How to Build an AI Proposal Generator in Bubble.io
A Bubble.io AI proposal generator transforms a 45-minute discovery call debrief into a complete, personalised proposal in under 5 minutes. This post shows you exactly how to build it — the data model, the AI prompt architecture, the Google Docs output, and the client approval workflow.
System Architecture
Debrief intake
A Bubble.io form where the account manager fills in the discovery call debrief immediately after the call: client name and company, their stated goal (in their own words), the specific problem they described, their timeline, any budget signals, the specific concerns or objections raised, what makes this project unique, the recommended approach (a brief description), and any commitments made during the call. This 10-minute structured debrief is the input that drives the entire proposal generation workflow.
AI generation
The Bubble.io backend workflow sends the debrief data to Claude via the API Connector. The prompt is structured to produce each proposal section separately: executive summary, situation analysis (the client’s problem in their language), proposed approach, scope of services (specific deliverables with clear inclusions and exclusions), investment (the pricing, payment terms, and value framing), why us (relevant case study + team credentials), and next steps. Each section is generated in the voice of the relevant team member and stored as a separate field in the ProposalDraft database record.
Google Docs output
A Make.com scenario takes the generated proposal sections from Bubble.io and populates a pre-designed Google Docs template. The template has placeholders ({{executive_summary}}, {{situation_analysis}}, etc.) that Make.com replaces with the AI-generated content. The resulting Google Doc is shared with edit access to the account manager for review and customisation. The final proposal is shared with the client via a secure link — or exported to PDF for formal submission.
Building the Proposal Generator
Build the data model
ProposalDebrief data type: client_name, company_name, client_goal, client_problem, timeline, budget_signal, concerns, unique_factors, recommended_approach, commitments_made, account_manager (linked to User), debrief_date. ProposalDraft data type: debrief (linked to ProposalDebrief), executive_summary, situation_analysis, proposed_approach, scope_of_services, investment_section, why_us_section, next_steps, status (generating/draft/reviewed/approved/sent), google_doc_url, created_at.
Build the AI generation prompt
The system prompt for the proposal generator: ‘You are a senior account manager at [company name], a [description of your business]. Your writing style: direct and specific, client-focused, never generic or vague. You write proposals that win because they demonstrate deep understanding of the client's specific situation, not because they describe your capabilities in general terms. Every section should reference the client's specific situation from the debrief.’ The user message for each section uses a structured format: ‘Write the [section name] for a proposal to [client name] at [company]. Debrief notes: [paste all debrief fields]. This section should: [specific instructions for this section]. Length: [target word count]. Tone: [confident/empathetic/authoritative].’
Build the sequential generation workflow
Generate all sections in a single API call for speed — or generate them sequentially for easier debugging. Single call approach: ‘Generate all sections of a proposal as a JSON object with these keys: executive_summary, situation_analysis, proposed_approach, scope_of_services, investment_section, why_us, next_steps. Each section should be [specifications]. Client debrief: [paste all fields].’ The JSON approach returns all sections in one call; Bubble.io parses the JSON and writes each field to the ProposalDraft record. Processing time: 15 to 30 seconds for a complete proposal draft.
Build the review and approval workflow
After generation: the account manager receives an email notification with a link to the Bubble.io proposal review page. The page displays each section in an editable rich text field — the account manager reads, makes any adjustments, and clicks Approve Section for each. When all sections are approved: click Generate Google Doc — this triggers the Make.com scenario that populates the template. The account manager receives the Google Doc link, makes final formatting adjustments if needed, and shares with the client.
The Investment Section: Where Proposals Win or Lose
The investment section is where most proposals fail — the price appears as a number in isolation with no context. The AI-generated investment section frames the price in the context of value: it opens with the client’s stated goal and the outcome they will achieve, calculates the value of that outcome in financial terms (where possible), presents the investment amount, and states the ROI. Example: ‘The goal you described — reducing proposal turnaround from 5 days to same-day — applied to your current volume of 80 proposals per year at a 24% close rate would, at a 10-point improvement, represent approximately $64,000 in additional annual revenue. The investment to build and deploy this system is [price]. The return is generated within [timeframe].’
Claude generates this section from the debrief’s budget signals and client goal fields — but the account manager must verify and refine the ROI calculation before the proposal is sent. The AI provides the structure and the calculation framework; the account manager validates the numbers are defensible and appropriate to share.
How do I prevent the AI proposal from sounding generic?
Two techniques: (1) ensure the debrief form captures the client's exact language — the words they used to describe their problem, their goal, and their concerns. The system prompt instructs Claude to use the client's own language when describing their situation. A client who reads their exact words reflected back in a proposal experiences a level of understanding that generic proposals cannot match. (2) Include a unique_factors field in the debrief — the one or two things about this project or client that are genuinely unique. Claude incorporates these into the situation analysis and the proposed approach, preventing the copy-paste feeling that comes from proposals that are clearly the same document with the client name changed.
Can clients edit and approve the proposal directly in the system?
Yes — build a client portal in Bubble.io where the client logs in with a unique link (no password required — use a magic link approach with a token), views the proposal sections, and can either approve or request specific changes. Requested changes are logged as revision notes and the account manager is notified. The approved version is stored with a timestamp and the client's identity — a legally relevant record of approval. SA Solutions builds this full client portal as part of the proposal generator system.
Want an AI Proposal Generator Built in Bubble.io?
SA Solutions builds the complete proposal generation system — debrief intake, AI section generation, Google Docs output, account manager review, and client approval portal.
