SocialJi

Accepts content (text, images, video) and schedules posts across Instagram, Twitter, LinkedIn, and TikTok from a single dashboard using platform-specific API integrations (Meta Graph API, Twitter API v2, LinkedIn Share API, TikTok Content API). Manages posting queues with timezone-aware scheduling, conflict detection, and batch publishing to reduce manual cross-platform posting overhead. Architecture likely uses a job queue (Redis/Bull or similar) with platform-specific adapters that translate unified content models into platform-native formats.

Analyzes uploaded image/video content and generates marketing-focused captions using a fine-tuned language model (likely GPT-3.5 or similar), with template selection for content type (product launch, promotional, educational, engagement-focused). Captions are generated with hashtag suggestions and emoji recommendations. Architecture uses vision-language model for image understanding, then applies content-type-specific prompt templates to shape tone and messaging. Likely includes basic keyword injection for SEO/discoverability.

Provides a calendar interface (likely month/week/day views) displaying scheduled posts across all connected platforms with drag-and-drop rescheduling. Supports team member assignment, comment threads for feedback, and approval gates before publishing. Architecture uses real-time synchronization (WebSocket or polling) to reflect changes across team members' views, with role-based access control (admin/editor/viewer) and audit logs for compliance. Integrates with the scheduling queue to prevent conflicts when posts are moved.

Generates content ideas based on trending topics, user-provided keywords, or past high-performing posts using a language model with access to trend data (likely from Twitter Trends API, Google Trends, or proprietary trend aggregation). Suggests content angles, formats (carousel, video, static), and optimal posting times. Architecture likely uses prompt engineering with trend context injection and ranking by predicted engagement (based on historical performance data). May include competitor analysis to suggest content gaps.

Aggregates engagement metrics (likes, comments, shares, impressions, reach) from connected social platforms via their native analytics APIs (Meta Insights, Twitter Analytics, LinkedIn Analytics, TikTok Analytics). Displays metrics in dashboard charts (line graphs, bar charts) with date range filtering and platform comparison views. Architecture uses scheduled data pulls (likely daily or hourly) to sync metrics into a local database, then renders visualizations. Likely includes basic KPI tracking (engagement rate, reach growth) but lacks advanced cohort analysis or attribution modeling.

Analyzes content (image, caption, keywords) and recommends relevant hashtags using a combination of keyword matching, semantic similarity, and hashtag popularity/trend data (likely from Twitter Trends API, Instagram Explore, or proprietary hashtag databases). Provides metrics for each hashtag (search volume, competition level, trend velocity) to help users choose high-discoverability tags. Architecture likely uses vector embeddings for semantic matching and periodic crawling of platform hashtag data to maintain current popularity scores.

Provides free access to core features (scheduling, caption generation, basic analytics) with usage quotas (e.g., 5 posts/month, 1 connected account, limited API calls) and paid upgrade tiers unlocking higher limits and premium features. Architecture uses quota tracking middleware to enforce limits per user account, with clear upgrade prompts when limits are approached. Designed to reduce friction for solopreneurs and startups testing workflows before committing financially.