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Excited to announce @Pulse__AI's $3.9M seed led by @natfriedman and @danielgross with participation from YC and some amazing angels! Document processing has existed for decades, yet both legacy players and AI startups still struggle with real-world extraction. We're solving this with intelligent schema mapping that maintains accuracy across millions of complex documents. We’re hiring, please reach out!

Exclusive: Andromeda started out as a project by AI-focused VC firm NFDG. Now it's a standalone startup with a run rate of $100M and a $1.5B valuation after raising $60M from Paradigm. I spoke to CEO @WMoushey about his plans to build a better market around global AI compute.

Footnote extraction is now available on the @Pulse__AI platform. Every footnote is returned as a structured object with its reference marker, full text, and positional metadata linking it back to exactly where it was cited in the document. Available now here: platform.runpulse.com

Platform: platform.runpulse.com More info: runpulse.com/blog/split-end…

The dirty secret of document AI: most accuracy problems aren't model problems. They're scoping problems. If your schema is running against the full document, you're asking your model to find signal across cover pages, appendices, boilerplate, and blank pages. It doesn't know what matters. You do. A billion pages taught us the fix is upstream: define which pages are relevant before extraction runs. Clean inputs, clean outputs. @Pulse__AI is launching Split for public access today.

@rithwikpat @rtwlz @Pulse__AI not bad, can play around with prompting

@rtwlz i’ve used @pulse__ai for a pretty similar use case

@rheejust @porterdotrun @FirstMarkCap @ycombinator @daltonc @ROWGHANI Great work and congrats!!

I’m excited to announce that @porterdotrun has raised a $20M Series A to provide effortless app infrastructure in any cloud provider. Our round was led by @FirstMarkCap with participation from @ycombinator and strategic angels including @daltonc and @ROWGHANI. When we started Porter, we had one simple goal: allow startups to stop thinking about cloud infrastructure. Today, the fastest-growing AI companies from the seed-stage to IPO use Porter to manage hyper-growth infrastructure across AWS, Google Cloud, and Microsoft Azure. Our users scale individual clusters in each cloud provider to hundreds of machines and terabytes of RAM without DevOps overhead. We’ve more than doubled headcount over the past few months and are aggressively expanding the surface of what our platform can manage. We are on a mission to make using the public cloud utterly seamless. I’d like to thank our users, investors, and most importantly, the Porter team. As all of them know, we are only getting started. We’re actively hiring with offices in NY and SF, so if you’re interested in managing core infrastructure for the next generation of public companies or tackling the hardest devX problems, shoot me a DM.

@itsalfredw Congrats!

Today, Listen crossed $100M in funding. Building is easy now. Knowing what to build isn't. Our AI finds and talks to your users so you don't have to guess. See how Sweetgreen, Microsoft, and Replit use it:

Agentic OCR is everywhere in document AI conversations right now. We published a breakdown of what it actually means, the tradeoffs that matter for production, and why hybrid architectures usually win. More below!

shoutout @NotionHQ

@Pulse__AI more here: runpulse.com/blog/introduci…

Introducing Suggest by @Pulse__AI upload a document, get a suggested schema in seconds. No manual field definition to get started. The output is production-ready and API-compliant. more details below!

We assumed structured outputs had solved document extraction. Then we tried complex schemas at scale. New post on the computational complexity of schema-guided extraction: why JSON schemas require pushdown automata, how state explosion happens, and why tighter constraints can actually hurt accuracy.

@ritvikpandey21 finally

Most spreadsheet errors are not obvious failures. They are structural ones. Flatten the grid and you lose meaning before the model ever reasons. After millions of XLSX pages in production, the takeaway was clear. Representation was the bottleneck, not scale. Advanced spreadsheet parsing is now generally available in @Pulse__AI - due to high demand, message our team to get started. Better structure. Fewer silent errors. Real business impact at production scale.

@natfriedman 🤝

🥳

Accuracy is only part of the document AI problem. In production systems, meaning depends on layout. Tables, hierarchy, and proximity determine how values should be interpreted and verified. Layout segmentation and bounding boxes preserve that structure. They enable citations, traceability, and review long after extraction. We wrote a technical post on why layout needs to be treated as a first-class primitive in document AI systems, with examples from finance, healthcare, and legal workflows. Link below.

Merry Christmas! A common request we get is how to share extraction results with someone who doesn't have a @Pulse__AI account. Now you can. Shared links with configurable expiration and org-level visibility controls. Simple feature, but it removes friction in a lot of workflows.

Webhooks are now live in @Pulse__AI . Real-time job notifications, auto-retries, signed payloads. If you're running async extraction at scale, no more polling. Small addition, but it came up a lot.

more here: runpulse.com/blog/introduci…

One of the hardest problems in document extraction isn't generating output. It's knowing whether that output is actually correct as systems evolve. Most teams rely on spot checks, which doesn't scale well. Accuracy Scorer from @Pulse__AI lets you upload ground truth, measure precision and recall at the field level, and catch regressions before they hit production. Available now: platform.runpulse.com