Two days ago, our prospecting pipeline looked like this:
- 1 Agent sending emails with a fixed template
- 19 emails sent, 0 replies
- No idea if anyone opened them
- No idea what our bounce rate was
- Send and forget
Two days later, it looks like this:
- 4 Agents, each responsible for a different product line
- AI-personalized opening lines for every email (Gemini)
- Real-time tracking of opens, clicks, and bounces
- Automatic A/B testing of subject lines — winners get promoted
- Auto-blacklisting bounced domains
- Most importantly: it learns from its own results and gets smarter every day
Cost? $0.
The Problem: Spray and Pray
We have 4 AI products to promote: MindThread (social media automation), UltraProbe (AI security scanner), Ultra Advisor (financial advisory platform), and Agent Fleet (AI agent platform).
Our first approach was brute force: Brave Search for targets → scan their website → send a template email.
Result? 0% reply rate.
Because every email looked exactly the same. Recipients could smell the mass-send from a mile away.
Architecture: 5 Stages × 4 Agents × Self-Learning
Discover → Qualify → Scan → Outreach → Nurture
↑ ↓
└──── Learner (analyze results, adjust strategy) ←────┘
Stage 1: Discover
Each Agent has its own Brave Search keyword set. Runs 3x daily (10:00, 15:00, 20:00), searching 3 keyword groups per run to find new SMBs.
Cross-agent dedup ensures no domain gets emailed by two different Agents.
Stage 2: Qualify
Fetches the target's HTML and extracts signals:
- Contact email found (+30 points)
- Matches target industry (+40 points)
- Has title and meta description (+10 each)
- Learned industry bonus (Learner adjusts based on open rates: +20 or -10)
Tier C and D targets are skipped — don't waste bullets.
Stage 3: Scan
Runs UltraProbe SEO and AEO scans. The scan result IS the sales material.
"Your website scored 43/100 on SEO — here are 5 issues hurting your Google ranking" is 100x more effective than "We offer SEO services."
Stage 4: Outreach (AI-Personalized Cold Email)
This is where the biggest upgrade happened.
Before: "We analyzed xxx.com.tw and found some issues." (identical for every recipient)
After: Gemini 2.5 Flash generates for each email:
- Personalized opening (mentions specific issues on their site)
- Custom subject line
- Targeted P.S.
Plus 3 A/B variants running simultaneously:
| Variant | Strategy | Example |
|---|---|---|
| Score | Show the grade | "example.tw SEO Health Check: D — Free Report" |
| Issue | Emphasize problems | "5 issues found affecting your Google ranking" |
| Competitor | Social pressure | "Your competitors outrank you in SEO" |
The Learner tracks which variant gets the highest open rate and automatically increases its selection probability.
Stage 5: Nurture (Auto Follow-Up)
- 3 days after sending, no open → resend with different subject line
- Opened but no click after 5 days → send a case study value-add
Real-Time Tracking: Resend Webhooks
Every email's lifecycle is tracked:
Sent → Delivered → Opened → Clicked
↘ Bounced → Auto-blacklist
↘ Complained → Auto-unsubscribe
We receive Resend webhook events (Svix signature verification), write to Firestore in real-time, then:
- 👀 Someone opened → instant Telegram notification
- 🔥 Someone clicked → Telegram + which link they clicked
- ❌ Bounced → auto-add to blacklist, never send again
- 🚨 Spam complaint → auto-unsubscribe permanently
Not hourly polling. The same second it happens.
The Core Innovation: Self-Learning Engine
This is the most valuable part of the entire system.
After each pipeline run, prospect-learner.js analyzes all webhook data and writes to learned-config.json. The next pipeline run reads this config and automatically adjusts behavior.
6 Learning Modules
1. A/B Weight Auto-Adjustment
Not simple "A won, only use A." It's weighted random:
30% open rate → weight 3.0x (3× more likely to be selected)
15% open rate → weight 1.5x
0% open rate → weight 0.1x (nearly eliminated, but keeps exploration space)
2. Smart Send Time
Tracks opens per hour (UTC+8). If 2 PM has the highest open rate, the system knows.
3. Industry Success Rates
"Restaurant industry: 5 sent, 0 opens" → next qualify score automatically -10.
"E-commerce: 3 sent, 2 opens" → qualify score +20.
The system naturally shifts toward industries that convert.
4. Scoring Calibration
If Tier B prospects actually get higher open rates than Tier A, the system flags "scoring model needs recalibration."
5. Bounce Pattern Detection
If the same domain pattern (e.g., .gov.tw) bounces 3+ times, it's auto-added to the skip list.
6. Lookalike Discovery
Identifies industries with the highest open rates, suggests new search keywords to expand the target pool.
4 Agents, Clear Division of Labor
| Agent | Product | Daily Cap | Qualify/Run | Scan/Run |
|---|---|---|---|---|
| Probe | UltraProbe Security Scanner | 40 | 60 | 30 |
| MindThread | Threads Automation SaaS | 25 | 50 | 25 |
| Advisor | AI Financial Advisory | 20 | 40 | 20 |
| Main | Agent Fleet Platform | 15 | 30 | 15 |
| Total | 100/day | 180 | 90 |
3 runs/day = theoretical capacity of 540 qualifications + 270 scans + 100 emails.
Dashboard: Full Visibility
The admin panel's Prospecting Panel shows real-time:
- Conversion Funnel: Targets → Qualified → Scanned → Emailed → Replied → Converted
- Email Performance: Sent, Delivered, Opened, Clicked, Bounced, Complaints (with percentages)
- Per-Agent Stats: Each Agent's funnel data
- Recent Emails: Latest sent emails with status icons
All updated in real-time, no waiting for daily reports.
Daily Telegram Report
Every night at 8 PM, an auto-generated Telegram report:
🦞 4-Agent Daily Report (2026-03-21)
Probe: 12 emails
→ sinyi.com.tw
→ lativ.com.tw
→ accupass.com
...
MindThread: 8 emails
→ dalang.tw
→ eztable.com
...
Total: 25 emails
Global sent: 63 domains
Not just numbers — exactly who was emailed.
The Learner also sends its own report:
🧠 Learner Report #3
📊 A/B Winners:
probe: score (2.4x)
mindthread: fomo (3.0x)
🏭 Top Industries:
ecommerce: 35% open (12 sent)
education: 28% open (7 sent)
⏰ Best Hour: 14:00
🚫 Bounce patterns: 2
Gen 3
Cost Structure
| Item | Cost |
|---|---|
| Resend email | $0 (free 100/day) |
| Gemini personalization | $0 (free 200/day) |
| UltraProbe scans | $0 (our own product) |
| Brave Search | $0 (free API) |
| Firestore | $0 (within free quota) |
| Vercel serverless | $0 (Hobby plan) |
| Human labor | $0 (fully autonomous) |
| Total | $0/month |
Tech Stack
- Engine: Node.js (prospect-engine.js, 776 lines)
- Learner: Node.js (prospect-learner.js, 344 lines)
- Webhook: TypeScript Vercel Serverless (resend-webhook.ts, 418 lines)
- Frontend: React + TypeScript (ProspectingPanel.tsx, 350 lines)
- AI: Gemini 2.5 Flash (personalization) + Ollama 7B (reply classification)
- Data: Firebase Firestore
- Notifications: Telegram Bot API + Resend Webhooks (Svix)
- Deployment: Vercel + WSL2 systemd timers
What's Next
The system is live and running. Now we let the data accumulate and observe the Learner's curve.
Metrics we're watching:
- A/B winner stability by Generation 10
- Whether industry preferences converge
- Whether bounce rate keeps declining
- Whether optimal send time stabilizes
This isn't a static sales tool. It's a system that evolves.
Every run, it learns a little more about what works and what doesn't. No one needs to tell it what to change — it reads the data and adjusts its own parameters.
This is what we believe AI sales systems should look like.
Built by Ultra Lab — the company that builds AI products with AI.
Want a free scan of your website? ultralab.tw/probe
Originally published on Ultra Lab — we build AI products that run autonomously.
Try UltraProbe free — our AI security scanner checks your website for vulnerabilities in 30 seconds: ultralab.tw/probe
