What a AI Proposal Management Platform actually does
Auto-answers RFP questions from past-proposal library and drafts proposal sections on demand, cutting sales-team RFP turnaround from 2 weeks to 2 days.
Enterprise B2B sales teams waste 30–40% of proposal time answering routine RFP questions ('Describe your data security', 'What's your uptime SLA?') that have been answered identically in dozens of past proposals. This implementation uses embeddings (Voyage) to index your organization's past-proposal content, then retrieves and personalizes relevant answers via LLM (Claude Sonnet 4.6 for fast sections, Opus 4.8 for high-stakes executive summaries). A sales-ops consultant or fractional RevOps team can resell this per sales-team at $199–$799/mo, with text-only COGS <$50/mo. The DIY path on Lovable is particularly attractive: Voyage embeddings cost ~$0.06 per proposal, and Sonnet drafts cost ~$0.005 per section — total COGS per team per month is $20–40 even at 20+ proposals generated.
AI capabilities involved
RFP question auto-answering from past-proposal library (RAG)
Proposal section drafting from brief
Win-loss-pattern detection (which proposal language correlates with wins)
Pricing-table personalization by deal context
Redlining suggestions on returned counter-proposals
Who uses this
- Sales-ops consultancies managing RFP workflows for 8–25 mid-market sales teams
- RevOps fractional teams selling AI-augmented proposal writing as a service
- Bid-management consultants supporting enterprise sales orgs through competitive tenders
SaaS alternatives on the market
Real products you can sign up for today — with current 2026 pricing, honest pros and cons.
Loopio
Large enterprises (100+ sales reps) managing 50+ RFPs per year and can justify $25K+/yr spend.
Enterprise quote (typically $25K+/yr)
Custom pricing (usually $50K+/yr at scale)
Pros
- +Purpose-built for enterprise RFP response. Gold standard in the space.
- +AI-assisted question answering + competitor analysis.
- +Team collaboration (RFP manager, subject-matter experts, legal review).
- +Integrations with Salesforce, CRM platforms.
Cons
- −Extremely expensive ($25K+/yr minimum); no agency-tier white-label pricing.
- −Implementation requires 8–12 weeks and significant internal resource commitment.
- −Not designed for resale or white-label; enterprise owns the RFP database.
- −Limited customization without professional-services engagement.
Proposify
Mid-market sales teams (10–50 reps) wanting professional proposal templates without RFP-specific complexity.
$49/user/mo (Pro)
$599+/mo per team (Business plan)
Pros
- +Beautiful proposal templates with strong design focus.
- +e-signature integration (DocuSign, Adobe Sign).
- +Mobile app for on-the-go signing.
- +CRM integrations (Salesforce, HubSpot).
Cons
- −Per-user pricing ($49–600/mo) scales with team size; expensive for large sales orgs.
- −No RFP-specific features or RAG question-answering.
- −No white-label option.
- −Limited AI; mostly design and signing workflows.
PandaDoc
Sales ops teams that want a general document-generation platform and can build custom workflows in-house.
Free plan (limited features)
$35/user/mo (Standard)
$65+/user/mo (Business)
Pros
- +Document-generation platform with Docusign, Stripe integrations.
- +API-first design; highly customizable.
- +Good for sales ops and document automation at scale.
- +Free tier for evaluation.
Cons
- −Per-user pricing ($35–65/mo) scales with team size.
- −No RFP-specific features; you'd have to build RAG + question-answering yourself.
- −No white-label agency tier.
- −Requires significant engineering to implement RFP workflows.
Responsive (formerly RFPIO)
Enterprise sales orgs in heavily regulated industries (government, finance) managing 100+ RFPs per year.
Enterprise quote (typically $25K+/yr)
Custom pricing
Pros
- +RFP-specific platform (like Loopio). Excellent for compliance / government RFPs.
- +Question-library matching + response tracking.
- +Strong audit trails for regulated industries.
Cons
- −Enterprise pricing (quote-based); typically $25K+/yr.
- −No white-label; designed for end-user enterprises.
- −Implementation is expensive and lengthy (12+ weeks).
- −Limited AI compared to newer platforms.
The AI stack
The core pipeline ingests past-proposal documents (PDFs), chunks them into sections, embeds via Voyage embeddings, and indexes into pgvector. On-demand, when a sales team needs to answer an RFP question or draft a section, the system retrieves relevant past-proposal content via cosine similarity, then uses Claude Sonnet 4.6 (or Opus 4.8 for high-stakes copy) to synthesize and personalize the response. The architecture trades off embedding fidelity (Voyage's higher cost vs. cheaper models) for retrieval quality — RFP proposals are nuanced, and retrieving the wrong past answer is worse than no answer.
Embeddings + proposal indexing (Voyage)
Converts past-proposal text into dense vectors for semantic search. When RFP question comes in, embed the question and retrieve top-5 most-relevant past-proposal sections (e.g., 'Data security' → 'Here's what we said about security in the 2024 Fortune 500 RFP').
Voyage voyage-3.5
$0.06 per 1M tokensEnterprise proposals with complex regulatory + compliance language. Default for production.
text-embedding-3-small
$0.02 per 1M tokensMVP; simple proposals without regulatory complexity.
Our pick: Use Voyage voyage-3.5 for production. Cost per proposal embed is ~$0.06 × (proposal_length / 1M tokens). A 10K-token proposal costs ~$0.0006 to embed. For a sales team of 10 reps with 2–3 new proposals per week, monthly embedding cost is ~$3–5.
Foundation model (RFP answering + section drafting)
Takes retrieved past-proposal sections and RFP question/brief, then synthesizes a personalized response or proposal section.
Claude Sonnet 4.6
$3 / $15 per M tokens (input / output)Routine RFP sections (technical capabilities, implementation timeline, team experience).
Claude Opus 4.8
$5 / $25 per M tokensExecutive summary, pricing terms, SLAs, compliance/security sections, and high-stakes deal language.
DeepSeek V4 Flash
$0.14 / $0.28 per M tokensLow-value drafts only (first-pass outlines; must be heavily reviewed before sending).
Our pick: Use Sonnet 4.6 for standard sections (capability, timeline, team, implementation). Use Opus 4.8 for executive summary, legal/compliance sections, pricing terms, and any high-stakes language. For a typical RFP (30 questions × 50 input tokens per Q + 200 output tokens per answer), mixed strategy costs ~$0.25 total (Sonnet) vs. ~$0.40 (Opus). For a sales team sending 10 RFPs/month (300 questions), monthly LLM cost is ~$2.50 Sonnet + ~$1 Opus = $3.50.
Proposal chunking + section extraction
Converts PDF proposals into searchable chunks (each major section — 'Security', 'Pricing', 'References' — becomes one vector for indexing).
PyPDF2 / Unstructured.io (SDK)
Free (OSS libraries)MVP; text-heavy proposals without complex formatting.
LangChain's PDF loaders + semantic chunking
Free (OSS library)Production; large proposals with complex structure.
Our pick: Start with PyPDF2 for MVP. Upgrade to LangChain semantic chunking for production to avoid splitting sentences across chunks.
Storage + vector DB (Supabase + pgvector)
Stores past proposals (as text blobs), embeddings, and retrieval results. Supports multitenancy (each sales team's proposal library is separate).
Supabase Pro ($25/mo)
$25/moDIY consultants with 5–20 sales-team clients, each with 500+ past proposals.
Our pick: Supabase Pro for MVP. Upgrade to Team only if you reach 50+ sales teams with 5K+ proposals each (rare).
Reference architecture
The system ingests past proposals (PDF uploads or DocuSign / Salesforce integrations), chunks them into proposal-section vectors via LangChain, embeds via Voyage voyage-3.5, and indexes into Supabase pgvector. On-demand, a sales rep asks 'How do we answer this RFP question?' or clicks 'Draft section: Security.' The system retrieves top-5 most-similar past-proposal sections, then passes them to Claude Sonnet 4.6 (or Opus for high-stakes copy) to synthesize a new answer tailored to the current RFP context and deal. The main engineering challenge is handling PDF variety (legal documents, marketing collateral, technical specs) and ensuring retrieved sections are actually relevant (false positives waste time).
Sales team uploads past proposals (PDF files) or connects Salesforce / DocuSign for historical documents
Lovable frontend → Supabase StorageUser selects PDFs to upload. Each file is stored in Supabase Storage with metadata (filename, upload_date, sales_team_id). Large documents (>50MB) are flagged for manual review.
Async: Chunk proposals into sections and embed
Supabase Storage webhook → Python worker (Fly.io) → Voyage APIWebhook triggers on PDF upload. Worker uses LangChain to parse PDF, splits into sections (semantic chunking; typically 5–10 sections per proposal). Each section is embedded via Voyage voyage-3.5. Results stored in `proposal_embeddings` table (sales_team_id, section_text, embedding_vector).
On-demand: Answer RFP question via RAG
Lovable UI → Supabase Function → Voyage + ClaudeSales rep enters RFP question (e.g., 'How do you ensure 99.9% uptime?'). Supabase function: (1) embeds the question via Voyage, (2) cosine-similarity search over `proposal_embeddings` to retrieve top-5 most-relevant sections from past proposals, (3) passes retrieved sections + question to Claude Sonnet 4.6 with prompt 'Based on these past proposal sections, draft an answer to this RFP question that's specific to the current client context.' Returns 200–400 token draft. Sales rep copies to RFP response document.
On-demand: Draft new proposal section
Lovable UI (section picker) → Supabase Function → ClaudeSales rep clicks 'Draft section' and selects 'Security' / 'Pricing' / 'Implementation Timeline'. Lovable auto-retrieves the top-3 most relevant sections from past proposals in this category. Claude Opus 4.8 drafts a new section tailored to current deal context (client industry, deal size, competitive context passed in). Returns 500–1000 token draft.
Dashboard: Past-proposal library + retrieval stats
Lovable UI + rechartsSales team sees: (1) total proposals indexed (e.g., '245 past proposals, 1,200 sections'), (2) most-frequently-retrieved sections (shows which content is most valuable), (3) low-coverage areas ('No healthcare-industry proposals' — flag for team to add).
Estimated cost per request
~$0.018 per RFP-question answer (Voyage embed of question ~500 tokens + retrieval + Sonnet generation ~500 input + 200 output = $0.018); ~$0.015 per proposal-section draft (Opus 4.8 ~1K input + 500 output).
Cost calculator
Drag the sliders to model your actual usage. The numbers update in real time so you can stress-test economics before writing a single line of code.
This calculator models monthly COGS for a sales-ops consultancy reselling an AI proposal-management platform to mid-market sales teams. Assumptions: each sales team has 300–1000 past proposals indexed; generates 5–20 new proposals per month using the RAG system. Fixed costs cover Supabase and Voyage embedding index; per-proposal costs cover LLM token usage for answering + drafting.
Estimated monthly cost
$35.40
≈ $425 per year
Calculator notes
- This model assumes mixed Sonnet (90%) + Opus (10% for executive summary, legal, pricing). Adjust ratio if more high-stakes sections.
- Voyage embedding cost is front-loaded (when ingesting past proposals) not per-draft. Assuming ~500 past proposals indexed per team = ~$0.30 one-time; ongoing embedding cost is minimal.
- Supabase Pro supports ~10–15 sales teams before hitting query limits. At 20+ teams, upgrade to Team ($599/mo).
- This excludes customer support, CRM/DocuSign integration development, and proposal-library curation consulting.
Build it yourself with vibe-coding tools
In 1 weekend with Lovable Pro, you'll have a working MVP that uploads PDFs, indexes them via Voyage embeddings, and retrieves + drafts RFP answers. By Sunday night, you'll have a demo showing a sales team how to answer their first RFP in 2 hours vs. 2 days.
Time to MVP
12–16 hours (1 weekend)
Total cost to MVP
$25 Lovable Pro + $80 API credits (Voyage embeddings, Claude Sonnet)
You'll need
Starter prompt
Build an RFP proposal-management tool with these features: 1. Authentication: Supabase Auth. Each sales team is a separate tenant. 2. Proposal upload: Form that accepts PDF file + metadata (client_name, date, won_or_lost). Store in Supabase Storage. After upload, trigger async job to parse PDF into text sections. 3. Proposal library: Show all uploaded proposals (table with client, date, status). Click to view full text. 4. RFP question answering: Text input to enter RFP question (e.g., 'How do you ensure 99.9% uptime?'). On submit: (1) embed question via Voyage voyage-3.5, (2) retrieve top-5 most similar past-proposal sections via pgvector, (3) call Claude Sonnet 4.6 with prompt 'Based on these past proposal sections [list them], draft an answer to this RFP question specific to enterprise context.' Display draft in text area with copy-to-clipboard button. 5. Section drafting: Dropdown menu to select section type (Security, Pricing, Implementation, Team, References). Click 'Draft.' System retrieves top-3 similar past sections, calls Claude Opus 4.8 to draft a new section. Display draft. 6. Dashboard: Show total proposals indexed, most-retrieved sections (bar chart), recent uploads. 7. Styling: Professional (dark/light mode), Tailwind + shadcn/ui, suitable for B2B sales teams. Database schema: - sales_teams (id, team_name, created_at) - proposals (id, sales_team_id, client_name, date, proposal_text, won_or_lost) - proposal_sections (id, proposal_id, section_type, section_text, embeddings) - proposal_embeddings (id, sales_team_id, section_text, embedding_vector, section_type) Edge Functions: - POST /chunk-proposal: takes proposal_id, parses PDF text, chunks into sections (5–10 per document), stores in proposal_sections - POST /embed-sections: takes proposal_id, embeds all sections via Voyage voyage-3.5, upserts into proposal_embeddings - POST /answer-rfp-question: takes sales_team_id + question, embeds question, retrieves top-5 sections, calls Sonnet 4.6, returns draft - POST /draft-section: takes sales_team_id + section_type, retrieves top-3 similar sections, calls Opus 4.8, returns draft
Paste this into Lovable
Follow-up prompts (run in order)
- 1
Add a 'Win/loss analysis' page: for each proposal marked as 'won', extract language patterns that correlate with wins. Show top phrases / talking points used in winning proposals. Train an XGBoost classifier to predict 'This language helps us win' and highlight it in drafted sections.
- 2
Integrate with Salesforce / HubSpot to auto-pull past proposals from the CRM. Create a scheduled job to sync new proposals weekly.
- 3
Add 'Pricing tables' feature: form to input pricing structure (user tiers, features, monthly cost). AI auto-generates 2–3 pricing-table variants for different customer segments. Sonnet 4.6 drafts narrative explaining pricing rationale.
- 4
Add 'Redlining' mode: when client returns a marked-up proposal with changes, upload the markup. Claude Sonnet 4.6 diffs the original vs. marked version, identifies specific asks, and drafts response options ('Accept', 'Counter with X', 'Escalate to legal').
- 5
Add multitenancy: support 5 separate sales teams logging in. Each team sees only their own proposals and RFP answers (RLS enforces isolation).
Expected output
A working Lovable app where a sales team can: (1) upload PDFs of past proposals, (2) ask RFP questions and get AI-drafted answers (retrieved from past proposals), (3) select a section type and auto-draft new sections (Security, Pricing, etc.), (4) see stats on most-retrieved content. No production-grade security audit needed — proof-of-concept.
Known gotchas
- !PDF parsing is brittle: complex layouts, tables, images break PyPDF2. For MVP, test with text-heavy proposals first. Upgrade to Unstructured.io or LangChain's semantic chunking for production.
- !Voyage embeddings are expensive if you embed every single sentence. Instead, chunk into sections (5–10 per proposal) and embed per section. This keeps cost low while preserving retrieval quality.
- !Claude Sonnet 4.6 is fast but sometimes generates generic answers (e.g., 'We have strong security practices' instead of 'We comply with SOC 2 Type II, penetration-tested quarterly, encryption at rest and in transit'). Review drafts carefully before sending to clients.
- !Lovable's form builder doesn't natively support PDF file uploads to Supabase Storage. Work around this by using a custom React component (file input → fetch upload) or Supabase Storage UI library.
- !Proposal sections retrieved by embeddings may not always match the RFP question perfectly. Add a human-review step: 'Review retrieved sections before generating answer' to avoid hallucinated content.
- !Large PDF files (>50MB) can timeout during async chunking. Set a 10MB file-size limit and warn users to split larger proposals or use text upload as fallback.
Compliance & risk reality check
Proposal-management platforms handle sensitive business information: past proposals, pricing terms, client names, competitive analysis, and intellectual property. This implementation must protect confidential proposal content, ensure data privacy (GDPR for international teams), and maintain proposal authorship / audit trails for legal review.
Confidential proposal content and competitive sensitivity
Past proposals contain proprietary pricing, implementation methods, vendor relationships, and competitive positioning that must not be disclosed. If a sales rep leaves or the platform is compromised, leaked proposals can cost millions in competitive disadvantage.
Mitigation: Implement strict role-based access control (RBAC): only sales reps and proposal managers in a given sales team see proposals. Finance, HR, and other departments see nothing. Add an audit log (immutable Supabase table) of who accessed / downloaded each proposal and when. Encrypt all proposal blobs at rest in Supabase (enable field-level encryption if available). Require VPN or IP allowlist for access to sensitive proposal libraries. Add a watermark to all downloaded PDFs: 'CONFIDENTIAL — [Sales Team Name] Proposal Library — [User Name] — [Date Downloaded]'.
GDPR / CCPA for client PII in proposals
Past proposals often mention client names, employee names (decision-makers), contact info, and company details. This is personal data under GDPR (Art. 4(1)) and CCPA. If a proposal is retrieved for a different RFP and the original client is mentioned, PII is being reused without the original client's consent.
Mitigation: Add a data-residency selector: 'This proposal contains data from [EU/US/Other] — choose region.' For EU proposals, host data in EU Supabase regions. When retrieving similar proposals to answer an RFP, redact client names from the retrieved text: 'Replace [Original Client] with [Current Client]' before showing to sales rep. Include a note: 'This section mentions your original client — please redact or verify consent before reusing.' For CCPA, add a 'Do not reuse' flag per proposal; compliance team can mark proposals that cannot be used as templates due to client confidentiality.
E-signature audit trail (DocuSign, Adobe Sign)
Many RFPs end with signed proposals (via DocuSign, Adobe Sign). The signature and timestamp are legal evidence of intent to contract. If the platform modifies a signed proposal or loses audit-trail metadata, the signature may become invalid.
Mitigation: If integrating with DocuSign / Adobe Sign, preserve the original signed PDF in a separate 'signed_proposals' bucket. Do NOT allow AI-drafting or editing of signed documents. Only allow AI-drafting on unsigned drafts. Store metadata (signature timestamp, signer identity, document hash) in Supabase `signed_proposal_metadata` table for audit. If a proposal is later disputed, you can prove the exact signed version.
AI-generated content and liability
If Claude generates a false claim (e.g., 'We support feature X' when we don't, or 'Our SLA is 99.99% uptime' when it's 99.9%'), the sales rep might send it to the client as-is. If the deal closes based on this false claim, the company faces breach-of-contract liability.
Mitigation: Add prominent disclaimers in the UI: 'AI-generated content is a DRAFT ONLY. Legal and product teams MUST review before sending to clients.' Require sign-off from a compliance or legal email tag before the draft can be exported. Log who approved each section and when. Include a 'Flag for review' button that routes AI drafts to legal / compliance queue. For high-stakes sections (SLAs, pricing terms, compliance claims), default to Opus 4.8 (higher accuracy) and add mandatory human review.
Proposal authorship and version control
When a sales rep generates a proposal section via AI, who 'wrote' it? If disputes arise later ('We never promised that'), the company must prove the claims were AI-assisted, not deliberately misleading.
Mitigation: Include a 'Generated by AI' label on all drafted sections in the final PDF. Add version history: track every AI generation, human edit, and approval. Store in Supabase `proposal_versions` table (version_num, drafted_by_ai, reviewed_by, approved_date, change_summary). When exporting to PDF, include a footer: 'This proposal was generated with AI assistance and reviewed by [Sales Rep Name] on [Date].' This provides defensibility: if a client claims 'You promised feature X,' the company can show it was an AI draft reviewed by a human.
Build vs buy: the real math
6–9 weeks
Custom build time
$18,000–$32,000
One-time investment
4–8 months (10 sales-team clients at $399/mo)
Breakeven vs buying
A DIY Lovable build at $25 Lovable Pro + $40–80/mo infra (Voyage embeddings, Supabase, LLM) allows you to resell at $199–$799/mo per sales team with 95%+ gross margin. At 10 sales-team clients × $399/mo = $3,990/mo revenue, COGS is <$50/month, yielding ~$3,940/mo gross margin (~$47K/year). The custom build path ($18K–$32K) breaks even after 4–8 months. Enterprise-proposal SaaS (Loopio at $25K+/yr) dominates the end-user market but charges so much that white-label resale is economically impossible. The consulting play (DIY sell to sales-ops teams) is the open market. DIY lets you validate consultancy demand (do sales teams actually buy?) before committing to $30K build.
Skip the DIY — RapidDev builds the production version
A Lovable MVP gets you a demo. Production needs auth that doesn't leak data, AI calls that don't bankrupt you, observability when models drift, and code you can audit. That's what we ship.
Discovery call (free)
30 minWe map your exact AI Proposal Management Platform use case: who uses it, target volume, AI model choice, integrations, compliance scope. You get a detailed scope document and fixed-price quote within 48 hours.
AI-accelerated build
6–9 weeksOur engineers use Claude Code, Lovable, and custom tooling to ship 3–5x faster than agencies. You see weekly progress in a staging environment — not a black box.
Launch + handoff
1 weekWe deploy to your infrastructure, transfer the GitHub repo, set up CI/CD and monitoring, and train your team. You own 100% of the source code, prompts, and model configurations.
What you get
Timeline
6–9 weeks
Investment
$18,000–$32,000
vs SaaS
ROI in 4–8 months (10 sales-team clients at $399/mo)
30-min call. Fixed-price quote within 48 hours. No commitment.
Frequently asked questions
How much does it cost to build a proposal-management platform?
A full custom build via RapidDev costs $18K–$32K (6–9 weeks). A DIY Lovable MVP costs $25 Lovable Pro + ~$80 API credits (1 weekend). Monthly infra is $40–80 (Voyage embeddings, Supabase, LLM). If reselling at $199–$799/mo per sales team, COGS is <$50/month, yielding 95%+ gross margin. Breakeven on custom build is after 4–8 months of 10+ team subscriptions.
How long does it take to ship a proposal-management platform?
DIY with Lovable: 1 weekend to MVP (PDF upload → Voyage embedding → RAG question-answering → Sonnet section drafting). Custom build with RapidDev: 6–9 weeks to production with DocuSign integration, compliance audit trails, and enterprise-grade proposal version control.
Can RapidDev build this for my company?
Yes. RapidDev has shipped 600+ applications and B2B SaaS platforms. For a proposal-management platform, typical scope is $18K–$32K over 6–9 weeks, covering Supabase multitenancy, Voyage embeddings, Claude integration, DocuSign / Salesforce APIs, and compliance (audit logging, confidentiality controls, e-signature preservation). Email seopartner@rapidevelopers.com for a free 30-min consultation.
Should I use Sonnet or Opus for section drafting?
Use Claude Sonnet 4.6 for standard sections (capability, implementation timeline, team, references) — cost is ~$0.015 per section. Use Opus 4.8 for high-stakes sections (executive summary, pricing terms, legal/compliance language, SLAs) — cost is ~$0.025 per section. Mixed approach: Sonnet for 90% of sections, Opus for 10%. For a typical 30-question RFP, total cost is ~$0.30.
How do I ensure AI-generated proposals don't contain false claims?
Add mandatory human review: flag all AI-generated sections as DRAFT and require sign-off from legal / compliance before exporting. Use Opus 4.8 for high-stakes sections (better accuracy than Sonnet). Include a 'Flag for review' button that routes drafts to compliance queue. Label the final PDF: 'Generated by AI and reviewed by [Sales Rep] on [Date].' This provides defensibility if disputes arise later.
Can I integrate with Salesforce or DocuSign?
Yes. Salesforce has an API for pulling past opportunities + associated proposal documents. DocuSign has webhooks for signed proposals + signature metadata. For DIY, use Supabase postgres-webhooks to sync new proposals from Salesforce weekly. For signed proposals, preserve the original signed PDF in a separate 'signed_proposals' bucket and store signature metadata (timestamp, signer identity, document hash) in an audit table.
How many sales teams can one Supabase instance support?
Supabase Pro ($25/mo) supports ~10–15 sales teams, each with 500–1000 past proposals before hitting query limits. For 20+ teams, upgrade to Supabase Team ($599/mo). Most consultancies start with Pro and upgrade once they have >15 paying clients.
How do I handle confidential client information in proposals?
Add a compliance flag per proposal: 'This proposal contains confidential client data; do not reuse.' When retrieving similar proposals for a new RFP, redact client names and contact info before showing to sales rep. Include a note: 'This section mentions your original client — please redact or verify consent before reusing.' Store client-PII-handling decisions in Supabase `proposal_confidentiality` table.
What's the advantage over buying Loopio?
Loopio costs $25K+/yr per enterprise and offers no white-label tier. DIY Lovable allows you to resell at $199–$799/mo per sales team with 95% gross margin. After 10 clients, your annual revenue is $24K–$95K with <$2K COGS. Loopio's enterprise pricing makes it impossible for consultants to resell affordably. DIY also lets you build custom integrations (Salesforce, CRM, deal-stage tracking) specific to your clients' needs.
Can I use text-embedding-3-small instead of Voyage to save cost?
Yes, but with caveats. text-embedding-3-small ($0.02/M) is 3× cheaper than Voyage voyage-3.5 ($0.06/M). For simple business proposals, text-embedding-3-small works. For legal/compliance-heavy proposals (especially RFPs from regulated industries), Voyage is higher-fidelity and less likely to retrieve irrelevant sections. For MVP, start with text-embedding-3-small. Upgrade to Voyage once you have 5+ paying clients and notice retrieval quality issues.
Want the production version?
- Delivered in 6–9 weeks
- You own 100% of the code
- AI cost monitoring built in
30-min call. No commitment.