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Parseflow.tech
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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
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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.
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.
Parseflow.tech's answer:
Parseflow is completely built with Python.
Based on our record, React seems to be more popular. It has been mentiond 818 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
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One inspiring example is a developer building a "Todoist Clone" using a combination of React, Node.js, and MongoDB. The developer tapped into open source libraries and community support to create a highly responsive task management application. This project underscores how indie hackers can achieve rapid development and adaptation with minimal budget โ a theme echoed in several indie hacking success stories. - Source: dev.to / about 1 year ago
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ABBYY - ABBYY's leading AI and machine learning technology solutions range from process analysis, data capture, pdf editor, text and content recognition (OCR) and extraction, combining process and content insights to deliver digital intelligence.
