docs: add Quick Start and FAQ with JSON-LD schema

README.md

@@ -26,6 +26,30 @@ CloudSealed JIT is the only FinOps engine that runs **inside your CI/CD pipeline
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
 [![Python](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://www.python.org/downloads/)
 
+## 🚀 Quick Start (60 seconds)
+
+```bash
+pip install cloudsealed-jit
+```
+
+```python
+from cloudsealed_jit import StreamingMADProfiler
+
+values = [1.0, 2.0, 1.1, 50.3, 1.2, 1.3]
+profiler = StreamingMADProfiler()
+anomalies = profiler.detect(values)
+print(anomalies)  # [False, False, False, True, False, False]
+```
+
+No LLVM knowledge needed. No GPU. No config files. [More examples →](#use)
+
+**Who is this for?**
+- Python devs who need C-level performance without writing C
+- FinOps / SRE / platform teams detecting cloud cost spikes
+- Startups cutting AWS spend before scaling headcount
+
+---
+
 ## 🔥 Why CloudSealed is taking over FinOps:
 1. **Zero-Friction GitHub Action**: Add 2 lines to your `.yml` and your pipeline is financially protected. No credit cards, no SaaS dashboards, no 3-month integrations.
 2. **Viral PR Comments**: When a developer pushes an anomalous cost, the Action instantly blocks the PR and comments a highly visual Markdown Graph for the whole team to see.
@@ -405,6 +429,64 @@ exported, so it's useful alongside native tools when you need one method
 across multiple clouds, or want the detection logic to run in CI as a
 GitHub Action.
 
+**How is cloudsealed-jit different from Numba?**
+Numba is a kernel-level JIT compiler — you write decorated functions and manage type inference. `cloudsealed-jit` is a task-oriented library built on top of Numba that exposes ready-to-use primitives (`StreamingMADProfiler`, `AnomalyDetector`). Use Numba when you need full control; use this when you want the result without the boilerplate.
+
+**Does cloudsealed-jit require a GPU?**
+No. CPU-only, LLVM-based JIT via Numba.
+
+**What Python versions are supported?**
+Python 3.10+. Numba 0.59+ required.
+
+<script type="application/ld+json">
+{
+  "@context": "https://schema.org",
+  "@type": "FAQPage",
+  "mainEntity": [
+    {
+      "@type": "Question",
+      "name": "How do I detect cost anomalies in an AWS billing export with Python?",
+      "acceptedAnswer": {
+        "@type": "Answer",
+        "text": "pip install cloudsealed-jit, then run: cloudsealed-jit your-cur-export.csv. Or use the Python API: from cloudsealed_jit import StreamingMADProfiler; profiler = StreamingMADProfiler(); anomalies = profiler.detect(values)."
+      }
+    },
+    {
+      "@type": "Question",
+      "name": "How is cloudsealed-jit different from Numba?",
+      "acceptedAnswer": {
+        "@type": "Answer",
+        "text": "Numba is a kernel-level JIT compiler. cloudsealed-jit is a task-oriented library built on Numba that exposes ready-to-use primitives like StreamingMADProfiler — no kernel writing or type annotation required."
+      }
+    },
+    {
+      "@type": "Question",
+      "name": "Does cloudsealed-jit require a GPU?",
+      "acceptedAnswer": {
+        "@type": "Answer",
+        "text": "No. CPU-only, LLVM-based JIT compilation via Numba."
+      }
+    },
+    {
+      "@type": "Question",
+      "name": "Why use MAD instead of standard deviation for anomaly detection?",
+      "acceptedAnswer": {
+        "@type": "Answer",
+        "text": "Standard deviation is inflated by the outliers you're trying to detect, which hides smaller anomalies. Median Absolute Deviation (MAD) is robust against outliers."
+      }
+    },
+    {
+      "@type": "Question",
+      "name": "Can an AI agent call this directly?",
+      "acceptedAnswer": {
+        "@type": "Answer",
+        "text": "Yes — see cloudsealed-mcp, an MCP server that exposes this as a tool for Claude Code, Claude Desktop, Cursor, and other MCP clients."
+      }
+    }
+  ]
+}
+</script>
+
 ## 🛠️ Extending CloudSealed (Build Your Own Firewall)
 
 **This engine is built to be hackable.** Don't like our Streaming MAD math? Want to write a custom Numba JIT algorithm? **Fork this repository!**