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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.
You could say a lot of things about AWS, but among the cloud platforms (and I've used quite a few) AWS takes the cake. It is logically structured, you can get through its documentation relatively easily, you have a great variety of tools and services to choose from [from AWS itself and from third-party developers in their marketplace]. There is a learning curve, there is quite a lot of it, but it is still way easier than some other platforms. I've used and abused AWS and EC2 specifically and for me it is the best.
Based on our record, Amazon AWS seems to be more popular. It has been mentiond 485 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.
> but it's still a singleton instance, so where do you run it? Most hardware doesn't give you enough uptime for what you need here, because what you actually needed was a re-architecture for distribution / failover / whatever, and while you could ask your LLM to do that you aren't going to run your bank on the result. If only we had a way to solve these issues with tools capable of running Rust programs in that... - Source: Hacker News / 16 days ago
Not because infrastructure isn't important. It is. Not because Amazon Web Services (AWS) is a bad platform. It isn't. - Source: dev.to / about 1 month ago
The AWS S3 documentation covers all of these in detail. The configuration takes about an hour to get right the first time and rarely needs changes after. - Source: dev.to / about 2 months ago
The first pattern is direct-to-storage. The client uploads chunks directly to an object storage service like Amazon S3 using pre-signed URLs. The application server creates the upload session and grants permission but never sees the file bytes. This pattern scales well because the application servers do not handle the upload bandwidth. - Source: dev.to / about 2 months ago
AWS Secrets Manager provides managed secrets storage with automatic rotation for RDS databases, Redshift clusters, DocumentDB, and other common services. For applications running on AWS infrastructure, Secrets Manager integrates directly with Lambda, ECS, EKS, and EC2 at the platform level, injecting secrets into the application environment without requiring files on disk or manual retrieval code. - Source: dev.to / 3 months 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.