AI-Powered Review Dispute SaaS Development

I need a full-stack developer who is comfortable blending AI, automation, and SaaS architecture to turn my product vision into a production-ready platform. The core of the application is a multi-client online reputation management tool that plugs directly into the Google Business Profile API, watches every incoming review in real time, weighs that content against Google’s Terms of Service, and—when a 1- or 2-star review appears to violate policy—launches an automatic flag-and-dispute sequence.

Two must-have integrations drive the build: native connection to the Google Business Profile API for ingestion and dispute submission, and smooth data exchange with popular customer-management systems so each client can match reviews to their own CRM records without manual work. The system must run under a single managed instance but let me spin up branded, white-label portals for different agencies or franchises.

I’m expecting modern, well-documented code (Python or Node welcomed), containerised deployment, and a clean admin UI that makes it simple to on-board new businesses, set alert thresholds, and track dispute outcomes.
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AI-Powered Online Reputation Management (ORM) Platform

Product Requirements & Technical Specification
Overview
The platform is a SaaS-based AI ORM tool that integrates directly with the Google Business Profile API to continuously monitor client reviews, analyze them against Google's Terms of Service, and automatically trigger a flagging and dispute workflow to challenge and seek removal of policy-violating 1- and 2-star reviews. It is designed to serve multiple business clients under a single managed instance, with a white-label option.

Core Modules
1. Google Business Profile API Integration Layer

OAuth 2.0 authentication flow for each client business account
Bi-directional API connection to Google Business Profile (GBP) using the Google My Business API v4+
Webhook or polling-based subscription to real-time review events
Bulk historical ingestion of existing reviews on initial account connect (filtered to 1 and 2 stars)
Secure token storage and refresh lifecycle management
Support for multi-location businesses (multiple GBP listings per client)
2. AI Review Analyzer

LLM-based analysis engine (recommended: GPT-4o or Claude Sonnet via API) applied to every qualifying review
Analysis tasks per review:
Detect fake or incentivized reviews (policy violation)
Detect spam or off-topic content
Detect conflict of interest indicators (competitor, ex-employee, etc.)
Detect harassment, hate speech, or personal attacks
Detect irrelevant content (wrong business, accidental review)
Detect missing first-hand experience signals
Confidence scoring per violation type (0–100)
Structured output: violation flags, evidence text, recommended action
3. Violation Detection Engine

Maintained rules database mapping Google T&C clauses to detectable signals
Rules updated as Google policy evolves (admin-manageable)
Each flagged review receives: violation category, confidence threshold met/not met, evidence excerpt, recommended flag type
Configurable confidence thresholds (default: flag at 75%+, escalate to human review below threshold)
Deduplication logic to prevent re-flagging already actioned reviews
4. Automated Dispute Engine

Upon violation confirmation, automatically submits a flag via the Google Business Profile API using the appropriate flag reason code
Generates a structured dispute rationale document per review (for cases that require manual escalation outside the API)
Tracks dispute status per review: Pending → Submitted → Under Review → Resolved / Rejected
Retry and escalation logic: if flag is rejected, re-analyze and escalate to next action tier
Escalation tiers: (1) automated API flag, (2) auto-generated manual appeal draft, (3) human review queue
Optional: Google Maps review flagging via browser automation (Playwright/Puppeteer) for cases where API access is limited
5. Client Dashboard (Web UI)

Per-client view of all monitored GBP listings
Review feed filtered to 1–2 star reviews with status badges (Analyzing / Flagged / Under Review / Removed / Upheld)
Case detail view: original review text, AI analysis breakdown, violation flags, dispute timeline
Analytics panel: flagging success rate, removal rate, average resolution time, review trend over time
Alert configuration: notify client on new low-star review, status change, or successful removal
Multi-location toggle for businesses with multiple GBP listings
Supporting Modules

Notification System

Email and SMS alerts triggered by: new qualifying review received, dispute status change, review successfully removed
Webhook support for integrating with third-party CRMs or Slack
Audit Log

Full immutable log of every action taken per review (ingestion timestamp, AI analysis result, flag submitted, status updates)
Exportable as CSV or PDF for client reporting
Multi-Client Management (Agency Layer)

Admin portal for managing multiple client accounts
Per-client billing hooks (Stripe or similar)
White-label branding option (custom domain, logo, color scheme)
Technical Stack Recommendations

Layer Recommended Options
Backend Node.js (Express/Fastify) or Python (FastAPI)
Database PostgreSQL (structured data) + Redis (queue/cache)
AI/LLM OpenAI GPT-4o API or Anthropic Claude API
Google Integration Google My Business API v4, OAuth 2.0
Job Queue BullMQ, Celery, or AWS SQS
Frontend React + Tailwind CSS
Hosting AWS, GCP, or Vercel + Railway
Auth Auth0 or Clerk (multi-tenant SaaS)
Browser Automation (optional) Playwright or Puppeteer
Key Technical Constraints & Considerations

Google's API does not provide a direct "request removal" endpoint — the platform flags reviews via the available report/flag API surface and tracks status changes. Engineers should architect around this constraint.
Google T&C violation categories must be mapped to available flagType values in the API.
The AI analysis layer must be designed to produce defensible, explainable outputs — not just a binary flag — to support any manual escalation path.
Rate limiting on the Google API must be handled gracefully with backoff logic.
PII handling: review text and business data must be stored with appropriate encryption at rest and in transit (SOC 2 alignment recommended for enterprise clients).
Phases

Phase 1 — MVP Single client, manual OAuth connect, historical review ingestion, AI analysis pipeline, basic dispute flagging, simple status dashboard.

Phase 2 — Automation & Scale Multi-client support, automated webhook ingestion, full dispute engine with retry logic, notifications, audit log.

Phase 3 — Agency Layer White-label option, multi-client admin portal, analytics, billing integration, SLA reporting.

Deliverables Expected from Engineer / Team

Architecture plan and tech stack proposal
API integration approach for Google Business Profile (and any limitations identified)
LLM pipeline design for the review analysis module
Estimated timeline and milestone breakdown by phase
Fixed-bid or hourly rate quote per phase

I’m happy to discuss architecture, preferred frameworks, and milestone structure once we connect. Show me something similar you’ve shipped and let’s get started. Show More