Maestro — A Complete AI Orchestration IP Package
A full production-grade orchestration engine that learns and reuses human judgment — offered as a complete IP and codebase.
MAESTRO — The AI Orchestration Engine That Remembers Why Your Content Worked
Full-stack AI orchestration IP package — not a prototype, but a deployable system that remembers your creative logic.
🧭 Market Context
The creator economy is shifting from automation tools to decision memory systems.
Maestro is not another AI app — it’s the first engine that lets you reuse your past reasoning.
💡 Why It Matters
AI tools are everywhere — but none of them remember why your content worked. Every creator repeats the same cycle: search trends → write → post → react → repeat.
Maestro breaks that loop. It’s a memory engine that captures your creative decisions and replays them automatically.
⚡ Why Now
Every month, 20,000+ AI tools launch — yet 99% fail to scale because they forget context. Maestro fixes this by making automation judgment-aware.
It’s the missing layer between LLM output and human reasoning — and it’s fully built.
🧩 What You’re Buying
Maestro remembers why your content worked.
A production-grade orchestration system that turns LLM prompts into structured, deterministic workflows. Fully built with FastAPI + React, ready to deploy with one command.
| Layer | What It Does | Tech |
|---|---|---|
| Backend | Deterministic DAG executor, Persona/Playbook memory, Graph RAG watcher | FastAPI, Celery, pgvector |
| Frontend | Chat-based orchestration UI (Ctrl + K anywhere), realtime card interface | React + Tailwind |
| Storage | Vector & object storage | PostgreSQL + SeaweedFS |
| Deployment | Docker Compose ready | Prometheus + Redis |
🧠 Core Innovations
Graph RAG Memory
Remembers why each post worked — connecting Trends ↔ Drafts ↔ Posts ↔ Comments. Real-time synchronization via Celery sidecar (< 5 min latency).
Deterministic Orchestration
Every action is traceable and reproducible. DAG executor + idempotent operators ensure predictable automation.
Adaptive Flow Chaining
New features emerge by recombining existing ones — typically < 50 LOC to implement a new capability.
Persona Playbooks
Your tone, brand, and results are stored as executable memories — reusable across campaigns.
🎯 Target Users
Independent creators, solo founders, and AI-native marketers who want to automate judgment, not just clicks.
Maestro acts as your “second brain for decisions.” It remembers your voice, tone, and metrics — and plays your rhythm again.
Who It’s Not For
- Large marketing teams requiring centralized data and CRM control
- Organizations optimizing for shared KPIs → Maestro is built for individual ownership of judgment, not corporate coordination.
🔁 Key Use Cases
| Use Case | Description |
|---|---|
| Trend → Draft → Publish | Deterministic campaign execution with full replayability |
| Comment → Response Template | Converts comment clusters into reply templates automatically |
| Reactive Automation | Keyword → Tag → Auto DM/Comment in 5-min loops |
🧩 Architectural Value: Recombination > Addition
Maestro’s architecture treats features like Lego blocks — new functions arise from combining existing flows.
- Flow Chaining Engine : detects combinable flows automatically (6 adapter patterns → 134 flow combinations)
- LLM Isolation Principle : logic is deterministic; LLM handles only language generation
- Graph RAG Watcher : keeps 8 domain tables in vector + graph sync
- Auto Embedding Pipeline : no manual RAG management — sync in < 30 s
Result: measurable development gains
- 60 % faster composite-feature development
- LLM calls cut by > 70 %
- Stable replayable automation
🧱 Tech Stack Overview
Backend (FastAPI + Celery)
- DAG Executor / DSL / Planner / Persona Context
- pgvector + TEI Embeddings (768-D)
- Graph RAG Nodes × Edges × Chunks with auto-sync
- Adapter pattern for Threads & Instagram (Graph API v23)
- Reactive Engine / CoWorker / Sniffer / Scheduler
Frontend (React + Tailwind + TypeScript)
- Chat-based orchestration UI ( Ctrl + K )
- Card-rendered actions & timeline
- Graph Explorer (Interactive RAG visualization)
- Playbook Dashboard + Trend Correlation Charts
Infrastructure
- PostgreSQL 16 + pgvector
- SeaweedFS / MinIO object store
- Redis 5 / Celery 5.4
- Docker Compose local deployment
- Prometheus metrics + Slack alert hooks
📊 Business Value
| Key Metric | Maestro Advantage |
|---|---|
| Development Cost | < 50 LOC to add a new flow (recombination > addition) |
| LLM Cost | LLM used only for content generation; rules handle logic |
| Extensibility | Add new adapters or platforms in hours (Threads, Instagram ready) |
| Maintainability | 80 % of new features built from existing components |
| Scalability | 8 domain tables auto-synced via Graph RAG Watcher |
✅ Proof of Implementation
- 17 + backend modules (Orchestrator, CoWorker, Sniffer, Reactive, Graph RAG)
- 14 + frontend features (Playbooks, Graph Explorer, Trends, Drafts)
- 6 adapter patterns → 134 flow combinations
- Realtime Graph RAG sidecar sync < 5 min
💰 Sale Terms
Type: Full IP + Codebase transfer (Frontend + Backend + Documentation) Price: $8,900USD
Includes:
- Full-stack source code (FastAPI + React + Docker)
- Deployment instructions & architecture docs
- Lifetime right to modify and commercialize
- Graph RAG + Deterministic DAG engine
Founder / Architect: Minwoo Yu (유민우) 📧 snowypainter@gmail.com