send for high-value proposals.
• Version history of generated documents.

Cost considerations

• S3, DynamoDB, SES, and Lambda are pay-per-use.
• Bedrock generation is the main per-proposal cost (cents to low dollars).
• No standing infrastructure.

Scalability

• Serverless generation scales with proposal volume.
• New services/templates added as data, not code.
• Multi-brand and multi-currency support.

Deployment roadmap

Future enhancements

• E-signature integration (DocuSign) for close.
• Interactive online proposals with acceptance tracking.
• Win/loss analytics feeding pricing.
• Auto-generated SOWs and contracts.