PreSocial AI

No more guessing on what will go viral predict the performance of your content before you post and turn every upload into a data-backed decision.

Comment

Inspiration

Many of us have experienced this: spending 30 minutes drafting a LinkedIn post, only to spiral into doubt.

Will recruiters engage with this? Is this the right time to post? Does this sound impactful enough?

Or imagine a creator staring at a holiday photo before uploading it to Instagram:

Will this get traction? Should I rewrite the caption? Do I need better hashtags?

Creators constantly second-guess themselves before publishing.

We are living in the age of content where everyone has a voice but no one knows if anyone is listening. That uncertainty became the motivation behind this project.

What if you could predict engagement before posting backed by real data?

That question led to PreSocial AI a predictive creative intelligence platform powered by Presaige’s scoring APIs and an integrated LLM interpretation layer (Ollama).

No more guessing. No more second-guessing. Just confidence before you hit publish.

What It Does

PreSocial AI enables creators to optimize content before publishing through the following pipeline:

  1. Upload Content Before Posting Users can upload an image, video, or post draft for pre-publication analysis.

  2. Get a Predictive Virality Score

Using Presaige’s APIs, the system fetches:

  • buzz_index
  • wow_factor
  • elite_engagement_score
  • presaige_score

These signals are combined into an overall virality percentage that estimates engagement likelihood.

Users can then refine their strategy by selecting a target platform (LinkedIn, Instagram, Reddit expandable to more).

  1. AI Interpretation Layer (Ollama-Powered)

Raw numeric scores don’t help creators make decisions.

So I built an LLM interpretation layer using Ollama, which:

  1. Explains what the scores mean in simple language
  2. Suggests stronger hooks and captions
  3. Generates platform-specific viral hashtags
  4. Rewrites content for different platforms
  5. Answers custom user prompts about their post

For example:

Instead of:

“Buzz Index = 72.4”

You get:

“Your post has strong emotional appeal but lacks a compelling hook. Add a question in the first line to improve LinkedIn engagement.”

This transforms technical metrics into actionable strategy.

  1. Live Reddit Trend Intelligence (Real-Time Dataset)

To avoid static recommendations, I integrated Reddit’s Web API as a live trend engine.

The system:

  • Fetches trending posts using GET /r/{subreddit}/hot and GET /r/{subreddit}/new
  • Filters for high-engagement image-based content
  • Uses upvotes and interaction velocity as real-time engagement signals

This creates a dynamic dataset of what is currently performing well.

Structured trend signals are injected into the LLM layer, which analyzes:

  1. Visual patterns (composition, framing, color usage)
  2. Recurring storytelling themes
  3. Meme formats and humor styles
  4. Emotional triggers driving engagement

Instead of guessing trends, the system learns directly from live audience behavior.

  1. Presage Recommendations Integration In addition to predictive scoring, the system surfaces Presaige’s built-in recommendations, including:
  2. Missing engagement elements
  3. Structural improvements
  4. Creative enhancements
  5. Optimization opportunities

These insights are combined with LLM reasoning to produce platform-ready guidance.

How I Built It

Frontend

  1. Content upload interface
  2. Platform selector (LinkedIn / Instagram / Reddit)
  3. Virality score dashboard
  4. AI strategy and recommendations panel

Backend

  1. Python + Flask server
  2. Presaige Scoring API integration
  3. Presaige Recommendations API integration
  4. Reddit API integration for real-time trend data
  5. Ollama LLM for interpretation
  6. Custom prompt-engineering pipeline combining:
  • Predictive scores
  • Trend signals
  • Platform context
  • User custom prompts

The system orchestrates multiple APIs and structures their outputs into a single strategy engine.

Challenges I Ran Into

  1. Translating Scores into Strategy Predictive metrics are technical. Creators need human-readable insight. Designing an effective LLM prompt structure was critical.

  2. Real-Time Trend Alignment Fetching Reddit data dynamically while filtering noise and extracting meaningful signals required custom ranking logic.

  3. Platform Context Awareness Each platform has different engagement behavior, tone expectations, and formatting styles.

  4. Time Constraints This entire system was built in under 24 hours, integrating multiple APIs, live trend fetching, and LLM orchestration.

Accomplishments I’m Proud Of

  1. Built a true “predict-before-you-post” system that combines predictive analytics with generative AI
  2. Successfully integrated Presaige Scoring + Recommendation APIs into a unified strategy pipeline
  3. Designed and implemented a structured LLM interpretation layer using Ollama to convert raw metrics into actionable insights
  4. Developed a live Reddit trend intelligence engine to capture real-time engagement patterns
  5. Enabled platform-aware optimization tailored to Instagram, LinkedIn, Reddit, and X
  6. Architected a modular backend system designed for scalability and future multi-platform expansion
  7. Built the entire system solo, managing frontend, backend, API orchestration, prompt engineering, and system design independently

Building this as a solo developer in 24 hours wasn’t easy. Integrating multiple APIs, handling real-time data, designing meaningful LLM prompts, and ensuring everything worked seamlessly under time constraints was challenging. But through this process, I learned how to think architecturally, debug under pressure, design structured AI pipelines, and transform complex data systems into user-friendly experiences.

What I Learned

  • Data without interpretation is noise.
  • Predictive AI becomes powerful when paired with generative AI.
  • Real-time behavioral signals (like Reddit upvotes) are extremely valuable.
  • Platform-specific optimization is essential.
  • Execution speed and architectural clarity matter in hackathons.

What’s Next for PreSocial AI

  1. Automated Report Generation
  2. AI-generated performance summaries
  3. Personalized content improvement reports
  4. Trend alignment insights

  5. Posting schedule optimization

  6. Users would receive structured, data-driven publishing reports.

  7. Multi-Platform Expansion Next integrations:

  8. X (Twitter)

  9. YouTube Shorts

  10. TikTok

  11. Threads

Each platform will include:

  • Custom scoring weights
  • Platform-aware LLM prompting
  • Engagement pattern modeling
  1. Integrating multiple global languages so it supports more communities.

thank you!

Built With

Share this project:

Updates