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Data Extraction with Flexible Schemas

Hacker News

Data Extraction with Flexible Schemas

Hey! We’ve built a data extraction tool to flexibly automate data and document processing. You’ve probably seen a few of these, so have we! A few of us have been varyingly stuck trying to automate the extraction of borrower financials for the past 5 years. We think that there are a few missing features of most data extraction tools. * They are usually too complex to quickly get up and running * They are overly constrained in terms of what workflows and documents they support We’ve always felt like speed and flexibility were sticking points, so we went slightly orthogonal to the alternatives. It is deliberately very simple, with three key features 1. Generate a custom schema that is the target of the data extraction 2. Logic and “expert insights” can get added to field level prompts 3. Ability to share with externals via chat to get data and feedback, quickly! The demo page is at https://go.sea.dev with an explainer and link to demo. The demo app is constrained, but feel free to get in touch with me with details if you’d like to unlock the full capabilities Some of the features we are working on: * Improvements on OCR * Advanced data management * Email or WhatsApp to accept responses * API and embedded data collection copilot for SaaS * Meeting recording plugin, and streaming audio * Securely share partial submissions with 3rd party for completion * Enrichment via website scraping * Integrations (Hubspot, Sheets etc) We are a very small team, with a mix of researchers and engineers. We are leaning into improvements in data extraction, conversation evals and data structuring innovation to make the product as powerful and seamless as possible. Our background is in financial services, so most of our effort is going into that use-case (business risk assessment to be specific), we’ve solved a bunch of our internal use cases as well as general purpose customer problems, so we thought to share it with a wider audience. Would love to get your feedback and comments!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: email, plain · Missing: mac, agents, macos
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, audio · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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