# AI Integration Recommendations for CFL Charitable Auction + Donor System

## 1. Auction AI (already partially designed)
- **Starting Bid Engine** (implemented in AiLotAnalyzer): Vision model identifies item → comps lookup → suggests 40/50/60% of market value. Store full JSON on Lot.
- **Bid Anomaly Detection**: Monitor for unusual patterns (sudden large jumps from new accounts, coordinated bidding). Flag for admin review.
- **Smart Lot Recommendations**: On the lot page, “Bidders who liked this also watched…” using collaborative filtering or simple co-occurrence.
- **Auto-description & SEO**: From the same vision call, generate clean listing title + description + suggested categories.

## 2. Donor Outreach AI
- **Personalized Solicitation Drafts**: Feed donor profile + past giving + school/region + recent notes into an LLM to draft a short, personal email or call script for the assigned intern. Intern reviews/edits before send.
- **Next-Best-Action Ranking**: Score each assigned donor daily: “Call this week”, “Send thank-you”, “Invite to live display event”, “High potential major gift”. Surface on intern dashboard.
- **Optimal Timing**: Suggest best day/time for outreach based on historical response data (or simple rules + LLM refinement).
- **Follow-up Sequence Generator**: After a pledge or win, auto-draft a thank-you + impact update sequence that the intern can approve.

## 3. Dashboard / Insights AI
- **Predicted Raise**: Given current pace, remaining days, and historical conversion, project final gross with confidence band.
- **Engagement Score Explanation**: “Why is this donor scored 92? Recent gift + high open rate + school match.”
- **Intern Coaching Tips**: “Your portfolio has 4 lapsed major donors. Here are three suggested re-engagement angles.”
- **Content for Live Display / Social**: Auto-generate short impact blurbs or “bid of the hour” captions optimized for conversion.

## Recommended Stack
- Primary: xAI Grok (vision + text) for consistency with CFL tooling preference.
- Fallback / specialized: OpenAI GPT-4o for vision when needed; simple embeddings + vector store for donor similarity.
- All AI calls should be queued (Horizon) and results stored as JSON so the system remains usable if the model is offline.
- Human-in-the-loop for any outreach that leaves the system (emails, calls). AI drafts; interns approve.

## Quick Wins (Phase 2–3)
1. Wire the existing AiLotAnalyzer to a real vision endpoint.
2. Add a “Suggest next action” button on the donor profile that returns a short LLM-generated recommendation.
3. On the public progress page, show an AI-generated one-sentence impact summary that updates with the live total.
