---
title: "JigsawStack Alternative for Structured Data | EnConvert"
description: "EnConvert is a JigsawStack alternative for structured data that returns clean Markdown from web pages and files without extraction prompts to define or maintain"
canonical: "https://www.enconvert.com/solutions/jigsawstack-alternative-for-structured-data-enconvert"
locale: "en"
---

# Best JigsawStack Alternative for Structured Data Extraction

A developer building a RAG pipeline or AI agent needs content from web pages and files returned as clean, LLM-ready output, without defining extraction schemas or managing a multi-product platform. JigsawStack is a multi-product AI platform whose AI Scraper takes a URL plus natural language prompts naming the elements to extract, and returns structured JSON shaped by those prompts. That means the developer defines and maintains what to extract for each source type before any content reaches the pipeline. For a developer who needs clean Markdown from a web page or chunked JSONL from a PDF, that definition step is overhead between the input and the vector store. EnConvert is a focused REST API and MCP server with dedicated endpoints for web scraping, file ingestion, document conversion, and summarisation, all returning consistent LLM-ready output with no prompts or field definitions required, so you get clean content from any input type in one call, ready for an agent or vector store.

## Example

```python
import os

import requests

PAGE_URL = os.environ["PAGE_URL"]  # the web page URL to extract, e.g. a pricing page

# JigsawStack: define element prompts (max 5), receive structured JSON
js_response = requests.post(
    "https://api.jigsawstack.com/v1/ai/scrape",
    headers={
        "x-api-key": os.environ["JIGSAWSTACK_API_KEY"],
        "Content-Type": "application/json",
    },
    json={
        "url": PAGE_URL,
        "element_prompts": ["Plan title", "Plan price", "Plan features"],
    },
    timeout=60,
)
js_response.raise_for_status()
js_result = js_response.json()
if not js_result.get("success"):
    raise RuntimeError(f"JigsawStack scrape failed for {PAGE_URL}")

# "context" maps each element prompt to the text matched for it
structured = js_result["context"]
print(structured)

# JSON shaped by your prompts, which you define and maintain per source type

# EnConvert: one POST, clean Markdown of the page, no prompts required
ec_response = requests.post(
    "https://api.enconvert.com/v2/perceive",
    headers={
        "X-API-Key": os.environ["ENCONVERT_API_KEY"],
        "Content-Type": "application/json",
    },
    json={
        "url": PAGE_URL,
        "outputs": ["markdown"],
    },
    timeout=60,
)
ec_response.raise_for_status()
perceived = ec_response.json()

# A blocked or failed read comes back without a markdown output
if perceived.get("error") or "markdown" not in perceived.get("outputs", {}):
    raise RuntimeError(
        f"Perceive failed for {PAGE_URL}: "
        f"{perceived.get('error') or perceived.get('warnings')}"
    )

# The Markdown is a signed artifact URL (valid 15 minutes). Plain GET, no API key.
markdown_response = requests.get(perceived["outputs"]["markdown"]["url"], timeout=60)
markdown_response.raise_for_status()
markdown = markdown_response.text

# No prompts or fields to define, ready to chunk and embed
print(markdown)
```

## Output

| Capability | EnConvert | JigsawStack |  |
| --- | --- | --- | --- |
| Extraction prompts required | None; no fields or prompts to define | Element prompts required for each extraction |  |
| Web page extraction | Perceive: any URL to clean Markdown, including JavaScript-rendered pages; Ingest also accepts URL lists, sitemaps, and website crawls returning the same JSONL chunk schema as file ingestion | AI Scraper: URL or raw HTML to structured JSON based on your prompts |  |
| LLM-ready output format | Consistent Markdown schema across all endpoints | Structured JSON shaped by the element prompts you define |  |
| File ingestion (PDF, DOCX, XLSX) | Ingest: PDF, DOCX, XLSX, CSV, Markdown, and plain text; chunks split at H1 to H3 boundaries, 512 word soft cap, tables and code blocks kept whole | Not documented for PDF and DOCX ingestion |  |
| Document conversion | Convert: 45+ conversion routes across documents, images, and data formats | Not documented as a dedicated conversion endpoint |  |
| Summarisation endpoint | Distill: any URL or file to a clean summary in one call |  | Summary API for text or PDF input (paragraph or bullet points) |
| MCP server | Native, exposes full ingestion toolkit as MCP tools | MCP server available with an AI scraper tool |  |
| Free tier | 500 ops per month, no card required | 1 million tokens per month on the Free plan |  |

**WHY ENCONVERT FOR STRUCTURED DATA EXTRACTION**

A no-prompt approach means EnConvert returns clean, structured Markdown from any input without requiring the developer to define or maintain extraction instructions as sources change. The output format is the same across web pages and files, so the code that loads your vector store does not branch by source type. The same API also covers ingestion, conversion, and summarisation as the pipeline grows.

## Get started

[Get free API key — 500 ops per month, no card required.](https://www.enconvert.com/auth?mode=signup)

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