Sales teams spend excessive time creating proposals instead of building relationships. The challenge of customizing proposals for specific client needs while maintaining consistency and quality often results in poor conversion rates and delayed client acquisition. The Web Project Proposal Agent automates this process, helping teams create compelling proposals quickly and professionally.

​
What It Does

  • Analyze client requirements and project scope
  • Study company offerings and capabilities
  • Generate comprehensive business proposals
  • Calculate realistic costs, timelines, and resource needs
  • Create customized value propositions for each client

​
How It Works

  • Input – Client requirements and project details
  • Analysis Phase – Agent processes needs against company capabilities
  • Proposal Creation – Generates structured proposal with pricing
  • Customization – Tailors messaging to specific client context
  • Output – Delivers professional proposal ready for presentation

​
Why This Agent?

  • Reduces proposal creation time by 80%
  • Ensures consistent quality and messaging
  • Improves conversion rates with targeted proposals
  • Frees sales teams to focus on relationships
  • Provides clear, justified pricing and timelines

​
Built for Agencies – Adaptable for Any Service Business

Although designed for marketing agencies, this agent can be tailored for:
  • Consulting Firms – Service proposals and engagement letters
  • Software Development – Project proposals and SOWs
  • Creative Studios – Design and branding proposals
  • Professional Services – Legal, accounting, or advisory proposals

​
Developer Guide :

​
AI (Python) — Execution Flow (Step-by-Step Directional View)

🔁 AI-Powered Proposal Generation

1️⃣  Store Local Template
    → Load `.docx` base template to be used for the final proposal

2️⃣  Initialize LLM
    → Load OpenAI, Gemini, or other provider

3️⃣  Scrape Client Website URL
    → Understand business goals, offerings, brand tone

4️⃣  Summarize Scraped Content
    → Use LLM to extract key highlights and business overview

5️⃣  Initialize Memory & History
    → Maintain session, input context, and prior results

6️⃣  Generate Full Proposal via LLM
    → Sections include:
       - Introduction
       - Objectives
       - Technical Approach
       - Feature List
       - Deliverables

7️⃣  Run Secondary LLM Call (Structured JSON Extraction)
    → Extract:
       - Key requirements
       - Estimated timeline
       - Cost breakdown

8️⃣  Map Extracted JSON into `.docx` Template
    → Replace placeholders with structured values

9️⃣  Upload Final Proposal Document to AWS S3
    → Generate public-accessible S3 path

🔟  Return Final API Response to Frontend
    → Include:
       ✅ Success message
       ✅ S3 filename/path

​
Core Components

ComponentDescription
template_store_local()Loads the Word document template locally (.docx)
scrape_url_content()Extracts and cleans website content
llm_generate_summary()Summarizes scraped content using LLM
initialize_llm()Prepares the language model (GPT-4, Gemini, etc.)
generate_proposal_data()Creates full proposal text using user info + scraped data
generate_requirements_json()Parses structured requirements, timeline, and pricing as JSON
map_json_to_template()Maps structured values into .docx placeholders
upload_to_s3()Uploads generated document to AWS S3 and returns path
final_response()Sends final status and download link to the frontend

​
Next.js — WEB_PROPOSAL_AGENT Integration

Frontend Execution
────────────────────────────────

1️⃣  User selects "Web Proposal Agent" in UI
    ↓

2️⃣  Fills Form Fields:
    ├─ clientName
    ├─ projectName
    ├─ projectDescription
    ├─ discussionDate
    ├─ submittedBy
    ├─ designationSubmittedBy
    ├─ userCompanyName
    ├─ submissionDate
    ├─ userContactNumber
    ├─ userEmail
    └─ userCompanyLocation
    ↓

3️⃣  Form submits to backend
    → Calls Python API via `proagent` service
    ↓

4️⃣  Backend invokes:
    → `getAIProAgentChatResponse()` with form + scraped content
    ↓

5️⃣  Streaming Output:
    → Shows live proposal text generation in chat UI
    ↓

6️⃣  Final Response:
    ✅ Downloadable proposal report (S3 link)
    ✅ Toast/Alert: "Web Proposal is Ready!"