GeoGuard Local SEO Monitor

Business

Original Idea

Local SEO Site Monitor A background service with a simple web dashboard that audits listings, reviews, and NAP drift before traffic drops.

Product Requirements Document: GeoGuard Local SEO Monitor

Version: 1.0
Status: Draft
Author: Senior Product Manager
Date: May 20, 2024 (Projected for 2026 Stack)

1. Executive Summary

GeoGuard Local SEO Monitor is a proactive, multi-tenant SaaS platform designed for digital marketing agencies and franchise managers. It automates the monitoring of "NAP" (Name, Address, Phone) data consistency and review sentiment across 10+ major directories. By utilizing a real-time drift detection engine and AI-powered sentiment analysis, GeoGuard prevents the search visibility drops and customer trust erosion caused by unauthorized listing changes or neglected customer feedback.

2. Problem Statement

Local search rankings are heavily dependent on the "Prominence" and "Relevance" of business data. When NAP data drifts—due to unauthorized directory edits, platform bugs, or human error—search engines lose confidence in the business's location, leading to immediate ranking drops. Furthermore, 88% of consumers trust online reviews as much as personal recommendations, yet manual review management across multiple platforms (Google, Yelp, Facebook) is unscalable for agencies managing dozens or hundreds of locations.

3. Goals & Success Metrics

  • Drift Detection Accuracy: Maintain >99% accuracy in identifying NAP discrepancies.
  • Time-to-Alert: Reduce the time between a listing change and user notification to under 60 minutes.
  • User Engagement: Achieve an average Review Response Rate of >80% for managed locations.
  • Churn Reduction: Help agencies reduce client churn by providing automated "Health Reports" that prove ongoing value.

4. User Personas

  • Agency Mark (Digital Marketing Agency Owner): Manages 50+ clients. Needs whitelabel reporting and a bird's-eye view of all location health scores.
  • Franchise Fran (Operations Manager): Manages 20 corporate-owned locations. Needs to ensure brand consistency and standard response times for reviews.
  • Consultant Cal (Local SEO Freelancer): Needs a low-cost, high-efficiency tool to monitor a handful of high-stakes clients.

5. User Stories

  • As an Agency Owner, I want to connect a client's Google Business Profile (GBP) so that I can automatically import all location data as the "Source of Truth."
  • As a Marketing Manager, I want to receive a Slack notification when a Yelp listing phone number differs from our GBP data so that I can correct it before it impacts SEO.
  • As a Business Owner, I want to see an AI-generated draft response for a 3-star review so that I can respond professionally and quickly.
  • As an Agency Manager, I want to provide my clients with a whitelabeled dashboard under my own domain so that I can reinforce my brand value.

6. Functional Requirements

6.1 Data Core

  • GBP Integration: Bi-directional sync with Google Business Profile API v1.
  • NAP Auditing: Background workers must scrape/query 10+ sources (Yelp, Bing, Facebook, YellowPages, etc.) every 24 hours.
  • Source of Truth (SoT): Users must be able to lock specific fields (Name, Address, Phone, Website) as the SoT.

6.2 Monitoring & Alerts

  • Drift Engine: Automated comparison between SoT and external directory data.
  • Real-time Notifications: Webhook-based alerts for Slack, Discord, and Email via SendGrid.
  • Sentiment Analysis: Automated categorization of reviews (Positive, Neutral, Negative) using local NLP models.

6.3 Dashboard & Reporting

  • Health Score: A proprietary algorithm (0-100) based on NAP consistency, review velocity, and response rate.
  • Review Feed: A unified inbox for all reviews across all platforms.
  • PDF Reports: Monthly automated performance reports featuring health score trends and drift corrections.

7. Technical Requirements

7.1 Tech Stack (2026 Standards)

  • Frontend: Next.js v16.1.3 (Stable), utilizing Partial Prerendering (PPR) and proxy.ts for multi-tenant routing.
  • Styling: Tailwind CSS v4 + Shadcn UI (Radix Primitives).
  • Backend: NestJS Microservices architecture.
  • Background Workers: BullMQ with Redis Streams, running on AWS Fargate Spot (Graviton 4 instances).
  • Database: PostgreSQL with TimescaleDB (Tiger Data) for high-performance audit history and columnar compression.
  • Authentication: Clerk for Organizations and Enterprise SSO (SAML).
  • AI/ML: Transformers.js for local sentiment analysis and Vercel AI SDK 5.x for LLM response generation (GPT-5/Claude 4).

7.2 Architecture Patterns

  • Tenant Isolation: Row-Level Security (RLS) at the PostgreSQL level.
  • Scraping Strategy: Distributed worker nodes with "Sandboxed Processors" (useWorkerThreads: true) to prevent event-loop blocking.
  • Sync Pattern: Real-time event-driven sync via Google Cloud Pub/Sub for GBP updates.

8. Data Model

8.1 Entity: BusinessLocation

  • id: UUID (Primary Key)
  • org_id: String (Clerk Org ID)
  • name: String (Source of Truth)
  • address_json: JSONB (Street, City, State, Zip)
  • phone: String
  • website: String
  • health_score: Integer (0-100)

8.2 Entity: AuditSnapshot (TimescaleDB Hypertable)

  • timestamp: TIMESTAMPTZ (Primary Key)
  • location_id: UUID
  • platform: Enum (Yelp, Bing, etc.)
  • captured_nap: JSONB
  • drift_detected: Boolean
  • drift_details: JSONB (e.g., {"field": "phone", "expected": "...", "found": "..."})

8.3 Entity: ReviewEntry

  • id: UUID
  • location_id: UUID
  • external_id: String (Platform-specific ID)
  • rating: SmallInt
  • content: Text
  • sentiment: Enum (Positive, Neutral, Negative)
  • reply_status: Enum (Unreplied, Drafted, Replied)

9. API Specification (Core Endpoints)

| Method | Endpoint | Description | | :--- | :--- | :--- | | GET | /v1/locations | List all locations for the current tenant. | | POST | /v1/locations/sync | Manually trigger a GBP data import. | | GET | /v1/audit/:locationId | Retrieve drift history (TimescaleDB query). | | POST | /v1/reviews/:id/generate-reply | Trigger AI response generation. | | PATCH | /v1/settings/white-label | Update tenant-specific branding metadata. |

10. UI/UX Requirements

  • NAP Audit Diff View: A side-by-side comparison UI. Left column: "Source of Truth"; Right column: "Directory Reality" with red highlight on discrepancies.
  • Geo-Grid Heatmap: A 5x5 grid overlay on a map showing local ranking "health" across coordinates.
  • Command Palette: Cmd+K functionality to jump between locations or trigger a manual audit.
  • Responsive Design: Mobile-first dashboard for business owners to respond to reviews on the go.

11. Non-Functional Requirements

  • Security: OAuth 2.1 implementation with mandatory PKCE and AES-GCM-256 encryption for all stored Google tokens.
  • Scalability: Fargate workers must auto-scale based on BullMQ waiting job count.
  • Performance: Dashboard LCP (Largest Contentful Paint) < 1.2s using Next.js PPR.
  • Availability: 99.9% uptime for the background monitoring service.

12. Out of Scope

  • Citation Building: We will not manually create new listings (directory submission).
  • Social Media Management: No posting or scheduling for Facebook/Instagram.
  • Review Gating: We will not provide tools to "filter" negative reviews before they are posted (violates Google TOS).

13. Risks & Mitigations

  • Risk: Google API Rate Limiting (10 edits per minute).
    • Mitigation: Implement BullMQ distributed rate limiting and exponential backoff.
  • Risk: Scraping targets blocking IPs.
    • Mitigation: Use a rotating proxy mesh and Fargate Spot instances to vary egress IPs.
  • Risk: AI Hallucinations in review responses.
    • Mitigation: Implement "DeepEval" unit testing for AI outputs and mandatory human-in-the-loop approval.

14. Implementation Tasks

Phase 1: Project Setup & Core Infra

  • [ ] Initialize Next.js 16.1.3 project with Tailwind CSS v4
  • [ ] Set up Clerk authentication with Organization support
  • [ ] Initialize NestJS monorepo for microservices
  • [ ] Configure TimescaleDB (Tiger Data) with Hypertable for AuditSnapshot
  • [ ] Set up Redis Cluster for BullMQ

Phase 2: GBP Integration & Sync

  • [ ] Implement OAuth 2.1 flow with PKCE and AES-GCM-256 encryption
  • [ ] Build GBP Location Import service (Google Business Profile API v1)
  • [ ] Set up Cloud Pub/Sub webhooks for real-time GBP updates
  • [ ] Create BusinessLocation CRUD API

Phase 3: Monitoring & Drift Engine

  • [ ] Develop Fargate Spot worker template using Graviton 4
  • [ ] Implement BullMQ "FlowProducer" for hierarchical directory audits
  • [ ] Build scraping logic for Yelp, Bing, and Facebook
  • [ ] Create the "Drift Detection" service (comparing SoT vs. Scraped data)
  • [ ] Implement Slack/Email notification triggers via SendGrid

Phase 4: AI & Reviews

  • [ ] Implement Review Aggregation service
  • [ ] Integrate Transformers.js for client-side/local sentiment analysis
  • [ ] Build "AI Response Generator" using Vercel AI SDK 5.x
  • [ ] Implement DeepEval for AI response quality monitoring

Phase 5: Dashboard & UI Patterns

  • [ ] Build "NAP Audit Diff" table using Shadcn UI
  • [ ] Implement "Geo-Grid Heatmap" using shadcn-map and React Leaflet
  • [ ] Build whitelabeling settings (custom logos/colors via publicMetadata)
  • [ ] Develop PDF Report generation service (Scheduled Fargate task)

Phase 6: Launch & Optimization

  • [ ] Configure Next.js PPR for dashboard performance
  • [ ] Set up Prometheus/Grafana monitoring for BullMQ health
  • [ ] Conduct security audit on OAuth token storage
  • [ ] Final end-to-end testing of tenant isolation (Postgres RLS)