Eliminate per-page OCR fees by doing PDF text extraction in-browser

Many enterprises and mid-market organizations that process large volumes of scanned PDFs pay per-page OCR fees that quickly add up and create ongoing vendor lock‑in, and privacy‑sensitive sectors (healthcare, legal, finance) are reluctant to send documents to third‑party cloud endpoints. The people who feel this pain are document processing teams, RPA integrators, and SaaS product managers looking to reduce recurring costs and meet stricter compliance requirements. You could build an in‑browser PDF text extraction product: a WebAssembly‑based OCR and layout parsing SDK and embeddable JavaScript library that performs conversion locally (with optional enterprise model bundles and secure update channels) so customers avoid per‑page cloud billing. The timing is attractive — modern browsers and WASM/WASI make heavier ML workloads feasible client‑side, user expectations and regulation are shifting toward privacy‑first processing, and rising cloud OCR unit costs create a clear cost arbitrage for high‑volume users. The total addressable market is roughly $6.0B (2M organizations × $3K ACV), with a market score of 88/100 and revenue potential rated 90/100, so commercial opportunity is tangible. This approach stands out on two fronts: lower ongoing costs for customers and stronger privacy guarantees (no document egress), and it can reduce your own cloud operating expenses. The honest challenges are significant: achieving cloud‑comparable accuracy across languages and document types with compact client models, handling device heterogeneity and performance limits, and overcoming competition from established cloud OCR providers in a medium‑competitive market. If you can prove accuracy for priority document classes and deliver enterprise integration and lifecycle management, the offering can win specific verticals and high‑volume customers; otherwise, technical limitations or integration friction will constrain adoption.