Contentr - AI Content Marketing Automation

AI agent that automates content marketing workflows using TiDB vector search - from competitor analysis to content creation, saving 90% of planning time.

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Inspiration

Marketing teams waste 10+ hours weekly on repetitive content tasks - researching competitors, planning calendars, and analyzing performance. The TiDB AgentX Hackathon's focus on multi-step agentic AI was the perfect opportunity to automate this entire workflow with a truly autonomous system.

What it does

Contentr is an AI agent that automates content marketing using TiDB Serverless vector search. It runs a 6-step autonomous workflow:

  1. Data Ingestion - Pulls social content and generates OpenAI embeddings
  2. Vector Search - Uses TiDB cosine similarity to find content gaps
  3. AI Analysis - GPT-4 analyzes patterns and opportunities
  4. Strategy Generation - Creates optimized content calendars
  5. Performance Tracking - Monitors engagement metrics
  6. Continuous Optimization - Refines strategy based on results

The system delivers 90% time savings (10+ hours → 1 hour weekly) and 40% better engagement through data-driven insights.

How we built it

Backend: FastAPI with TiDB Serverless for vector search, Redis for caching, OpenAI GPT-4 for analysis

Frontend: Next.js 14 with TypeScript, Tailwind CSS styling

Key TiDB Integration:

-- Content similarity search
SELECT content_text, engagement_rate
FROM content_posts
ORDER BY VEC_COSINE_DISTANCE(content_embedding, :query_embedding)
LIMIT 10;

Architecture: Multi-step workflow where vector search results feed AI analysis, which generates strategy, creating true autonomous decision-making.

Challenges we ran into

Performance Optimization: Vector similarity searches needed optimization for real-time analysis. Implemented TiDB vector indexes to improve query performance.

Workflow Coordination: Managing 6 autonomous steps required careful state management and error handling between components.

Accomplishments that we're proud of

True Multi-Step Autonomy: Goes beyond simple chatbots to demonstrate genuine autonomous decision-making across 6 workflow steps

Advanced TiDB Usage: Leverages vector indexes, full-text search, JSON columns, and serverless auto-scaling

Production Quality: Complete with API documentation, live demos, and scalable architecture

Real Business Impact: Quantifiable 90% time savings and 40% engagement improvements

What we learned

Vector Search Optimization: Learned how to effectively use TiDB's vector capabilities for content similarity matching

Agentic AI Patterns: Building autonomous systems requires robust workflow orchestration, not just LLM chains

Real-World Applications: Successful AI products need deep domain understanding alongside technical implementation

What's next for Contentr - AI Content Marketing Automation

Short-term: Enhanced error handling, additional platforms (YouTube, TikTok), basic A/B testing

Medium-term: Team collaboration workflows, custom templates, more scheduling integrations

Focus on refining the core agentic workflow and gathering user feedback. Open-source MIT license enables community contributions.

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