Over the past six months, I've had the chance to work with hundreds of founders and operators building AI companies across legal, healthcare, finance, enterprise agents, and more! We recently ran around SF dropping off @reductoai swag boxes, meeting teams IRL, and hearing the stories behind what they're building. Excited to share a few of those conversations soon :)

If you printed and stacked the pages @reductoai has processed this year, the stack would reach space multiple times over We just wrapped an insane Q2 but some highlights that stand out: - 🖥️ We made a lot of progress on core model performance and grew the product surface. Reducto is now faster and more accurate across the board - 📈 Our GTM team is crushing it, and the team delivered 350% to quota capacity this quarter - 🖥️ (pt 2) Our new products like Deep Extract are setting a new standard for the industry. In @micro1_ai's benchmark there was literally no solution that came close. - 💰We broke our record for largest new contract twice - 🏢 We're now 50% over intended capacity for our office, and are moving to a beautiful new space soon --- The day to day of startup life is always chaotic with plenty of ups and downs, but every time I pause to think about the bigger picture of our journey it's genuinely hard to believe. Revenue growth is still around 8x YoY, and we're just now ramping our capacity. Our sales team in Q3 will be 4x the size of what it was in Q2. Even more importantly, we secured a massive cluster of GPUs and there's a lot that our product research teams are working on that I think will be a step change for our customers. As always, could not be more grateful for our customers and for the team that makes all of that possible. More from us soon 🔮

seeking ny cafe recs to work out of this week ( ac required with this heinous heat )

okay hi 🕺🏽

Today we're publishing LongExtractBench, a benchmark commissioned by @reductoai and independently validated by micro1. We evaluated seven production document extraction systems across the same 225 complex enterprise documents. The benchmark was intentionally difficult: documents averaged 358 pages and contained roughly 88,700 ground-truth fields each. Every system was evaluated using the configuration documented in the benchmark methodology. Key findings: • Reducto Deep Extract was the only system to successfully complete all 225 documents. • Direct frontier LLM baselines achieved substantially lower completion rates on long, complex documents. • In this benchmark, dedicated extraction platforms achieved higher completion rates than the direct frontier LLM baselines. • Recall was the clearest differentiator. Precision remained high across systems, but recall ranged from 33.8% to 99.6%, highlighting which systems consistently captured the information contained in long, complex documents. The full report includes the benchmark methodology, limitations, and reproducibility resources. Check out the report and results in the comments below.

see it for yourself! we’re offering up to $1,000 in credits to test deep extract on your own documents 🙌🏼 get access here: reducto.ai/try-deep-extract

If you ever felt like you don't have enough Reducto in your life, we're fixing that this week at @aiDotEngineer ☺️ We have *5* events this week, a few thousand dollars worth of swag to give out, and prizes at our booth (P8) Come say hi!

just getting started 🚀🚀🚀

We ordered SO MANY more tshirts for AIE!! Come say hi to win one 👋🏼 Ft @adelwu_ and @vibhayellamraju

marketing stand ⬆️

reducto.ai/startups 🤫

loved hearing what everyone was building ! if docs are core to your stack, we’ve got a pretty sweet startup program to help you get started with @reductoai 🤝

If you're building vertical AI, this is a room worth being in!

indian weddings >>>

this sales draft is our world cup

A dropped minus sign on a $1.3B loss caused Fidelity's Magellan fund to issue a dividend estimate that was off by $2.6 billion. That happened before LLMs existed, and the same class of errors shows up in every financial RAG pipeline we work with today. The problem is almost always upstream of the model. We wrote up exactly where 10-K parsing fails, with benchmark numbers and real-world costs. Blog link below:

“hello world” but it’s a smoothie

model intelligence ≠ document understanding

New to SF? we're hosting a happy hour at @browserbase HQ with our friends from @harvey and @reductoai. come by next Tuesday to meet engineers & founders building agents automating busy work on behalf of humans. demos & drinks included🥂 (not pictured: @yash_s_gupta)

so loved 🫶