ChimeraMatrix

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  ChimeraMatrix is the fusion of martech and fintech applying quantitative methods to content performance.

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  Analyzing Video：AI analyzing the uploaded video and extracting features.

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  Generated Strategy：Automatically generated cover, title, description, hashtags, and posting time based on video analysis.

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  3. Backtest Result：Performance prediction with time-series charts, CTR estimates, and matched similar videos.

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  Saved Strategy & Backtest Result Report：Saved reports that allow creators to revisit, compare, and iterate on different strategies.

Inspiration

The inspiration for ChimeraMatrix came from the creators and brand partners around me. Publishing a video has become increasingly complicated, yet all creators really want is to focus on creating.

I watched YouTubers, Shorts creators, and brand influencers spend countless hours on repetitive but critical tasks: generating titles, thumbnails, tags, posting times, SEO descriptions, and planning strategy. None of this work is creative or enjoyable, yet it directly affects video performance.

Some friends would stay up late producing dozens of thumbnail and title variations just to optimize one upload. That’s when I realized: if AI can automate this entire packaging and strategy workflow, creators could reclaim their time.

More importantly, I wanted a system that not only generates strategy but can also evaluate it, backtesting performance expectations and previewing how a video might appear in YouTube’s recommendation feed.

ChimeraMatrix was born from this need: an AI-driven assistant that handles the optimization, analysis, and strategic decisions, so creators can focus their energy on what they do best-creating.

What it does

ChimeraMatrix analyzes video content, generates optimized publishing strategies, and predicts performance based on historical data.

It provides creators with:

1. Automated strategy generation (cover, title, hashtags, posting time)

2. Infinite regeneration of strategy components

3. Similarity-based matching against historical examples

4. Time-series performance prediction

5. Clear explanations of performance drivers

6. Saved reports for comparison and iteration

How I built it

This project was built using Kiro’s structured development workflow.

Kiro Specs automatically generated a full requirement document and converted it into task lists, letting us build the backend in a clean, modular, and consistent way.

I created two agent hooks to enforce code quality—one ensuring minimal, clean implementation, and another preventing unnecessary file creation or over-engineering.

Agent steering helped maintain context, summarize goals, and streamline multi-step workflows.

The backend runs on serverless functions and follows a clear pipeline: video upload → feature extraction → strategy generation → similarity matching → backtest → report saving. The frontend was built with React, shadcn/ui, and Recharts.

Challenges I ran into

Designing prompts that consistently extract structured content metadata

Balancing multimodal analysis speed with serverless time limits

Ensuring regenerated strategies remain logically aligned with video context

Building meaningful similarity matching with limited sample data

Keeping architecture clean while iterating quickly under hackathon time constraints

Accomplishments that I'am proud of

Delivering a fully end-to-end product experience within hackathon time

Maintaining a clean and modular backend thanks to Kiro agent hooks

Building a quant-style backtesting engine for content strategy

Creating a UI workflow that feels intuitive and creator-friendly

Using Kiro’s tools to stay organized and avoid over-engineering

What I learned

Structured workflows dramatically reduce mental load during rapid prototyping

Consistent metadata extraction is crucial for meaningful downstream predictions

Backtesting concepts translate surprisingly well to creator analytics

Kiro’s automatic requirement + task generation ensures clarity and alignment throughout development

What's next for ChimeraMatrix

Expanding the historical dataset to improve similarity and prediction accuracy

Adding support for more platforms and longer-form videos

Providing real-time A/B testing for covers and titles

Introducing creator dashboards for multi-video insights

Building APIs for third-party creator tools

Exploring monetization and brand-collaboration prediction modules

Built With

• agent
• csv-data-pipeline
• geminiapi
• jest-+-fast-check
• kiro
• kiro-agent-hooks
• kiro-specs
• multimodal-ai-models
• python
• react
• recharts
• shadcn/ui
• tailwind-css
• vercel-serverless-functions
• vite