Production-Grade Agentic AI Pipeline for Automated Viral Clip Generation
Transform long-form video content into publication-ready social media clips using an enterprise-grade hybrid AI architecture that combines specialized ML models, intelligent orchestration, and strategic LLM integration.
Not another video splitter. This is how modern AI systems process video at scale.
CLIPER takes a single long-form video and automatically generates multiple optimized social media clips with:
- Intelligent Segmentation - Understands narrative structure to identify natural clip boundaries
- Agentic Caption Generation - Multi-step AI reasoning that produces contextually appropriate captions for each clip
- Quality Assurance - Validates every output against brand and content guidelines
- Real-Time Computer Vision - Uses machine learning models to understand frame composition and intelligently optimize framing for vertical video formats without subject cropping
- Subtitle Synchronization - Precise word-level subtitle alignment
- Batch Processing - Handles 30+ clips simultaneously without cascading failures
The result: A complete suite of publication-ready clips, each with optimized captions and formatting, ready to publish across TikTok, Instagram Reels, YouTube Shorts, and other social platforms.
CLIPER isn't a collection of separate tools. It's an integrated pipeline where each component informs the next.
Long-Form Video
↓
Transcription & Analysis
↓
Semantic Understanding
↓
Intelligent Segmentation
↓
Agentic Caption Generation
↓
Quality Validation
↓
Computer Vision Optimization
↓
Video Engineering & Export
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Publication-Ready Clips
CLIPER transcribes video content with precision word-level timing. This foundation enables everything that follows - from semantic understanding to subtitle synchronization.
Why This Matters: Most clip generators use generic transcription. CLIPER's approach ensures captions are perfectly timed to speech patterns.
Rather than arbitrary fixed-duration chunks, CLIPER understands narrative structure. It identifies natural topic boundaries and thematic shifts in your content.
This produces clips that feel intentional, not algorithmic.
CLIPER analyzes the semantic structure of your content to identify optimal clip boundaries. This ensures each generated clip is a self-contained narrative unit rather than a random cut.
This is where CLIPER demonstrates true AI sophistication.
Rather than template-based caption generation, CLIPER uses an agentic AI system that:
- Classifies each clip's narrative context and optimal style
- Reasons about what caption style will maximize engagement
- Generates contextually appropriate captions
- Validates every caption against brand guidelines
- Refines outputs that don't meet quality standards
This multi-step reasoning approach produces captions that are simultaneously on-brand, engaging, and contextually appropriate.
Every output is validated against a comprehensive set of brand and content guidelines. Captions are checked for:
- Brand compliance (required hashtags, approved terminology)
- Length constraints (optimized for platform specifications)
- Content appropriateness
- Call-to-action presence
Only outputs that pass validation are included in the final publication set.
Converting horizontal video to vertical formats is a classic computer vision problem. Static center-crop approaches fail: they cut off speakers, waste frame space, and produce unprofessional results.
CLIPER implements a machine learning-based solution that:
- Detects key visual elements in real-time (faces, subjects, focal points)
- Analyzes frame composition to understand optimal framing
- Predicts subject movement and anticipates reframing needs
- Optimizes crop windows dynamically throughout the clip
The result is vertical content that maintains professional framing, keeps subjects centered, and adapts intelligently to movement. This is computer vision applied to the video content problem - not just cropping, but understanding.
This represents a significant advancement over naive aspect-ratio conversion approaches and demonstrates CLIPER's commitment to ML-first solutions rather than heuristic shortcuts.
The final stage combines all previous analysis into a complete video engineering pipeline. Each clip is:
- Temporally precise (frame-accurate cutting)
- Aspect-ratio optimized
- Subtitle-synchronized
- Audio-balanced
- Codec-optimized for platform delivery
CLIPER's architecture combines several advanced technologies, each chosen for specific capabilities:
Speech Processing Advanced speech recognition with temporal precision, enabling word-level subtitle synchronization.
Natural Language Understanding Deep semantic analysis of content to identify narrative structure and thematic boundaries.
Agentic AI Architecture Multi-step reasoning system for intelligent caption generation and quality validation. Not simple templates - actual AI reasoning about your content.
Computer Vision Real-time visual understanding for intelligent frame composition and vertical video optimization.
Video Engineering Professional-grade video processing with multi-input handling, codec optimization, and subtitle burn-in.
Runtime Validation Every output passes through a validation layer that enforces business rules and brand guidelines before publication.
The video clip generation space is crowded with simple solutions - fixed-duration chunking, keyword-based segmentation, template captions. They're easy to build and cheap to deploy. They're also obviously automated and low-quality.
CLIPER takes a different approach:
Local-First Intelligence - Core ML models run locally for privacy and cost efficiency. Only strategic reasoning tasks use cloud APIs.
Graceful Degradation - If some captions don't meet validation standards, that's okay. Better to publish 27 excellent clips than 30 mediocre ones.
Modular Architecture - Each stage can be updated or configured independently without affecting the rest of the pipeline.
Production Resilience - The system is designed to handle edge cases, partial failures, and resume from any stage if interrupted.
Intelligent Clip Generation - Not random cuts. CLIPER uses semantic understanding to generate clips at natural narrative boundaries
AI-Reasoned Captions - Every caption is generated through multi-step agentic reasoning, not templates. Contextually appropriate and brand-compliant
Computer Vision Optimization - Professional vertical video framing powered by real-time machine learning, not naive cropping
Word-Level Subtitle Synchronization - Precision timing that matches speech patterns exactly
Automated Quality Validation - Pydantic-based validation ensures every output meets your content standards before publication
Scalable Batch Processing - Process 30+ clips simultaneously with graceful degradation (no cascading failures)
Production-Ready Infrastructure - Built for reliability, observability, and resumability. Designed to handle edge cases and partial failures
git clone https://github.com/opino-tech/cliper.git
cd cliper
uv sync
export GOOGLE_API_KEY=your_key
uv run cliper.py- Python 3.9+
- FFmpeg
- macOS or Linux (Docker available for other platforms)
Set environment variables to customize behavior:
GOOGLE_API_KEY # Required for AI caption generation
WHISPER_MODEL # Transcription model size
MIN_CLIP_DURATION # Minimum clip length in seconds
MAX_CLIP_DURATION # Maximum clip length in secondsFor reproducible, containerized deployment:
docker-compose build
docker-compose run cliper- Input - Provide a YouTube URL or local video file
- Processing - CLIPER processes the video through its pipeline
- Review - Generated clips appear in the output directory
- Publish - Use clips directly on social platforms
CLIPER organizes output by content style:
output/
├── viral/ # High-engagement, hook-driven clips
├── educational/ # Value-focused, educational clips
└── storytelling/ # Narrative-driven, emotional clips
Each clip includes:
- Optimized video file
- Synchronized subtitles
- Metadata (captions, timings, styling information)
If processing is interrupted, CLIPER can resume from the last successful stage without reprocessing.
Complete visibility into what succeeded, what failed, and why.
Define your own brand guidelines and content rules. CLIPER enforces them automatically.
Process 30+ clips simultaneously with no manual intervention.
No clips generated
- Video may be too short or lack clear topic boundaries
- Try adjusting clip duration settings
Transcription issues
- Ensure video has clear audio
- Check FFmpeg is installed correctly
API errors
- Verify GOOGLE_API_KEY is set correctly
- Check API quota and rate limits
CLIPER is built with modularity in mind. Each component can be:
- Configured for specific use cases
- Extended with custom logic
- Integrated into larger systems
- Modified for specialized workflows
For architecture documentation, see:
- TECHNICAL-STACK.md - Deep technical analysis
- pasoxpaso/contextofull.md - Complete architecture context
Speech Recognition - Advanced transcription with temporal precision
Natural Language Processing - Semantic content analysis and understanding
Agentic AI - Multi-step reasoning for intelligent caption generation
Computer Vision - Intelligent frame composition and visual optimization
Video Processing - Professional-grade video engineering
Runtime Validation - Automated quality assurance and compliance checking
MIT License
CLIPER is a production-grade AI system developed by opino.tech, powered by AI CDMX.
This is professional-grade infrastructure designed for production use at scale. Not a tutorial, not a demo - this is how modern video AI systems are built.
Ready to transform your video content?
For more information, documentation, or to discuss custom implementations, reach out to the opino.tech team.
Built with production systems in mind.