Everyone here knows that it's a really big problem that no one has nailed yet.
My 2 cents:
1. It took us (newscatcherapi.com) three years to realize that customers with the biggest problems and with the biggest budgets are the most underserved. The reason is that everyone is building an infinitely scalable AI/LLM/whatever to gain insights from news.
In reality, this NLP/AI works quite OK out of the box but is not ideal for everyone at the same time. So we decided to do Palantir-like onboarding/integration for each customer. We charge 25x more, but customers have a perfect tailor-made solution and a high ROI.
I see you already do the same! "99%+ accuracy with fine-tuning and human-in-the-loop" is what worked great for us. This way, your competitor is a human on payroll (very expensive) and not AWS Tesseract.
Going from 95% to 99% is just a fractional improvement, but it can be "not good enough" to a "great solution" change that can be charged differently.
2. "AI-powered workflow for unstructured data" what does it even mean? Why don't you say "99%+ accuracy extraction"? It's 2024, everyone is using AI, and everyone knows you need 2 hours to start applying AI from 0. So don't lower my expectations.
1. I completely agree. Last-mile accuracy is crucial for enterprise buyers, and the challenge isn't just the AI. It's about mapping their business logic and workflows to the product in a way that demonstrates fast time to value.
2. Thanks for the feedback. We're still refining the messaging and don't want to be overly focused on just the extraction aspect. Do you think positioning it as ETL for unstructured data or high-accuracy extraction for enterprises might work better?"
We've been building something similar with https://vlm.run/: we're starting out with documents, but feel like the real killer app will involve agentic workflows grounded in visual inputs like websites. The challenge is that even the best foundation models still struggle a lot with hallucination and rate limits, which means that you have to chain together both OCR and LLMs to get a good result. Platforms like Tesseract work fine for simple, dense documents, but don't help with more complex visual media like charts and graphs. LLMs are great, but even the release of JSON schemas by OpenAI hasn't really fixed 'making things up' or 'giving up halfway through'.
Everyone here knows that it's a really big problem that no one has nailed yet.
My 2 cents:
1. It took us (newscatcherapi.com) three years to realize that customers with the biggest problems and with the biggest budgets are the most underserved. The reason is that everyone is building an infinitely scalable AI/LLM/whatever to gain insights from news.
In reality, this NLP/AI works quite OK out of the box but is not ideal for everyone at the same time. So we decided to do Palantir-like onboarding/integration for each customer. We charge 25x more, but customers have a perfect tailor-made solution and a high ROI.
I see you already do the same! "99%+ accuracy with fine-tuning and human-in-the-loop" is what worked great for us. This way, your competitor is a human on payroll (very expensive) and not AWS Tesseract.
Going from 95% to 99% is just a fractional improvement, but it can be "not good enough" to a "great solution" change that can be charged differently.
2. "AI-powered workflow for unstructured data" what does it even mean? Why don't you say "99%+ accuracy extraction"? It's 2024, everyone is using AI, and everyone knows you need 2 hours to start applying AI from 0. So don't lower my expectations.