AI Proposal Generator for Consultants

AI Prompts to Build This

Copy these build prompts into Claude, Cursor, or your AI coding tool. Create a free account to unlock the full research behind them.

1. 1. Project Setup

   Build the weekend MVP of "ProposalPro": draft a client-ready proposal in a consultant's own voice from their past winning proposals, and see when the client opens it.
   
   Stack: Next.js (App Router), TypeScript, Tailwind, Supabase (Postgres with pgvector, Row Level Security, Auth with email magic link), Claude (Anthropic API) with structured output, and an embeddings API that returns 1536 dimensions. Deploy on Vercel.
   
   Tables (Row Level Security on, consultants see only their own rows, and the share page reads one proposal by its token):
   - consultants(id, user_id, name, brand_primary_hex, default_hourly_rate)
   - clients(id, consultant_id, name, industry, contact_email)
   - proposals(id, consultant_id, client_id, title, status, scope_summary, pricing_model, total_price_cents, generated_at, share_token)  where status is one of draft, sent, viewed, won, lost
   - proposal_sections(id, proposal_id, type, content jsonb, position)  where type is one of summary, scope, timeline, pricing, terms
   - past_proposals(id, consultant_id, raw_text, outcome, embedding vector(1536))  where outcome is one of won, lost, unknown
   - view_events(id, proposal_id, viewed_at, referrer)
   
   Screens: /login, /new (client and scope form), /proposals/[id] (editor), /p/[token] (public share page).
   Env vars (names only): NEXT_PUBLIC_SUPABASE_URL, NEXT_PUBLIC_SUPABASE_ANON_KEY, SUPABASE_SERVICE_ROLE_KEY (server only), ANTHROPIC_API_KEY, EMBEDDINGS_API_KEY.
   
   Do not build: billing or plans, PDF export, email notifications, team accounts, a second AI model.
   Done when: npm run dev starts, you can sign in, and the six tables exist with pgvector on and Row Level Security on.

2. 2. Core Feature

   Build the one feature that proves ProposalPro: a proposal drafted in the consultant's own voice, with a signal when it is opened.
   1. Retrieve: embed the new request (industry, goals, rough deliverables, target price) and query past_proposals by cosine similarity for this consultant's 2 or 3 closest, preferring outcome won.
   2. Generate: call Claude with "You write consulting proposals in this consultant's voice and structure, using their past winning proposals as style reference. Return strict JSON { summary, scope: string[], deliverables: string[], timeline: [{ milestone, week }], pricing: [{ item, amount_cents }], terms }. Do not invent client details that were not provided." Pass the retrieved proposals and the new inputs. Stream the JSON into the editor and save the sections.
   3. Benchmark: compare total_price_cents with the consultant's average for similar projects. If it is more than 20% below, show a non-blocking warning: "This is priced below your typical rate for similar projects. Review before sending."
   4. Share: create a unique share_token and serve /p/[token] as a public, read-only page with no login. On every load, write a view_events row, and set the status to viewed on the first view. Show "viewed" with the time on the proposal list.
   Empty state: with no past proposals, show an upload box for them and one sentence on why they matter.
   Done when: a request returns a proposal that follows a past one's structure, an underpriced draft shows the warning, and opening the share link flips the proposal to viewed.

3. 3. Landing Page

   Build a one-page landing site for ProposalPro, an AI proposal generator for independent consultants.
   Hero: "Proposals from your own winning proposals, in minutes." Sub: "It drafts scope, pricing and timeline in your voice, then tells you the moment the client opens it." One button: Join the waitlist.
   Sections: the problem (a proposal takes hours and sounds like everyone else's), how it works in four steps (set up a client and scope, generate the draft, refine and benchmark, send and track), a sample proposal with a pricing warning, and an FAQ on whose data it learns from and client confidentiality.
   Waitlist: store the email in a waitlist table in Supabase. No other service.
   Voice: confident and practical, written for a solo consultant.
   Done when: the page renders on a phone and a submitted email appears in the waitlist table.

4. 4. Branding Package

   Use a design or image tool for this one. A coding agent cannot draw a logo.
   Brand for ProposalPro: a wordmark and an icon that suggest a folded proposal with a check mark.
   Colors: warm off-white, deep ink and one gold accent. Type: a refined serif for headings and a clean sans for the interface.
   Deliverables: wordmark, icon, a proposal cover style that takes the consultant's own logo and color, and one launch graphic.
   Done when: each deliverable is saved in one folder and the icon reads at 32 px.