mdstill
Firecrawl
Reducto
Mindee
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Parseflow.tech
Reducto
Mindee
ABBYY
mdstill is a document-ingestion tool purpose-built for LLM and RAG workflows. Where generic converters dump messy text, mdstill outputs clean, semantic markdown that preserves tables, headings, and document structure โ the things LLMs actually need to understand context.
What you can do with it:
Prepare documents for RAG pipelines (chunk-ready, semantic boundaries preserved) Feed PDFs, Word files, or spreadsheets into ChatGPT, Claude, or Gemini without losing tables Build knowledge bases in Obsidian, Notion, or Logseq from existing document archives Extract structured context for AI agents and embeddings How it's different: Deep-conversion mode runs layout-aware parsing (tables, OCR, multi-column PDFs) โ not just text dumping. Markdown output is ~40% more token-efficient than raw text, so your LLM costs drop. REST API available for pipeline automation.
Free tier, no signup required for basic use. Competes with markitdown, Unstructured.io, and LlamaParse โ but with a zero-friction web UI.
ParseFlow is a document parsing API that converts PDFs, DOCX files, and plain text into structured, evidence-backed JSON output for developers, automations, and AI workflows.
Unlike tools that return opaque extracted values, ParseFlow includes evidence metadata with every result โ confidence scores, source character offsets, and evidence snippets showing exactly where each value came from. This makes output easier to verify, debug, and trust in production.
Key features: - Structured JSON extraction with evidence spans - Table-aware chunking with presets for RAG, summarization, and extraction - Async jobs and batch processing - LangChain and LlamaIndex adapters - MCP / OpenClaw tooling support - BYOK for advanced extraction with your own model provider keys - Free deterministic tier for evaluation
Best use cases: invoice processing, contract clause extraction, receipt parsing, document intake pipelines, RAG preprocessing, AI workflow integration.
Built by a student. Priced for builders and small teams.
Free deterministic tier available. Starter: $10/month Growth: $15/month
Docs: docs.parseflow.tech
mdstill Parseflow.techmdstill's answer
mdstill is built specifically for LLM and RAG workflows, not generic file conversion. Drop any of 20+ document formats (PDF, Word, Excel, PowerPoint, EPUB, and more) and get back clean, structure-preserving Markdown that's tuned for ChatGPT, Claude, Gemini context windows and vector-database ingestion. Tables stay intact, headers become linkable anchors, output is ~40% more token-efficient than raw text extraction. Free web tool + REST API โ humans and pipelines use the same engine.
Parseflow.tech's answer:
Parseflow is built for solo devs and small teams. Unlike competitors, Parseflow has a simple set up and usage and is much more affordable compared to enterprise options while offering the same features and quality.
mdstill's answer
Alternatives fall into two camps: developer libraries that require setup, or enterprise SDKs that require a sales call. mdstill fills the middle: open a browser, drop a file, get Markdown in seconds โ and when you need to scale, the same conversion runs through a REST API. 20+ formats in one tool instead of picking a different parser per format. Tables survive the trip (most tools mangle them). Files are deleted immediately after processing. Free tier, no credit card, no signup for basic use.
mdstill's answer
Two overlapping groups. Developers building AI features โ engineers feeding documents into ChatGPT, Claude, or Gemini APIs; teams building RAG pipelines and AI agents who need reliable document ingestion. Knowledge workers and researchers โ Obsidian and Notion users importing legacy PDFs, students preparing papers for AI analysis, analysts converting spreadsheets for LLM review. Common thread: anyone who's discovered that pasting raw PDF text into an LLM loses tables and wastes tokens.
mdstill's answer
mdstill started from a personal frustration: feeding documents into ChatGPT and Claude meant pasting messy PDF text with broken tables and lost structure, or paying for heavyweight enterprise tools just to preprocess a few files. The fix seemed obvious โ Markdown is what LLMs understand best, so the conversion should be a utility anyone can use, not a product you buy. mdstill was built to make high-quality document-to-Markdown preprocessing free and instant for everyone, with an API for teams who need to scale.
Parseflow.tech's answer:
As a student, AI chatbots and LLMs would always struggle to understand correctly my school homework and documents. To fix this, I built Parseflow to help improve the context for AI models simply to help me complete my homework. Today, Parseflow has become a finished product that can parse, chunk and organize all types of documents to improve context and reduce token usage.
mdstill's answer
mdstill launched publicly in April 2026 and is in the early-adopter phase. Currently used by individual developers, indie AI-tool builders, and small research teams โ customer logos will be added as early adopters opt in to share them.
mdstill's answer
Python + FastAPI on the backend, Next.js + TypeScript on the frontend.
Parseflow.tech's answer:
Parseflow is completely built with Python.
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