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Reducto
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Our platform provides a comprehensive toolkit for working with documents the way a human would, combining custom in-house and leading frontier models to power efficient and accurate document workflows.
We are built for enterprise workloads with flexible deployment options from the cloud to fully air-gapped environments, SOC II and HIPAA compliance, and zero data retention.
Reducto is trusted by leading AI teams at companies like Harvey, Scale AI, Toast, Carlyle and Vanta.
GitHub ReductoReducto's answer:
Reducto is the only agentic document platform that orchestrates custom in-house and frontier models under the hood, automatically routing each page to the right model based on complexity. That means we balance accuracy, latency, and throughput for your specific use case โ not a one-size-fits-all pipeline.
Where others stop at parsing or extraction, we cover the full lifecycle of document work โ parse, classify, split, extract, edit, and workflow orchestration โ in one platform. The result: 99%+ accuracy on the long-tail documents (handwriting, complex tables, scanned PDFs, charts) that break other solutions, with grounded outputs (bounding boxes, citations, confidence scores) so your team can trust what comes out.
Reducto's answer:
Performance for you, not for a benchmark. Competitor benchmarks are biased. Reducto encourages head-to-head evaluations on your own documents โ and consistently wins on accuracy, robustness, and the long tail (tables, charts, handwriting, scans). We're not the cheapest; we're the most optimal, automatically balancing accuracy, latency, and throughput for your workload.
Enterprise-ready from day one. Flexible deployment from cloud to hybrid VPC to fully air-gapped, SOC 2 and HIPAA compliance, zero data retention, autoscaling for spiky loads, and white-glove FDE support with custom SLAs. We've processed billions of pages and counting.
One complete platform instead of a stitched-together stack. Parse, Classify, Split, Extract, and Edit endpoints โ plus a Workflows product, agent-ready tooling (CLI, MCP, integrations), and 30+ supported data and file types. Stop maintaining four vendors for one document pipeline.
Reducto's answer:
Our primary audience is technical leaders at AI-native companies and document-heavy enterprises โ CTOs, VPs of Engineering, Heads of AI/ML, and Chief AI Officers โ who own AI and platform strategy and are accountable for shipping production AI on messy real-world data. Our champions and end users are the AI engineers, ML engineers, data engineers, and AI platform engineers who actually build on top of Reducto.
We see the strongest fit in regulated, document-heavy industries: financial services, fintech, insurance, healthcare, and legal โ plus the AI-native companies serving them. The common thread: they process large volumes of unstructured documents (often millions of pages a month), they care about accuracy and throughput at production scale, and they have engineering teams that would otherwise burn cycles building and maintaining OCR, parsers, and extraction pipelines themselves.
Reducto's answer:
Reducto was founded by Adit Abraham (CEO) and Raunak Chowdhuri (CTO) on a simple observation: modern AI models are exceptional at reasoning, but they're only as good as the data fed into them โ and most real-world data lives in messy, unstructured documents. PDFs, scans, handwritten forms, complex tables, charts. The "odd structure of documents" was breaking otherwise capable AI systems.
So they took a different approach: treat document ingestion as a computer vision problem, not a text problem. By combining traditional CV models with vision-language models in an agentic orchestration layer, Reducto reads documents the way a human would โ interpreting layout, structure, and visual cues before extracting meaning.
What started as a parsing engine has grown into a complete agentic document platform โ powering document workflows for the largest AI teams in the world, with billions of pages processed and counting.
Reducto's answer:
Based on our record, GitHub seems to be a lot more popular than Reducto. While we know about 2470 links to GitHub, we've tracked only 2 mentions of Reducto. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
This is why eval and observability ship as a unit, not as separate purchases. agent-eval scores and gates the output โ the tiers above, drift, hallucination. AgentLens captures the trace of how the agent got there: every model step and tool call, the resolved inputs, the raw outputs, the trajectory. Two things fall out of that:. - Source: dev.to / 7 days ago
The real fragility is in trying to constrain arguments. The docs are explicit that a pattern like Bash(curl http://github.com/ *) fails to do what it looks like it does. It won't match curl -X GET http://github.com/... (option before the URL), curl https://github.com/... (different protocol), curl -L http://bit.ly/xyz (redirects to GitHub), URL=http://github.com && curl $URL (variable), or curl http://github.com... - Source: dev.to / 9 days ago
Fallback chains โ og:title โ twitter:title โ- SSRF protection โ if you fetch user-supplied URLs, you MUST block
localhost, RFC-1918 ranges, and internal hostnames, or your preview endpoint is a proxy into your own infrastructure- Caching โ you do not want to re-fetch a URL on every render
- Rate limiting โ a public...
- Source: dev.to / 11 days ago
$ git pull Remote: Repository not found. Fatal: repository 'https://github.com//.git/' not found. - Source: dev.to / 12 days ago
// ==UserScript== // @name GitHub -> Obsidian Task // @namespace obsidian // @version 1.0 // @match https://github.com/*/*/issues/* // @match https://github.com/*/*/pull/* // @grant GM_setClipboard // ==/UserScript== (function () { 'use strict'; function getTitle() { return document.querySelector("bdi")?.textContent.trim(); } function copyTask() { ... - Source: dev.to / 15 days ago
Key to this was software from AI document-management company Reducto, which shared an office building with Kino AI. In another oversubscribed session, developer relations lead Palak Agarwal, explained how the advanced nature of the companyโs code enabled a comprehensive scan of the messy PDF files and organization of the information gleaned into a usable format. - Source: dev.to / 26 days ago
Yes! We used our friends at Reducto (https://reducto.ai/ to see what I mean. For apps like Jmail and JFlights we use their structured extraction endpoint insteadโyou define a schema (e.g. {from, to, subject, date, body} for emails or {departure_airport, arrival_airport, passengers[], date} for flights) and it pulls those fields directly into JSON. The JFlights example served as the best ad for Reducto and how doc... - Source: Hacker News / 7 months ago
GitLab - Create, review and deploy code together with GitLab open source git repo management software | GitLab
Mindee - Extract any data point, from any document, in a second
BitBucket - Bitbucket is a free code hosting site for Mercurial and Git. Manage your development with a hosted wiki, issue tracker and source code.
DocParser - Extract data from PDF files & automate your workflow with our reliable document parsing software. Convert PDF files to Excel, JSON or update apps with webhooks.
VS Code - Build and debug modern web and cloud applications, by Microsoft
Parseflow.tech - Evidence first, PDF and DOCX parsing API. Structured JSON, no enterprise setup.
