SceneWatch - Autonomous Retail Shelf Compliance Vision Agent

Brands pay field reps to walk stores and photograph shelves to check if products are placed correctly — in April 2026 that is a $40/hour human doing a job a phone camera and a vision model can do in 30 seconds. SceneWatch is the autonomous compliance agent that replaces the clipboard.

𝕏 Post Reddit HN

Difficulty

advanced

What is it?

Consumer packaged goods brands spend millions annually on retail execution audits to verify shelf placement, pricing compliance, and planogram adherence. The current workflow is a field rep with a phone app, a clipboard checklist, and a two-day reporting lag. SceneWatch gives brands a mobile upload flow where any rep (or even the store manager) snaps a shelf photo, and the vision pipeline instantly checks product presence, facing count, price tag validity, and out-of-stock flags against the expected planogram. Anomalies trigger instant alerts to the brand manager. No field rep training required — the model does the analysis. Priced at $99/month per brand per region, targeting emerging CPG brands doing 50-500 store audits monthly who cannot afford Salesforce Consumer Goods Cloud.

Why now?

Roboflow released its fast inference API in late 2025 making real-time shelf analysis affordable for indie builders, and Claude Vision API now handles planogram comparison without custom model training — the stack that makes this $99/month instead of $150k/year only exists right now.

▸Mobile photo upload with instant shelf analysis posted back in under 30 seconds
▸Planogram compliance score showing facing count, out-of-stock flags, and price tag anomalies
▸Anomaly alert email to brand manager with annotated photo showing exactly what is wrong
▸Store audit history dashboard with compliance trend by region over time

Target Audience

Emerging CPG brands doing 50-500 retail store audits monthly — roughly 15,000 US brands in this tier per Nielsen retail data.

Example Use Case

A hot sauce brand manager uploads 20 shelf photos from a weekend promotion, SceneWatch flags 4 stores where the product is stocked in the wrong aisle, and the brand corrects placements before losing a week of sales velocity.

User Stories

▸As a CPG brand manager, I want to upload a shelf photo and receive a compliance score in 30 seconds, so that I catch placement errors before they cost a week of sales.
▸As a field rep, I want to snap a photo in-store and get instant pass/fail feedback, so that I resolve issues on the spot instead of filing a report.
▸As a regional manager, I want a dashboard showing compliance trends across 50 stores, so that I can identify which regions need more audit attention.

Acceptance Criteria

Photo analysis: done when compliance score and anomaly list appear within 30 seconds of upload. Anomaly alert: done when email with annotated photo fires within 60 seconds of anomaly detection. Dashboard: done when audit history loads with compliance trend chart for last 30 days. Billing: done when Stripe trial converts to paid and access continues uninterrupted.

Is it worth building?

$99/month x 60 brands = $5,940 MRR at month 4. $99/month x 200 brands = $19,800 MRR at month 10. Assumes 3% conversion of cold outreach to emerging CPG brand managers.

Unit Economics

CAC: $40 via LinkedIn cold outreach. LTV: $1,188 (12 months at $99/month). Payback: 1 month. Gross margin: 82%.

Business Model

Per-brand SaaS subscription, $99/month per brand region

Monetization Path

14-day free trial converts at 18% when brand managers see first anomaly alert catch a real pricing error.

Revenue Timeline

First dollar: week 4. $1k MRR: month 3. $5k MRR: month 7.

Estimated Monthly Cost

Railway (FastAPI): $30, Roboflow inference: $40, Claude Vision API: $50, Supabase: $25, Resend: $10, Stripe fees on $3k MRR: $90. Total: ~$245/month.

Profit Potential

Full-time viable at $6k MRR, achievable within 8 months via CPG Slack communities and LinkedIn outreach.

Scalability

High — brand count and region count scale independently, white-label tier adds enterprise revenue.

Success Metrics

Week 2: 10 brands in free trial. Month 1: 15 paying brands. Month 4: 60 paying brands at less than 10% monthly churn.

Launch & Validation Plan

Post in r/CPGindustry and LinkedIn CPG groups asking if retail compliance audits are a pain — collect 20 responses before writing any model code.

Customer Acquisition Strategy

First customer: find 20 emerging CPG brand managers on LinkedIn who post about retail execution, send a cold DM with a 15-second screen recording of SceneWatch catching a planogram violation on a sample shelf photo. Ongoing: LinkedIn content targeting CPG brand managers, Fancy Food Show virtual exhibitor outreach, ProductHunt launch.

What's the competition?

Competition Level

Medium

What's the roadmap?

Feature Roadmap

V1 (launch): photo upload, compliance score, anomaly alerts, audit dashboard. V2 (month 2-3): planogram upload UI, store comparison view. V3 (month 4+): white-label, automated rep dispatch, competitor price detection.

Milestone Plan

Phase 1 (Week 1-2): Roboflow pipeline and FastAPI returning compliance scores on test photos. Phase 2 (Week 3-4): mobile upload, alert emails, Stripe billing live. Phase 3 (Month 2): dashboard with trend charts, 15 paying brands.

How do you build it?

Tech Stack

React Native for mobile upload, FastAPI, OpenCV, Roboflow for shelf object detection, Claude Vision API for planogram comparison, Supabase, Railway — build with Cursor for backend, Lovable for dashboard

Suggested Frameworks

Roboflow Inference, OpenCV, FastAPI

Time to Ship

4 weeks

Required Skills

Roboflow model training, FastAPI, React Native, Claude Vision API.

Resources

Roboflow shelf detection datasets, Claude Vision API docs, React Native image picker docs.

MVP Scope

mobile/App.tsx, mobile/UploadScreen.tsx, api/main.py, api/shelf_analyzer.py (Roboflow + Claude Vision), api/planogram_checker.py, api/alert_dispatcher.py, dashboard/pages/audits.tsx, dashboard/pages/alerts.tsx, supabase/migrations/001_init.sql, README.md.

Core User Journey

Sign up -> upload planogram image -> rep snaps shelf photo in store -> compliance score appears in 30 seconds -> anomaly alert fires to brand manager email -> upgrade after first caught violation.

Architecture Pattern

Rep uploads shelf photo via mobile -> Supabase Storage -> FastAPI triggers -> Roboflow detects products -> Claude Vision compares to planogram -> compliance score computed -> anomaly alert email via Resend -> audit result stored in Supabase.

Data Model

Brand has many Regions. Region has many Stores. Store has many Audits. Audit has photo URL, compliance score, anomalies array, created at. Planogram belongs to Region.

Integration Points

Roboflow Inference for shelf object detection, Claude Vision API for planogram comparison, Supabase Storage for photos, Supabase DB for audit records, Resend for alerts, Stripe for billing.

V1 Scope Boundaries

V1 excludes: automated rep dispatch, price comparison against competitor products, video audit mode, and white-label branding.

Success Definition

A CPG brand manager the founder has never met signs up, uploads real shelf photos, receives an anomaly alert that catches a real placement error, and pays for month two.

Challenges

The hardest non-technical problem is convincing brand managers to change their existing audit workflow — product must show a catch on the first demo photo or the deal dies.

Avoid These Pitfalls

Do not try to train a custom shelf detection model from scratch in v1 — use Roboflow pretrained models or you lose 3 weeks. Do not launch without a live demo shelf photo that produces a dramatic anomaly catch. Finding first 10 paying brands takes 3x longer than building the vision pipeline.

Security Requirements

Supabase Auth with Google OAuth. RLS on all brand audit data. Supabase Storage bucket policies restricting access to brand owner only. Input validation on photo upload (file type and size). GDPR deletion endpoint for uploaded photos.

Infrastructure Plan

FastAPI on Railway (2 vCPU). Supabase for DB and photo storage. Vercel for dashboard. GitHub Actions for CI. Sentry for error tracking. Total: ~$95/month infra.

Performance Targets

Compliance score returned within 30 seconds of photo upload. Dashboard load under 2s. Roboflow inference under 5 seconds. Support 500 photo uploads per day at launch.

Go-Live Checklist

☐Security audit complete
☐Payment flow tested end-to-end
☐Error tracking live
☐Monitoring dashboard configured
☐Custom domain set up
☐Privacy policy published
☐5 beta brands signed off
☐Rollback plan documented
☐LinkedIn demo video and ProductHunt post drafted.

How to build it, step by step

1. Set up a Roboflow project and use the pretrained retail shelf detection model to identify products and facings. 2. Build a FastAPI endpoint that accepts a shelf photo URL and returns detected product positions. 3. Implement a planogram checker that compares detected positions against an uploaded reference image using Claude Vision API. 4. Compute a compliance score (0-100) based on out-of-stock count and facing delta. 5. Build a React Native app with image picker and upload to Supabase Storage. 6. Trigger analysis pipeline on upload complete and store results in Supabase. 7. Send anomaly alert email via Resend when compliance score drops below threshold. 8. Build a Next.js dashboard showing audit history, compliance trend charts, and annotated anomaly photos. 9. Integrate Stripe for $99/month subscription with 14-day free trial. 10. Deploy FastAPI to Railway, Next.js to Vercel, and post demo video on LinkedIn CPG groups.

Generated

April 10, 2026

Model

claude-sonnet-4-6

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