SurgeGuard

Predict & prevent university store POS crashes with smart alerts and staffing boosts.

Score: 8.2/10SSHard BuildReady to Spawn
Brand Colors

The Opportunity

Problem

POS systems in university stores crash during peak hours, resulting in long lines and lost sales.

Solution

Monitors Retailtech POS metrics in real-time, predicts peak overloads 30min ahead using sales data. Alerts managers via app/SMS to add registers/staff, with auto-suggested shifts. Dashboard shows historical peaks for better planning.

Target Audience

University campus store managers, staff, and operators relying on Retailtech POS systems

Differentiator

AI-powered prediction just for uni stores, integrating Retailtech APIs for proactive crash avoidance.

Brand Voice

edgy

Features

Real-time Monitoring

must-have12h

Track POS load, queue length via API.

Peak Prediction

must-have18h

ML model forecasts crashes based on time/sales.

Alert System

must-have10h

SMS/app push for predicted surges.

Staff Scheduler

must-have14h

Suggest extra shifts based on predictions.

Dashboard Insights

must-have12h

Visual peak history and prevention ROI.

Custom Thresholds

nice-to-have8h

Set store-specific alert triggers.

Team Sharing

nice-to-have6h

Share alerts with staff group.

Report Exports

nice-to-have7h

PDF/CSV peak reports.

Total Build Time: 87 hours

Database Schema

stores

ColumnTypeNullable
iduuidNo
api_keytextNo

Relationships:

  • one-to-many predictions

metrics

ColumnTypeNullable
iduuidNo
store_iduuidNo
load_levelintNo
timestamptimestampNo

Relationships:

  • foreign key stores.id

predictions

ColumnTypeNullable
iduuidNo
store_iduuidNo
predicted_peaktimestampNo
risk_leveltextNo

Relationships:

  • foreign key stores.id

API Endpoints

POST
/api/stores/:id/metrics

Ingest POS data

🔒 Auth Required
GET
/api/predictions/:storeId

Get alerts

🔒 Auth Required
POST
/api/alerts/send

Trigger SMS

🔒 Auth Required

Tech Stack

Frontend
Next.js 14 + Tailwind + shadcn/ui + Recharts
Backend
Next.js API + Vercel AI
Database
Supabase Postgres
Auth
Supabase Auth
Payments
Stripe
Hosting
Vercel
Additional Tools
Twilio SMSSupabase Edge Functions for ML

Build Timeline

Week 1: Core & DB

20h
  • Auth
  • Metrics ingest

Week 2: Prediction model

25h
  • Simple ML via Vercel AI

Week 3: Dashboard & alerts

20h
  • Charts
  • SMS

Week 4: Scheduler & test

15h
  • Shift suggest
  • E2E test
Total Timeline: 4 weeks • 95 hours

Pricing Tiers

Free

$0/mo

1 store

  • Basic monitor

Pro

$15/mo

3 stores

  • Predictions
  • Alerts

Enterprise

$99/mo

None

  • Unlimited
  • Custom ML

Revenue Projections

MonthUsersConversionMRRARR
Month 1604%$36$432
Month 650011%$825$9,900

Unit Economics

$45
CAC
$400
LTV
6%
Churn
82%
Margin
LTV:CAC Ratio: 8.9xExcellent!

Landing Page Copy

Stop POS Crashes Before They Start

Predict peaks, alert teams—save sales in uni stores.

Feature Highlights

Live monitoring
30min predictions
Smart alerts
Staff scheduler
Peak insights

Social Proof (Placeholders)

"'Predicted every rush!' - Mgr"

First Three Customers

Target Retailtech users via their forums/support tickets on outages, offer free prediction setup. LinkedIn connect with 'POS reliability' uni managers.

Launch Channels

Product Huntr/EdTechr/SaaSTwitter #RetailTech

SEO Keywords

university POS predictorretailtech crash alertcampus store peak monitor

Competitive Analysis

$69+
Strength

Full POS

Weakness

No prediction

Our Advantage

Add-on predictor cheap

🏰 Moat Strategy

Historical peak data network effect per uni.

⏰ Why Now?

AI tools make predictions cheap; uni sales booming.

Risks & Mitigation

executionhigh severity

API integration delays

Mitigation

Mock data first

Validation Roadmap

pre-build10 days

Validate API access

Success: 3 stores share data

Pivot Options

  • Gym peak manager
  • Restaurant rush predictor

Quick Stats

Build Time
95h
Target MRR (6 mo)
$1,200
Market Size
$180.0M
Features
8
Database Tables
3
API Endpoints
3