AI Reviews

Aggregates customer reviews from disparate sources (Google Reviews, Facebook, Trustpilot, native review sites) into a unified dashboard using API connectors to each platform's review endpoints. The system normalizes review metadata (rating, timestamp, reviewer info, platform source) into a common schema and displays them in a single interface, enabling businesses to monitor feedback across channels without switching between platforms.

Uses a language model (likely GPT-3.5 or similar) to auto-generate contextually-aware responses to customer reviews by analyzing review text, rating, and sentiment, then applying business-provided brand voice templates and tone guidelines. The system generates draft responses that can be edited before posting, with optional one-click approval for high-confidence responses. Implementation likely uses prompt engineering with review context injection and template variable substitution rather than fine-tuned models.

Generates customizable HTML/CSS/JavaScript widgets that display aggregated reviews directly on a business website without requiring backend changes. The system provides a visual builder to configure widget appearance (layout, colors, fonts, review count), generates embed code, and handles widget data fetching via client-side API calls to EmbedSocial's CDN. Widgets support multiple display modes (carousel, grid, list) and lazy-load reviews to minimize page performance impact.

Analyzes review text to extract sentiment polarity (positive/negative/neutral), assigns topic tags (e.g., 'product quality', 'shipping speed', 'customer service'), and flags reviews for priority handling (e.g., urgent negative reviews). Implementation likely uses pre-trained NLP models or LLM-based classification with prompt engineering, categorizing reviews into business-relevant buckets without requiring manual tagging. Results are used to surface high-priority reviews and inform response generation.

Implements a review response management workflow where AI-generated responses are held in a draft state, reviewed by authorized team members, and posted to source platforms via API calls. The system supports bulk approval of multiple responses, scheduling posts for optimal engagement times, and tracking response metrics (approval time, posting status, platform-specific errors). Uses role-based access control to restrict approval permissions and maintains an audit log of all responses posted.

Handles OAuth 2.0 authentication flows for connecting to review platforms (Google Business Profile, Facebook, Trustpilot, etc.), securely stores and refreshes access tokens, and manages API rate limits and quota tracking per platform. The system abstracts platform-specific authentication requirements (e.g., Google's service account vs Facebook's app token) into a unified connection interface, automatically refreshing expired tokens and handling authentication errors gracefully.

Provides aggregated analytics on review volume, rating distribution, sentiment trends, and response metrics across all connected platforms. The dashboard displays time-series charts (reviews over time, average rating trends), comparison views (platform-by-platform performance), and exportable reports (CSV/PDF). Analytics are computed from aggregated review data and updated daily or on-demand, with optional email report scheduling.