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Niyati Chhaya PRO

nchhaya
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https://apis.hyperbots.com/

AI & ML interests

Document AI, Finance AI , Agentic AI, Document Intelligence

Recent Activity

repliedto their post 5 days ago
Try HyperAPI on 1,000+ of your own pages for $1. Use code HYPRAPI1K: https://apis.hyperbots.com/pricing We've been building HyperAPI at Hyperbots, a set of document APIs (parse, extract, classify, split, redact), and one thing kept bugging us: most document AI tools make you commit to a big plan before you even know if they work on your files. So the Starter plan is $1/month. It includes 300 pages a month, and the code HYPRAPI1K adds 1,000 extra pages in your first month. That's enough to throw your real, messy documents at it (scanned invoices, multi-page PDFs, forms with tables) and see for yourself whether it holds up. No seat pricing. You only pay for the pages you process. If you're building agents or RAG pipelines that need to read documents, I'd love for you to try it and tell us where it breaks. Honest feedback is more useful to us than anything right now.
posted an update 5 days ago
Try HyperAPI on 1,000+ of your own pages for $1. Use code HYPRAPI1K: https://apis.hyperbots.com/pricing We've been building HyperAPI at Hyperbots, a set of document APIs (parse, extract, classify, split, redact), and one thing kept bugging us: most document AI tools make you commit to a big plan before you even know if they work on your files. So the Starter plan is $1/month. It includes 300 pages a month, and the code HYPRAPI1K adds 1,000 extra pages in your first month. That's enough to throw your real, messy documents at it (scanned invoices, multi-page PDFs, forms with tables) and see for yourself whether it holds up. No seat pricing. You only pay for the pages you process. If you're building agents or RAG pipelines that need to read documents, I'd love for you to try it and tell us where it breaks. Honest feedback is more useful to us than anything right now.
posted an update 2 months ago
**The hardest part of agentic AI in finance may not be the agent. It may be the document boundary.** Bank statements are one of the highest-volume documents in finance and one of the least standardized. Every bank has its own layout, columns move, descriptions wrap across lines, and a misread cell can change a lending decision. Here's what one call to **HyperAPI** does with a real (redacted) statement: - **Parse** returns layout-aware text and the transaction table, with bounding boxes - **Extract** returns account-level entities plus 24 line items as clean JSON - **Redact** masks the account number, customer name and address before anything leaves the pipeline ```bash curl -X POST https://apis.hyperbots.com/v1/parse \ -H "X-API-Key: $KEY" \ -F "file=@statement.pdf" ``` **What's still hard**, and what we're working on: skewed or low-quality scans, statements where the opening balance sits outside the table, multi-currency accounts, and bundles with several documents in one PDF. We're building this as the document layer under financial agents, on a simple belief: the agent can be perfect and the outcome still wrong if the input data is wrong. If you work on document pipelines, I'd like to know what breaks for you. Try it in the playground at https://apis.hyperbots.com and tell me where it falls over. #documentai #agents #fintech #informationextraction
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