A regulatory filing shouldnβt become an all-consuming manual data-entry project.
Unstructured takes dense PDFs, scanned filings, earnings statements, and spreadsheets and turn them into structured data your teams can actually use.
Revenue. Capital ratios. Provisions. Tables. Dates. Jurisdictions. All extracted with the source and metadata needed for auditability.
From there, that data can flow straight into reporting dashboards, compliance systems, or AI applications.
What used to take hours of manual review can become usable data in a fraction of the time. π
Learn more about how Unstructured supports reporting, compliance, and AI across financial services: https://t.co/UIYapDyQ1O
Unstructured has been awarded a $2 million Army contract to support PdM AIOPS under CPE C2IN, operationalizing AI-ready multimodal data pipelines that detect anomalies in sensor data and compress the sensor-to-decision timeline.
https://t.co/tTu0jNyhhm
Tomorrow, our founder & CEO @_Brian_Raymond joins @Teradata CPO, Sumeet Arora, to talk multi-modal context and enterprise AI.
Together, theyβll dig into how Teradata + Unstructured help connect unstructured content with enterprise data to build richer, more trusted context for AI.
Make sure to tune in tomorrow morning! π₯ https://t.co/JKRv1FwqDR
All set up and ready for another full day of events!
Excited to be at the Hurlburt Field Innovation Expo today. Make sure to swing by our booth to learn how Unstructured transforms complex documents into clean, structured, agent-ready data.
Metadata might not be the buzziest word in AI, but it may be one of the most important.
Our CEO @_Brian_Raymond took the stage at @Teradata's Autonomous Intelligence World Tour to break down why the next wave of enterprise agents depends on context thatβs not only accessible, but *actually usable*
Tomorrow, our founder & CEO @_Brian_Raymond joins @Teradata CPO, Sumeet Arora, to talk multi-modal context and enterprise AI.
Together, theyβll dig into how Teradata + Unstructured help connect unstructured content with enterprise data to build richer, more trusted context for AI.
Make sure to tune in tomorrow morning! π₯ https://t.co/JKRv1FwqDR
ποΈ Ever wonder how your parser knows where to start reading in a newspaper layout?
Reading order isn't as obvious as it looks. Columns, pull quotes, captions, a headline spanning three of them. Read it straight across and the text turns into nonsense.
Unstructured works out the exact sequence a human would actually follow, then labels and extracts each piece in that logical flow.
See it on your own documents: https://t.co/Rlx9MCfIcX
Fine-tuning Object Detection for documents is harder than it looks.
The short version: Object Detection (OD) is the foundation under every document pipeline, and small inconsistencies in bounding boxes cascade into measurable errors in OCR, reading order, and table structure.
Getting it right takes consistent annotations, careful control of data distribution, and a lot of debugging that most teams underestimate. π«
If you've ever assumed OD is a solved problem, you'll wanna check this out: https://t.co/fWhuBLuH4J
We're at #AFANational!! βοΈ
If you're attending, keep an eye out for Marshall Leipprandt π He'll be there, ready to chat all things Air and Space Forces.
Learn more about how Unstructured helps companies deliver next-generation agentic AI systems using mission-ready data: https://t.co/FpuWU0jVlO
We're heading to WPAFB next week! βοΈ
Stop by our booth to hear about how Unstructured transforms and orchestrates complex, multimodal data, powering the next generation of AI mission applications.
π Wed, Sep 16
β° 9 AM - 1 PM
π Dayton, OH
See you there!
"We already use Textract, it works fine, why would we add Unstructured?"
It does work fine, until the requirements grow. Then it is scanned PDFs, complex tables, slide decks, multi-cloud, chunking, enrichment, retrieval, and suddenly your document pipeline is an infrastructure project in disguise, owned by people who never signed up to maintain one.
The honest math is not the per-page rate. It is the engineering months you spend holding the homegrown version together.
We wrote up where DIY quietly gets expensive: https://t.co/jsr0s7su77
Good document parsing starts before the parsing itself. A deeper look at the role object detection plays behind the scenes, and why getting it right matters π
Everyone's talking about VLMs parsing documents end to end.
But before a page can be read, something has to know where the tables, headers, blocks, and formulas actually are.
That's Object Detection.
Get the boxes wrong and everything downstream gets messy.
Bad crops.
Brittle prompts.
Bloated cleanup code.
Worse OCR, reading order, and table extraction.
So we went deep on fine tuning our own OD model π€
Turns out, the βjust add data and trainβ story is... not quite it.
Full write up: https://t.co/fWhuBLuH4J
Some text is built to be misread.
Rotated sideways, stamped as a watermark, set in a font nobody has shipped since 1998, or written in a script your OCR never trained on.
Traditional OCR reads it wrong, and that wrong text lives in your index forever.
Generative OCR catches those cases and routes them to a vision language model, so the hard-to-read parts come out accurate instead of quietly corrupted.
Try it on your own docs: https://t.co/Rlx9MCfIcX
Document parsing changed more in the last year than in the five before it. VLMs went from novelty to the default way serious teams pull structure out of hard documents.
Our "State of Generative Parsing 2026" whitepaper lays out where the field actually is: what generative parsing does well, where it still breaks, and how to evaluate it without fooling yourself.
Read more! π https://t.co/bHNLUdDCja
Nobody wants to babysit a job queue.
Unstructured webhooks push event data, job scheduled, started, stopped, failed, or completed, straight to the receiver of your choice, like Lambda, Azure Functions, or Slack.
Setup is two API calls: one to create the channel, one to verify it.
Let your inbox or your Slack do the watching so you can work on what actually matters π
Check it out: https://t.co/cMIaZyn5cs
Semantic search alone will happily hand your agent the right answer... from the *wrong* document.
The old version. The wrong region. Last year's policy.
Metadata filtering narrows retrieval before similarity ever runs. Unstructured generates a lot of it during processing, filename, page, element type, languages, and enrichments add more like named entities.
Filter first, then retrieve, and the answers stop coming from the wrong place.
Check out what metadata Unstructured extracts by default in our docs: https://t.co/fXGU49ei0n
An image inside a document is invisible to your retriever. Diagrams, charts, screenshots full of text: none of it is searchable unless something describes what's in it.
Image description enrichment writes a text description of each image as the document is processed, so the picture becomes something your RAG system can find and reason about.
Now "find the architecture diagram with the load balancer" is a real query!
Learn More: https://t.co/pl73bdnNnD