MarketMatrix: Autonomous SEO Content Optimization Engine - Integration Practice of Multi-Agent Evaluation and Iterative Experiments

MarketMatrix (Content Forge) is an autonomous SEO content optimization system integrating fine-tuned large language models, multi-dimensional scoring engines, and automated research loops. Its core innovation combines Karpathy's Autoresearch mode and MiroFish's multi-agent simulation framework to enable overnight iterative experiments for content self-evolution, addressing traditional manual optimization limitations in the dynamic search ecosystem.

In the generative AI-driven search era, SEO, AEO, and GEO boundaries are merging. Traditional content optimization relies on manual experience and static rules, struggling to adapt to evolving algorithms. MarketMatrix was built as an autonomous evolutionary system, replacing manual adjustments with a machine-driven experiment-evaluation-feedback loop to explore optimal content forms via hundreds of iterations.

Content Generation Layer: Uses Qwen2.5-1.5B base model, fine-tuned via LoRA to internalize Caleb Ulku's SEO framework. Steps: data prep (293 examples, ~287K tokens from Caleb Vault), training with Unsloth, export to GGUF for Ollama deployment. Multi-dimensional Scoring Engine: 5 weighted dimensions: SEO (30%: keyword layout, density, title structure), AEO (25%: capsule content, question H2s), GEO (25%: citation value, fact density), Voice (10%: readability, short sentences), Competitive (10%: vs competitor win rate). Automated Research Loop: Karpathy's Autoresearch model: generate variants → evaluate → commit if score improves/revert otherwise → repeat hundreds of times overnight (git records history).

MarketMatrix has 149 agent portraits in 4 categories: platform agents (simulate search/answer/generative engines), consumer agents (user intents), competitor agents (generate competitor content), expert agents (domain expert views). Currently as JSON configs, supporting Competitive dimension evaluation. Future plan: integrate MiroFish OASIS for multi-agent simulation (Phase3).

Phase1 (Completed): 5-dimensional scoring engine (baseline 71.74/100), Autoresearch loop (Ollama+git), training data pipeline, 149 agent portraits. Phase2-3 (Planned): MiroFish OASIS integration, Neo4j knowledge graph, evolution distiller (retrain via tournament winners), three-layer engine (SEO/AEO/GEO dedicated models).

Tech Stack: Content generation (Qwen2.5-1.5B + LoRA via Unsloth/PEFT), model service (Ollama local), scoring engine (Python rule engine), optimization loop (Autoresearch+git), agent config (JSON portraits). Hardware: NVIDIA GPU with 24GB VRAM (e.g., RTX3090/4090).

Open Source Acknowledgments: Thanks to karpathy/autoresearch (experiment loop reference) and nikmcfly/MiroFish-Offline (agent portrait inspiration). Practical Value: 1. Shift from manual to machine optimization;2. Unified SEO/AEO/GEO evaluation framework;3. Transparent process (rubric scoring + git history);4. Domain expert knowledge internalization via fine-tuning. Industry Significance: Provides data-driven path for content marketing, local SEO, GEO strategies.