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Launch HN: Extend (YC W23) – Turn your messiest documents into data

extend.ai

21–30 of 35 posts

Re: Launch HN: Extend (YC W23) – Turn your messiest documents into data

#21

on the pricing page, what in the world is performance optimized vs cost optimized??? https://docs.extend.ai/2025-04-21/product/general/how-credit... Are those just different SLAs or different APIs or what?

This is the most confusing pricing page I’ve ever seen - different options have different credit usage and different cost per credits? How many degrees of freedom do you real need to represent API cost.

Feedback heard. Pricing is hard, and we've iterated on this multiple times so far.

Our goal is to provide customers with as much transparency & flexibility as possible. Our pricing has 2 axes:

- the complexity of the task

- performance processing vs cost-optimized processing

Complexity matters because e.g. classification is much easier than extraction, and as such it should be cheaper. That unlocks a wide range of use cases, such as tagging and filtering pipelines.

Toggles for performance is also important because not all use cases are created equal. Similar to how having options between cheaper and the best foundation models is important, the same applies to document tasks.

For certain use cases, you might be willing to take a slight hit to accuracy in exchange for better costs and latency. To support this, we offer a "light" processing mode (with significantly lower prices) that uses smaller models, fewer VLMs, and more heuristics under the hood.

For other use cases, you simply want the highest accuracy possible. Our "performance" processing mode is a great fit for that, which enables layout models, signature detection, handwriting VLMs, and the most performant foundation models.

In fact, most pipelines we seen in production often end up combining the two (cheap classification and splitting, paired with performance extraction).

Without this level of granularity, we'd either be overcharging certain customers or undercharging others. I definitely understand how this is confusing though, we'll work on making our docs better!

Re: Launch HN: Extend (YC W23) – Turn your messiest documents into data

#22
post #20

Earlier quoted context omitted.

This is the most confusing pricing page I’ve ever seen - different options have different credit usage and different cost per credits? How many degrees of freedom do you real need to represent API cost.

> How many degrees of freedom do you real need to represent API cost. The amount that your users care about. At a large enough scale, users will care about the cost differences between extraction and classification (very different!) and finding the right spot on the accuracy-latency curve for their use case.

Exactly correct! We've had users migrate over from other providers because our granular pricing enabled new use cases that weren't feasible to do before.

One interesting thing we've learned is, most production pipelines often end up using a combination of the two (e.g. cheap classification and splitting, paired with performance extraction).

Re: Launch HN: Extend (YC W23) – Turn your messiest documents into data

#23

on the pricing page, what in the world is performance optimized vs cost optimized??? https://docs.extend.ai/2025-04-21/product/general/how-credit... Are those just different SLAs or different APIs or what?

How different are the extraction qualities, any benchmarks or other info you can share?

It's very dependent on the use case. That's why we offer a native evals experience in the product, so you can directly measure the % accuracy diffs between the two modes for your exact docs.

As a rule of thumb, light processing mode is great for (1) most classification tasks, (2) splitting on smaller docs, (3) extraction on simpler documents, or (4) latency sensitive use cases.

Re: Launch HN: Extend (YC W23) – Turn your messiest documents into data

#25
post #12

at ng3n.ai ive been using datalab.to for document processing. currently its mostly for conversion to markdown and some extraction. ng3n is more of a grid-like workflow solution on top of documents. it's a user-facing application geared towards non-technical users that have processing needs. if there are all these new problems that became solvable, what exactly are they? id be interested in replacing datalab with exte…

thanks! Datalab is great, I've met Vik a few times and their team has done some impressive work. We can also support the conversion to markdown use case, and might be a better fit depending on your use case. Feel free to create an account to try it out!

Re: Launch HN: Extend (YC W23) – Turn your messiest documents into data

#26

Earlier quoted context omitted.

fully aware that OcR and IDP has been around, but the “AI native” versions are pretty saturated too

There's certainly a lot of tools that focus on individual parts of the problem (e.g. the OCR layer, or workflows on top). But very few that solve the problem end-to-end with enough flexibility for AI teams that want a lot of control over the experience. For example, we expose options for AI teams to control how chunking works, whether to enable a bounding box citation model, and whether a VLM should correct handwriti…

Not sure about that. There's Llamaindex and plus many other document orchestration frameworks

Re: Launch HN: Extend (YC W23) – Turn your messiest documents into data

#29

For some reason "turn your messiest data into documents" makes more sense.

Seconded. It's unstructured data that becomes structured.

To what degree does this product (or others) look at a collection of documents and offer various possible schema to choose from? This seems like not just a "hard for AI" problem but a "hard for humans" problem. In other words, even a high-quality AI with lots of "thinking" time isn't enough. It isn't just about reasoning through a problem -- there will be a lot of judgment calls. Judgment calls that require significant context and domain knowledge. This to me seems like an area where human-in-the-loop really matters.

Re: Launch HN: Extend (YC W23) – Turn your messiest documents into data

#30
Other players:

1. Trellis (YC W24) 2. Roe AI (YC W24) 3. Omni AI (YC W24) 4. Reductor (YC W24)

Other players(extended):

1. Unstract: Open-source ETL for documents (https://github.com/Zipstack/unstract) 2. Datalab: Makers of Surya/Marker 3. Unstructured.io

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