Another day, another benchmark broken. @Pulse__AI schema v2 scores #1 on LongExtractBench-50, the public set of the long-document extraction benchmark Reducto commissioned and Micro1 ran. Pulse scores 99.7 precision and 99.7 recall, run through Micro1's published harness and grader on the 50 released documents. This was done with 0 custom configuration on Pulse. Reducto, who commissioned the benchmark, published 99.3. One caveat: the leaderboard was scored on 225 documents and 50 are public. @micro1_ai , happy to run our models through your private set, or have your team run it as well!

Astra and Fable are genuinely excellent at reasoning over text, and dont get me started on how much ive enjoyed coding with these new models. the problem is that enterprise documents are not only text. The failures are the same on the models we test. Past ~50 pages, recall drops hard and the model does not notice, so it returns a confident result with most of the values missing. Schema logic, like "use the current period column" or "sum these rows unless a subtotal exists" starts drifting super quickly. The economics also dont help - running these complex/long docs through a coding agent costs 15 to 30 cents a page before retries, with no auditability on the output Each generation of oai/ant models is a large jump on reasoning and coding benchmarks , but a considerably smaller step on enterprise documents. Progress here comes from models trained on the problem directly

Awesome to see the latest results from our Schema V2 model. The price/accuracy curve has been a huge frontier push for our team, and Pulse non-effort is a sleeper: • Pulse Non-effort: 92.7 at $0.015/page + parsing Meanwhile: • LlamaExtract Agentic: 89.5 at ~$0.03/page • LlamaExtract Agentic Plus: 95.6 at ~$0.08/page • Reducto Deep Extract: 90.4 at ~$0.05/page And if you want maximum accuracy, Pulse Effort reaches 95.9 at $0.09/page + parsing. Try it out now!

@Pulse__AI is now #1 on ExtractBench. ExtractBench is LlamaIndex's open benchmark for schema-guided extraction from enterprise documents: 370 real documents, 4,869 pages, and 67 schemas spanning SEC filings, customs entries, court exhibits, energy filings, and scanned forms with handwriting. Across 39 systems, including frontier models, coding agents, and specialized extraction APIs, Pulse ranks first with an overall score of 95.9. The board also measures cost, and Pulse's standard mode makes the case on that axis. It ranks #4 overall with a score of 92.7, ahead of GPT-6 Astra, Reducto Deep Extract, and every open-weight model tested, while running at a fraction of the price. This is the same schema extraction system that ships in the Pulse API today. We have an exciting set of models releasing very shortly, our research team has models training every day - stay tuned!

A few months ago, the team at @Pulse__AI launched PulseBench-Tab, our frontier table extraction benchmark with 1,820 human-annotated tables across 9 languages. Our evals team has updated the benchmark against the latest generation of document parsers and frontier models. Pulse Ultra 2 remains state of the art on the benchmark, and is now just 1 credit per page. As always, everything is open source: the scorer, the runners for every provider, the dataset, and every prediction. Leaderboard: benchmark.runpulse.com GitHub: github.com/Pulse-Software-Co… Dataset: huggingface.co/datasets/puls… PRs welcome if you'd like a provider added or a sample corrected. Excited to keep pushing the frontier of table extraction.

genuinely curious on @reductoai 's rd-tablebench from 2 years ago, r1 performs worse than the model back then. What changed here?

. @reductoai released their latest model this week, r-1. The @pulse__ai team ran it on PulseBench-Tab, our frontier table-extraction benchmark consisting of thousands of human-annotated, multilingual tables across 9 languages. Pulse Ultra 2 remains state-of-the-art with r-1 taking minimal accuracy improvements over their previous generation. Pulse Ultra 2: 0.935 Reducto r-1: 0.811 Reducto Agentic: 0.795 We will be rolling out PulseBench-Tab updates throughout the week, DM us if you’d like a parser included!

Our engineering team has been launching a lot of features. Here's what we launched over the past few weeks at @Pulse__AI 1/ MCP server 2/ Generate-split 3/ Selection model 4/ CLI 5/ One-click SSO 6/ Tutorial mode All of it is live today, and there's more on the way.

Charts are among the most valuable objects in an enterprise document, and until now they have also been among the least usable. A decade of company performance can live inside a single line chart, and decades of subsurface measurement inside a scanned well log. Conventional OCR returns the title, the legend, and the surrounding text, because a chart stores most of its meaning in the height of a bar or the slope of a line rather than in any character on the page. Today we are releasing @Pulse__AI Agentic Chart Reconstruction to the general public. It combines a family of small, specialized vision models with a VLM that orchestrates them, deciding where the system needs to look more closely. Clean digital charts move through the fast path, while faded scans, overlapping series, and tick labels too small for a full-page pass receive additional attention before anything is returned. The output is structured chart data with the context required to use it, including series names, colors, line styles, axis values, legends, and the location of the chart in the source document, so every recovered number can be checked against the figure it came from. For historical well logs, a single degraded scan can yield thousands of values aligned to a shared depth grid, exported as JSON, Excel, CSV, or LAS files that move directly into the software subsurface teams already run. The system has been in beta with a group of leading oil and gas companies recovering data from historical logs and technical records, and it is now available through the Pulse platform and API for line, scatter, bar, pie, donut, and well-log charts. The earlier generation of document systems made text searchable; Pulse makes the entire document usable. Try it now with your hardest charts! runpulse.com/blog/pulse-rele…

@Pulse__AI has signed the Open Weights and American AI Leadership letter, alongside Microsoft, NVIDIA, Meta, and 100+ others. The letter's central argument matches what nearly every enterprise customer we work with already believes, which is that control over critical infrastructure should not sit with a single provider. microsoft.com/en-us/corporat…

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Your agent needs data it can actually work with. Pulse handles that; turning complex documents like financial statements, contracts, and claims into clean markdown and structured data for agents. With MPP, agents can access and use Pulse via API. No accounts needed:

Pulse turns complex documents such as financial statements, contracts, and claims into clean markdown and structured data for agents. With MPP, your agent can use Pulse as soon as a task requires it; payment and access to the data are handled through the API and settled on Tempo

Agentic payments are now live on @Pulse__AI . An agent can point at our API with no account, no API key, and no signup, and pay for extraction on its own, one call at a time. It runs on the Machine Payments Protocol (MPP), settling inline over HTTP in USDC, in the same request that asks for the work. The agent sends an extraction request, Pulse answers 402 Payment Required with the priced terms, the agent pays and retries with proof, and the result comes back. Two round trips, no invoice to reconcile. The 402 has sat reserved since the early days of HTTP, waiting for a caller that could pay on its own. It’s live on Pulse today, more here: runpulse.com/blog/welcome-to…

@Pulse__AI ran GDP.pdf through their document stack. GDP.pdf measures whether AI can handle the messy, high-stakes documents enterprises run on. Pulse kept the models and the grader fixed and changed only the evidence layer - feeding each model a cleaner, structured version of the PDF. The result was +10 points in whole-task accuracy. One of the things they noticed: models often fail because a footnote gets dropped, a value comes from the wrong row, or a number loses its unit along the way. The public set is open to anyone working on document AI. Check it out! huggingface.co/datasets/surg…

Every document extraction system can read the text on a form, yet almost none of them can read the pen, so the checkmarks, circles, underlines, and strikethroughs people use to record decisions get treated as noise and quietly ignored. Today Pulse is launching Selection Mark, a model trained to read them, available now through a single parameter in the extraction API. More: runpulse.com/blog/capturing-…

Surge's GDP.pdf benchmark tests the documents enterprises run on, and even top frontier models pass only a fraction of whole tasks. The bottleneck isn't model intelligence. It's input quality. @Pulse__AI as the extraction layer lifted whole-task success from ~22% to ~32% (criteria-level ~80%) on the same models. When retrieval preserves document structure, models reason over the right facts. Full write-up here: runpulse.com/blog/gdp-pdf-an…

We co-authored a new post on the @awscloud ML Blog with Greg Fina, Jim Fratantoni, and ND Ngoka, walking through how @Pulse__AI extracts clean structured data from financial documents that break traditional OCR and turns it into a fine-tuning set for Amazon Nova Micro on Bedrock.

Every enterprise that processes documents eventually hits the same wall. Extracting data from PDFs is one thing, but putting data back into a PDF is where the workflow breaks down. Most enterprise PDFs don't have defined form fields at all. Government forms arrive as scanned images, insurance claims are flat PDFs, and healthcare intake forms have been printed and photocopied beyond recognition. Traditional tools have nothing to work with. @Pulse__AI Form Fill uses our vision-language models to analyze the full document layout, detect where fillable regions exist based on visual context, and place values in the correct locations. You provide structured data or natural language instructions, and Pulse returns a filled PDF. Healthcare teams are already using this to automate pre-auth forms, CMS-1500 claims, and provider enrollment packets through a single API call. Full blog post: runpulse.com/blog/introducin…