Helpful plan and ready-to-use pieces to automate RFP responses for hospitality events.

High-level goal

• Reduce time-to-first-response, increase accuracy/consistency, and win more business by automating RFP ingestion, qualification, pricing, and proposal generation while keeping humans in the approval loop for high-value items.

Overview pipeline (end-to-end)

1. Ingest: capture RFPs from email, portal, or upload.
2. Extract & normalize: OCR (if PDF/image) → NLP/LLM extracts structured fields into a canonical schema.
3. Qualify & score: match requirements to venue capability, availability and budget; compute a qualification score.
4. Price & assemble: pull rates/availability from PMS/CRM/rate engine; assemble modular content blocks (rooms, F&B packages, AV, diagrams, terms).
5. Draft proposal: LLM generates a tailored proposal document (PDF/PowerPoint) and an email reply.
6. Review & approve: route to sales/operations for review (with change-tracking).
7. Send & track: send with e-sign or web form, track opens/links and next steps.
8. Learn: capture outcomes and retrain templates/scoring.

Required data model (fields to extract from any RFP)

• RFP metadata: source, date received, RFP id, deadline for response.
• Client: company name, contact name(s), email, phone, timezone.
• Event basics: event name, type (conference, wedding, meeting), primary contact.
• Dates & flexibility: start date(s), end date(s), alternate dates, set-up/strike times.
• Attendance: expected peak daily attendance, room blocks (room nights), number of meeting rooms (size/layout), breakout needs.
• Space specs: capacities, required setup (theater/classroom/banquet/u-shape).
• Food & beverage: meal counts (breakfast/lunch/dinner), dietary needs, banquet-style or plated, coffee breaks.
• AV/Production: mic(s), projector, screens, live-streaming, stage size.
• Budget & pricing constraints: target per-person, total budget, billing rules.
• Ancillary: parking, Wi‑Fi bandwidth, security, branding/signage, vendor restrictions.
• Selection timeline & decision maker(s).
• Contract terms & insurance requirements.
• Attachments: detailed technical specs, diagrams.

Sample JSON schema (example output from extractor) { "rfp_id": "string", "received_date": "2025-09-03", "deadline_response": "2025-09-17", "client": {"company":"string","contact_name":"string","email":"string","phone":"string","timezone":"string"}, "event": {"name":"string","type":"string","start_date":"YYYY-MM-DD","end_date":"YYYY-MM-DD","flexible_dates":["YYYY-MM-DD"]}, "attendance": {"expected_peak": integer, "room_nights": integer, "per_day_attendance":[integer]}, "rooms": [{"name":"Main Hall","capacity":500,"setup":"theater","date":"YYYY-MM-DD"}], "fnb": {"meals":[{"type":"lunch","count":250}], "dietary_requirements":["vegan","gluten-free"]}, "av": {"needs":["projector","sound","mics"], "special":"live stream"}, "budget": {"per_person_estimate": number, "total_estimate": number}, "selection_timeline": {"decision_by":"YYYY-MM-DD"}, "attachments": ["file1.pdf"] }

Extraction approach and tools

• OCR: Tesseract or commercial (AWS Textract, Google Vision) for scanned PDFs.
• NLP/LLM: Use a model (e.g., GPT-family or similar) with a robust prompt that outputs the JSON schema above. Wrap the model with a verification step (regex + rules).
• Libraries: LangChain or a small custom pipeline (Python + PyPDF2, pdfplumber).
• Optionally: use an RPA/email connector (Zapier / Make / Power Automate) to push new RFPs into the pipeline.

Example extractor prompt (ready to paste to an LLM) Extract the following fields from the RFP text. Output only valid JSON matching this schema: {..include the JSON schema from above..}. If a field is unknown, set value to null. Add a short confidence score (0-1) per field. Do not include any other text.

Qualification & scoring

• Build a rule-based + ML scorecard. Example weighted factors:
  • Date availability (35%)
  • Capacity fit (20%)
  • Budget alignment (15%)
  • Event fit/amenities match (15%)
  • Strategic value (repeat client, upsell potential) (15%)
• Score thresholds:
  • ≥80%: auto-generate full proposal and send to ops for quick check
  • 50–79%: generate proposal draft, require sales approval
  • <50%: send a quick “not a fit / partial fit” reply with suggestions or referrals

Pricing & rate engine

• Centralize rate tables: room types, seasonal adjustments, F&B package rates, AV unit costs, labor/housekeeping charges.
• Automation steps:
  • Given extracted dates and room nights, compute availability and rates via PMS or a central pricing service.
  • Apply rules for minimums, food & beverage minimums, taxes, service charges.
  • Output line-item pricing in the proposal.

Proposal templating & modular content

• Create modular content blocks for:
  • Venue summary & selling points
  • Room specifications and diagrams
  • F&B package descriptions (with alt options)
  • AV and production options
  • Contract & cancellation terms
  • Local attractions and transportation
• Use a template engine: e.g., Jinja for HTML → PDF, or generate PowerPoint with python-pptx.
• LLM use: feed modular blocks + extracted fields and ask LLM to personalize language/ordering.

Sample LLM prompt to generate final proposal Use the extracted JSON (paste it). Use these modular blocks (paste selected). Create a professional one‑page summary + detailed proposal with pricing and a short email. Tone: professional, warm, concise. Include next steps and a deadline for acceptance. Output: structured JSON with fields {subject, email_body, proposal_html, attachments:[{filename, base64_pdf}]}.

Approval & human-in-the-loop

• Add a lightweight approval UI: show extracted fields, confidence flags, pricing line items, and diff from last similar bid.
• Allow editors to change text blocks and rerun the LLM to reformat (versioning).
• Enforce mandatory review for deals above a threshold (e.g., >$50k) or where confidence <0.6 on key fields.

Sending, tracking & contract

• Send proposals via email with tracked links or a portal (PandaDoc/DocuSign integration).
• Track opens, time spent on pages, signature progress; push events back to CRM.

Operational integrations (common choices)

• PMS / CRS / Sales: Salesforce, HubSpot, Cvent, Delphi (or your existing systems).
• Document signer: DocuSign, PandaDoc.
• OCR & storage: AWS S3 + Textract, Google Cloud Storage + Vision.
• Automation/Orchestration: Zapier / Make / Power Automate / custom serverless (AWS Lambda).
• LLM provider: OpenAI or other LLMs; host with safe guard rails and system prompts.

Quality, legal, and compliance

• Ensure the contract language and insurance limits are reviewed by legal before sending.
• Keep price-valid-until dates and version control visible.
• Log audit trails for RFP handling and approvals.

Metrics to track (KPIs)

• Avg time from RFP receipt → first response
• Win rate by lead source and by score band
• Response accuracy issues (post-send corrections)
• Revenue per bid, cost-per-proposal
• Time saved per RFP

MVP implementation plan (timeline from today: Sep 3, 2025)

• Week 1–2: Requirements, collect 20 historical RFPs, define schema and modular blocks.
• Week 3–4: Build ingestion and extraction prototype (OCR + LLM JSON extractor).
• Week 5–7: Integrate price source (manual CSV initially) and generate templated proposal outputs.
• Week 8–10: Add review/approval UI and basic tracking; pilot with small sales team.
• Week 11–14: Integrate PMS/CRM, refine scoring, implement analytics and e-sign.
• After 90 days: iterate on templates, retrain extractors, extend to more RFP types.

Risks & mitigations

• Bad extraction → require human verification and confidence thresholds.
• Price errors → lock critical pricing behind a read-only price engine and require ops sign-off for exceptions.
• Over-personalization errors (misstated capabilities) → include an “assumptions” section pulled from canonical venue data.
• Legal exposures in contract language → always require legal sign-off on contract clauses.

Quick example outputs

• Quick reply email (automated draft): Subject: Proposal for [Event Name] — [Venue Name] — Response by [date] Body: Hi [Contact], thanks for the RFP for [Event Name] on [dates]. We can accommodate ~[attendance] in [room]. Attached is an initial proposal including rooms, F&B and AV options. If this looks good I’ll confirm availability and hold the space for [hold_period]. Best, [Sales Rep]

• One-paragraph proposal summary: [Venue Name] is pleased to propose meeting space for [Event Name] from [start_date]–[end_date]. We can provide the [Main Hall] (capacity X) and [Y] breakout rooms; sample F&B package at $Z per person and AV estimate $A. Total preliminary estimate: $TOTAL (subject to contract). Next step: a 48-hour tentative hold to secure space.

Actionable next steps you can pick now (I implemented these often)

• Gather 20 representative RFPs and flag common formats and missing fields.
• Build an extraction prompt + JSON schema and run it over those 20 to measure extraction accuracy.
• Create 6 modular content blocks (venue intro, 3 F&B packages, AV, terms) and wire them into the LLM prompt to generate a proposal.
• Pilot with low-risk RFPs and require human approval for all outgoing proposals until error rate <5%.

If you want, I can:

• Provide a ready-to-run extractor prompt and validation script (Python).
• Draft a sample Jinja HTML proposal template and a sample email template.
• Sketch the approval UI wireframe and database schema.

Which one of those would you like me to generate now?