reem-plus/quiver-compass

A production-grade, open-source framework for automating deep GTM research, competitive landscape mapping, and strategic execution workflows — powered by Claude Code plugins and OpenAI API orchestration. Inspired by the pioneering work in agentic market research, this repository reimagines how teams surface intelligence, reduce analysis paralysis, and accelerate revenue decisions.
## 🧭 Table of Contents
1. Why GTM Compass AI Exists
2. Architecture & Mermaid Diagram
3. Key Features & Capabilities
4. Example Profile Configuration
5. Example Console Invocation
6. OpenAI & Claude API Integration
7. Emoji OS Compatibility Table
8. Responsive UI & Multilingual Support
9. 24/7 Customer Support & Automation
10. Disclaimer & Ethical Use
11. License
## Why GTM Compass AI Exists
The modern go-to-market (GTM) landscape is a dense jungle of fragmented data, shifting competitor moves, and noise that drowns signal. Traditional research tools treat market analysis like a static snapshot, but markets are living organisms — they breathe, pivot, and evolve. GTM Compass AI is not another research scraper; it is an autonomous intelligence engine that transforms raw, chaotic data into structured, actionable GTM narratives.
Think of it as a digital cartographer for your market strategy. Instead of handing you a map drawn in ink, we give you a living atlas that redraws itself with every competitive move, every pricing change, and every customer sentiment shift. This approach emerged from observing how top-tier GTM teams spend 60% of their time in research and only 40% in execution. We invert that ratio.
Built with the philosophical DNA of agentic plugins and deep research pipelines, GTM Compass AI operates on three core principles:
Signal over noise — It filters out the 2026's relentless data churn to surface the one insight that changes your Q3 strategy.
Speed without shallowness — Analysis that once required a week of analyst work completes in under 12 minutes.
Actionable narratives, not reports — You don't need a 40-page PDF. You need a clear, strategic fork in the road.
## Architecture & Mermaid Diagram
The system is built on a modular, event-driven architecture that separates data ingestion, intelligence processing, and output delivery into discrete, swappable layers. Each plugin operates as an autonomous sub-agent, coordinated by a central orchestrator inspired by the Claude Code plugin paradigm.
mermaid
flowchart TD
A[User Config & Profile] --> B[Orchestrator Agent]
B --> C[Competitive Scanner Plugin]
B --> D[Market Signal Analyzer]
B --> E[Customer Sentiment Crawler]
B --> F[Pricing Intelligence Module]
C --> G[Raw Data Lake]
D --> G
E --> G
F --> G
G --> H[LLM Processor - Claude / OpenAI]
H --> I[Strategy Synthesizer]
I --> J[Output Layer]
J --> K[Console Dashboard]
J --> L[Slack / Webhook]
J --> M[PDF Export]
J --> N[GTM Action Cards]
style A fill:#e3f2fd,stroke:#1e88e5
style G fill:#fff3e0,stroke:#fb8c00
style H fill:#f3e5f5,stroke:#8e24aa
style J fill:#e8f5e9,stroke:#43a047
The architecture is intentionally stateless to allow horizontal scaling. Each plugin can be run independently for targeted research, or chained together for comprehensive market campaigns. The Orchestrator Agent uses a priority-weighted queue to ensure high-signal items are processed first, mimicking how a senior analyst would triage tasks in a 2026 competitive intelligence war room.