Launch HN: Parsewise (YC P25) – Reason Across Documents with an API
10 hours ago
Hi all, it’s Greg and Max, founders of Parsewise here (https://www.parsewise.ai/api). Parsewise transforms a bucket of unstructured data into schema compliant data, retaining lineage for values resolved across documents.
Imagine giving Claude a bunch of files and asking for a CSV or JSON output. If you have tried this, you know both the system limitations (number of files, type of inputs, cost, latency) but also the human-facing challenge of having no way to validate the results quickly. We solve both. We help tech teams simplify their unstructured data ETL, and loop in business experts for the definitions and for instant validation.
Here is a video with a few use cases: https://www.youtube.com/watch?v=dbRllnnh47w
Parsewise in the words of someone coming to us: ”I need to extract information from insurance policy PDFs, phone calls that have been transcribed, emails, etc. I am NOT looking for something that would just extract data point by data point, page by page into a structured well-defined schema but more something more agentic that can understand that information might be across documents and that it should reason over what to extract.”
We started the company based on a decade of experience (and pain) in complex data transformation and data analysis / synthesis. Greg was building both classical ETL and implemented AI workflows at Palantir. At Bain, Max did highly complex data analysis in the financial sector, similar to many of our customers.
Parsewise works by taking in a bucket of data (think hundreds or thousands of pdfs, excels etc.), and outputting schema compliant data where every single value is traceable down to word level citations across multiple documents in the bucket. We provide API customers with ways to show the lineage in their own applications, or they can use our platform for internal operations. At the core of the data processing we have self-improving agent definitions. They define the acceptable sources, the logic for resolving or combining values, and the rule for highlighting uncertainty to the end user.
The underlying tech is model and cloud agnostic and can be deployed in private networks. We have seen the best results with Gemini models for visual reasoning, achieving SOTA (beating Claude Fable) on the strongest grounded reasoning benchmark we have found (Databricks OfficeQA). Notably, we focused more on the “human harness” rather than the model harness, leaning into the actual friction we saw in uptake, which is around verifiability. That means optimizing the time and clicks required to trust the outcomes. We use vLLMs for parsing, and then we use small models for efficient large scale exhaustive search. Unlike RAG, we do not sample; instead, we exhaustively find all relevant values for a given query. We use larger models for decision making around resolutions and flagging inconsistencies to users.
This exhaustiveness and explicit value sourcing is unique to our platform, and it goes beyond the first step of data parsing that many existing providers cover.
We would love to welcome builders and tinkerers to try Parsewise on your complex document challenges. We have a ton of ideas on how we can expand the product and make it better, but would appreciate feedback and ideas from the community!
"With experience and support from" is a nice landing trick!
How do you extract and relate to each other the facts from the documents that require comprehension and not simple similarity matching using common embeddings models?
Haha thanks, the reader can try and guess which is which;)
We actually don't use embeddings or vector similarity, since those tend not to work well in specialist domains (e.g. for the OfficeQA benchmark where we have 90k pages talking about US treasury numbers, they would be mostly mapped to a very small embedding space because it's all the same topic, with small variations across years, expense categories etc.).
We use LLMs for the extraction and comparison as well, and we route between different models depending on the complexity of the comprehension of the given step required (and by this I mean routing between our pipeline steps; we currently do not dynamically try to judge individual cases for complexity like OpenRouter Fusion).
Document parsing is top of my mind lately because in some of the areas we work on the bottleneck is starting to become being able to query documents the same way one queries an api.
I keep thinking the most obvious analogue is we need some way to represent documents the same way we can represent structured data in parquet. Parquet allows easy range bases queries and there is so much tooling built around Arrow.
But for documents I keep hitting a wall to figure out what the right abstractions are. Parquet allows filterable metadata. But what such metadata is there for documents. Then there is the arbitrrariness of chunking, vectorization.
If we could just do this in a 2 step process where every document to process can be represented in a parquet like data format then I think we will atleast have the semblance of a solution.
100% the really hard challenge is that the intermediate representation (ie the parquet equivalent) will be dependent on the given use case. So what we do with the platform is have the users configure the intermediate layer that serves most of their queries, and if they need to extend it we will suggest it for them. For example for the demo on the grounded reasoning benchmark I referred to, here is what the intermediate layer looks like on top of which the agents can more efficiently query: https://demo.parsewise.ai/projects/39bee9d8-d722-4b23-8894-e...
I built a similar tool some time ago called Struktur (https://struktur.sh).
It’s much more limited in scope but fully open source and highly customisable. In fact it’s made for people to build their own pipelines on top of, providing the scaffolding needed to do so in a reliable way.
During development I’ve found it to be hard to truly generalise agent/llm-based data extraction, especially around the unlimited number of input types without task specific instructions (many files of the same kind, single large files, mixed kinds, bad quality files, docx/pdf/png/… the list goes on). Users sadly wanna upload all of these, and developers want a „one size fits all“ solution.
I am interested in how your solution deals with this. I came up with a strategy based approach so every task can be customised if needed, but I’d be delighted to see a technical writeup of how you deal with this endless variety of input + extraction task combos! :)
I'll need to check it out!
We had the same observation in that the possible space is almost endless, and for example even for the same file type there may be different kind of processing required (e.g. an excel can be database style, vs small narrative heavy, or both).
We have baked in some ground processing rules for different kinds of documents, and we do allow custom instructions on how to deal with specific cases (e.g. translations, particular format layouts). The best write-up I have at the moment is https://www.parsewise.ai/doc-processing-pipelines but we're working on something that goes into more detail:)
I am seeing my client using things like this heavily (not exactly this). Also, what I would call "business awareness" is declining.
I can see why, it's tempting to go for full automation. The reason we go for fine grained sourcing is so that people can build their awareness quickly. Plus many of our customers work in regulated industries where full automation is prohibited.
How portable are your agent definitions? If I build one for insurance documents, how much work is needed to adapt it to a completely different domain like legal contracts or healthcare?
In practice we find that each domain (and even each organisation) ends up having highly customized definitions.
At first, fairly generic templated definitions sort of work, but what we've seen is that over time data comes up that is out of distribution, and there was no explicit instruction on how to deal with it. In such cases we tend to flag this and offer suggestions to the users on how they can improve the specificity of agents.
Another structure we have seen play out is having a manager review ratings and feedback comments from their team and updating the definitions accordingly over time (where we offer them the capability to see results of before and after side -by-side for all existing data as well, so they are more confident in the change before committing).
The amount of work is dependent on how good the initial definitions are and how complex the use case is (and how much it evolves - new data sources etc). A bit of an unsatisfying answer but it can be anywhere between a few hours one off or a couple of minutes per day on an ongoing basis.
Interesting product! Do you think it would work for e-discovery? I have around 120GB of emails, contracts, and the like, and I need to search for data and where certain expressions are referenced.
Potentially, but at that scale cost and latency may actually become an issue, so probably better to consider some sort of indexing or keyword searching.
clickhouse?
This looks great for digital humanities, specifically archival work. Would love to try it.
Fully agree, that's why we quite like the Databricks OfficeQA benchmark.. it made us experts on historical US treasuries haha Some screenshots in here: https://www.parsewise.ai/officeqa-sota
I'm surprised at the low rate every model manages considering the (apparent) ease of the benchmarked document. Can your pipeline produce ground truth as a byproduct ? How do you think open-weight ocr models compare to the one showcased ? I've had good results with glm-ocr on complex documents (complex by their handwriting, pretty easy layouts).
What I like about your solution is the traceability of the information. A scruffy pipeline I used was gemini-flash 3.0 to pdf to notebook-lm (really amateurish work i know), but it yielded tremendeous time gains to extract info from documents (that could be borderline impossible to read for me). However, to trace back the info was obviously very tedious. But from my experience, notebooklm can now manage ocr/htr without a third party. I wonder how competitive your solution might be compared to messy workflows that work -- albeit with efforts -- but let's the researcher be "in contact" with the material.
What I really want is obviously an easy to setup local rag system, with the (very) light model that goes with it ... sweet dream.
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I worked recently on an internal tool to achieve this kind of things, mostly plugging mistral OCR to gemini to extract structured data from documents. We then perform automated diffs too.
There seems to be an insane amount of competition in the "Intelligent Document Processing" market, like for instance parseur, whose founder is often on HN himself.
What do you think sets you apart from competition like : 1) Mistral document AI : depending on the model, it looks way cheaper than yours, OCR model pricing ranges from 0.001 to 0.004 EUR / page and they have structured output wired in the OCR API if needed (things then get fed to one of their LLMs) + EU-based and GDPR ready 2) parseur / rossum / docsumo / nanonets (which is YC 2017) ?
Hi, Parseur founder here :D
I understand what they are trying to do, but to me it feels like the moment when MongoDB entered the database space, with semi-structured, "flexible" storage format. It has its uses, for prototyping mostly.
But in high-volume, production workloads, giving a structure to the data you extract (what Parseur does through defining the Fields in your Mailbox, basically giving your output data a schema) adds a ton of value, and the larger the dataset, the truer it is.
Usually, you start by defining where you want your data to go, and which structure it should have, before working backwards from here and starting to extract the data. This is the key to automating your document workflow.
Hey, good point about structure for integrated workflows:)
Fully agree, for enterprises we need to guarantee types, flag discrepancies and provide underlying sources so they can integrate it downstream (whether that's Databricks, n8n etc.)
Here is our documentation for working with a fixed JSON schema: https://docs.parsewise.ai/api#schema-driven-extract-convenie...
Great question!
1. We are working with the assumption that OCR is (or soon will be) solved at super low prices.
So if we have the extracted data, what can we do with it? Where we see Parsewise making a difference is for use cases that span across documents. I.e. if you are extracting the same 5 fields from every invoice, there are lots of solutions as you listed (+ reducto etc). However, once you have a set of documents (e.g. an entire mortgage application package) and you are trying to get a structured response out, then your option is either an LLM API (if things fit into context and you are okay with limited citations), or building a pipeline with LLMs. I posted it in another comment but an example of trawling through 90k pages is here: https://www.parsewise.ai/officeqa-sota
2. While we rely on LLMs, the outcomes will be non-deterministic, so the bottleneck is and will remain the human verification (that is for somewhat complex use cases). The architecture that we have built is optimizing for the human reviewer to provide as granular values and citations as possible. This is either through our platform, or API clients.
Hey ! Is this kind of like structured output over a large scale document corpora ?
Hey, that's exactly it!
I say this with a lot of love: The vibecoded applications in your demo reek of AI slop design.
This isn't a critique of your product. It's just that the a beige-orange theme, the pill components, and the left-border highlight give me that visceral reaction as reading a paragraph littered with em dashes and "not X but Y." It makes me take you less seriously.
Cool demo otherwise.
Haha no appreciate it! That's on me for not calling it out explicitly (was trying to make the video as short as possible), but the demo UIs were literally vibe coded to show the ease of integration https://youtu.be/F1cSuZal03s?si=1H4zTcO-8cosLbVr&t=70
llamaparse also do it, what is different here?
Similar to my other comment, we assume that llamaparse and others can provide the individual page OCR. But once you have that the way that you can integrate it into your workflows often requires additional complexity around combining results from different sources. Here is a deeper dive I wrote on the complexities of building extraction pipelines: https://www.parsewise.ai/doc-processing-pipelines
Mostly cross-doc reasoning at scale (e.g., 90k-page corpora) as opposed to doc-to-markdown conversions.
Just use claude. Not another wrapper
If Claude is good enough for your use case then for sure. If you need scale, persistent structure and verifiability we can help:)
Ah probably should add a link to our website: https://www.parsewise.ai/api
Added above :)
"retaining lineage"
"That is a great catch!"
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A launch post is not a place to attack other users personally. Neither is any other HN thread for that matter, so please don't do it here.
https://news.ycombinator.com/newsguidelines.html
I do respect your moderation, however I addressed the statement, the choice of words, not the person.
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I learnt a lot at Palantir, though always worked in commercial so no ties to security state (for the better or worse). (Also side-note, we are working towards enabling frontier performance with smaller open models that allows our customers to protect their data. https://www.parsewise.ai/officeqa-sota )
And I do get genuine joy from helping our users, so love it is:)
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