Reducto – A vision based document ingestion API for LLMs

Hacker News

Hey HN, I'm Raunak from Reducto ( https://reducto.ai ), a high-quality document ingestion API tailored for language models. We developed Reducto to address our own need - no existing parsing solutions provided the accuracy and speed necessary for our user-facing AI applications. We designed a system that comprehends documents visually (like a human), ignoring document metadata and processing each page as an image to ensure the highest possible accuracy (with benchmarks to prove it). Please give our demo a try with some of your own PDFs or reach out at founders@reducto.ai if you’d like to start using Reducto in production.

Visit product View on Hacker NewsFeb 27, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, models · Missing: mac, agents, macos

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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