I've been building an extraction API for the past month. The use case is specific — reading informal...
LLM extraction from multi-language WhatsApp orders silently produces malformed JSON; corrective prompting fixes 90% of parse failures on retry. For production LLM pipelines, morphex-api open-source with per-field confidence metadata enables auditable extraction and auto-approval workflows, eliminating silent data corruption.
I've been building an extraction API for the past month. The use case is specific — reading informal WhatsApp orders in mixed Hindi/English/Malayalam and turning them into structured records for Indian distributors. Something like:
"bhai 50 bags opc 53 cement calicut tuesday urgent"
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needs to become:
I needed to extract invoice line items from hundreds of PDF documents. Dates, amounts, vendor names....
JSON for the payload, XML tags for the wrapper. Which format survives nesting, partial streams, and a model that wandered…
A solo AI operator tested llama.cpp, OpenRouter, Gemini batch, and OpenAI Batch under one strict rule for the 2asy.ai extraction…
I kept seeing the same gap: people ship LLM-based document extractors (invoices, receipts, forms)...
A few months ago, I was building a system to answer questions from 100-page technical PDFs. You know...
A technical deep dive into parsing PDFs, Office files, emails, images, and websites into clean markdown for extraction and RAG.
