Monumint

We’re excited to announce that OmniAI is now Monumint! A new name for the next chapter of our company and our vision for AI in financial services. Every customer deserves a high-touch financial experience. Monumint is building voice AI to make that possible at scale.

We sat down with Business Insider to share the story behind Monumint. Every year, people in the US spend more than 10 billion minutes on the phone with their bank, and 60% of customer interactions happen during business hours. That's why we built Monumint. Full story below 👇

Today we're launching Monumint! Monumint is conversational AI for financial services. Our agents work across voice, SMS, and email to help banks, credit unions, and lenders reach customers at scale. In the US, people spend 10 billion minutes a year on the phone with their bank. Despite that, as a consumer it feels like there's a mile wide communication barrier anytime you want to get something done. And on the flip side, financial institutions have more data than ever and still struggle to reach customers with the right message at the right time. That’s the gap we’re working to close. We’ve now handled more than 5 million customer interactions for financial institutions, and the growth we’ve seen over the last 10 months has been incredible. Building a startup is never a straightforward journey. Where we are today is very different from where we started in YC. We had to say goodbye to great customers, make hard calls, and rebuild a lot of our assumptions from the ground up. Huge thank you to the team, our customers, and the investors who stuck with us along the way. Excited to finally share Monumint with the world.

One real estate finance customer cut manual review time by 80% 🤯 Rent rolls are huge multi-page tables with dozens of columns. Most tools fail: ❌ Headers only on the first page ❌ Rows break across pages ❌ Layouts shift mid-doc Agentic document processing solves this!

We just rolled out a new agentic loading experience! And you can actually see it thinking. 🔄 Each document flows through layout correction, contrast enhancement, OCR selection, structure recognition, and more. Check it out, and watch it think before your eyes. 👀

One of the trickier document types we’ve worked on lately are multi-page tables. Header’s on page 1. Data's on page 5. Agentic document processing tracks header context across pages and keeps your table structured from start to finish. Drop a comment to see it on your documents

Bounding boxes for extracted fields are here! 🔍 Now you can see exactly where each extracted field came from. 🔍 It draws a box around the exaction 📄 Auto-scrolls to it ⚙️ Works on all documents Personally, this has made reviewing extractions way easier!

A new round of OpenAI models (4.1 series) came out today and GPT is finally back in the running. But not with the model you'd expect... Turns out 4.1-mini dominates when it comes to document understanding. Gemini 2.5 pro is still the top ranked but at a higher cost and latency.

See the full benchmark here: getomni.ai/blog/benchmark…

It's been a busy week for open-source VLMs, so we ran them through OmniAI’s OCR benchmark. 🥁 And the results are in... Qwen 2.5 VL (72B & 32B) performed best, matching GPT-4o with ~75% accuracy and beating Mistral OCR. Gemma-3 came in much lower at 42.9%.

Excited to welcome Terry as OmniAI’s founding Growth! He built and scaled BuildStream (YC S19) and brings a ton of experience in driving growth from 0 to 1. 💡Fun fact: The twinning at Omni continues. Terry isn’t a twin himself, but he’s the proud dad of twin daughters and son!

Zerox hit 10K stars! Mic drop. 🎤⭐️

@LinkedInHelp Can you help unblock my account on LinkedIn? It got restricted about a week ago. I was instructed to do the Persona verification, and it got denied. I have been trying to reach out to customer support but have not gotten any responses. Any help would be appreciated!

It’s difficult to measure document extraction accuracy! Our benchmark compares the OCR / extraction JSON to the ground truth JSON, and then calculates the number of JSON differences divided by the total fields in the ground truth JSON. Comment with providers you’d like to see.

Introducing the Omni OCR Benchmark, the most comprehensive evaluation of OCR tools. We evaluated traditional OCR providers and multimodal LLMs across 1,000 documents for accuracy, cost, and speed with an open-source approach. See how each provider ranks: getomni.ai/ocr-benchmark

Check out the docs for details: docs.getomni.ai/zerox

🚀 Exciting news! Zerox just got a major upgrade. It now supports multiple models - Google Gemini, Azure OpenAI, AWS Bedrock. And structured schema extraction! Pass in a schema to extract data directly from your documents.

What do you do when someone sends you a PDF with 12,640 rows you've got to extract? OmniAI automates it in minutes, no code! Companies spend hundreds of engineering hours figuring out how to parse that data. We can’t stop PDFs, but at least we can turn them into real data!

We just added Gemini 2.0 Flash to Zerox! ⚡️ These are early results from our VLM benchmark. While it still has a ways to go on the accuracy side (about ~80%), it easily beats GPT 4o and other traditional OCR providers like AWS Textract and Unstructured. And it's cheap!