In the event you’ve ever burned hours wrangling PDFs, screenshots, or Phrase recordsdata into one thing an agent can use, you know the way brittle OCR and one-off scripts will be. They break on structure adjustments, lose tables, and sluggish launches.
This isn’t simply an occasional nuisance. Analysts estimate that ~80% of enterprise knowledge is unstructured. And as retrieval-augmented technology (RAG) pipelines mature, they’re changing into “structure-aware,” as a result of flat OCR collapse beneath the burden of real-world paperwork.
Unstructured knowledge is the bottleneck. Most agent workflows stall as a result of paperwork are messy and inconsistent, and parsing rapidly turns right into a facet undertaking that expands scope.
However there’s a greater choice: Aryn DocParse, now built-in into DataRobot, lets brokers flip messy paperwork into structured fields reliably and at scale, with out customized parsing code.
What used to take days of scripting and troubleshooting can now take minutes: join a supply — even scanned PDFs — and feed structured outputs straight into RAG or instruments. Preserving construction (headings, sections, tables, figures) reduces silent errors that trigger rework, and solutions enhance as a result of brokers retain the hierarchy and desk context wanted for correct retrieval and grounded reasoning.
Why this integration issues
For builders and practitioners, this isn’t nearly comfort. It’s about whether or not your agent workflows make it to manufacturing with out breaking beneath the chaos of real-world doc codecs.
The impression reveals up in three key methods:
Simple doc prep
What used to take days of scripting and cleanup now occurs in a single step. Groups can add a brand new supply — even scanned PDFs — and feed it into RAG pipelines the identical day, with fewer scripts to keep up and quicker time to manufacturing.
Structured, context-rich outputs
DocParse preserves hierarchy and semantics, so brokers can inform the distinction between an govt abstract and a physique paragraph, or a desk cell and surrounding textual content. The consequence: less complicated prompts, clearer citations, and extra correct solutions.
Extra dependable pipelines at scale
A standardized output schema reduces breakage when doc layouts change. Constructed-in OCR and desk extraction deal with scans with out hand-tuned regex, reducing upkeep overhead and slicing down on incident noise.
What you are able to do with it
Below the hood, the mixing brings collectively 4 capabilities practitioners have been asking for:
Broad format protection
From PDFs and Phrase docs to PowerPoint slides and customary picture codecs, DocParse handles the codecs that often journey up pipelines — so that you don’t want separate parsers for each file sort.
Format preservation for exact retrieval
Doc hierarchy and tables are retained, so solutions reference the suitable sections and cells as a substitute of collapsing into flat textual content. Retrieval stays grounded, and citations truly level to the suitable spot.
Seamless downstream use
Outputs move immediately into DataRobot workflows for retrieval, prompting, or operate instruments. No glue code, no brittle handoffs — simply structured inputs prepared for brokers.
One place to construct, function, and govern AI brokers
This integration isn’t nearly cleaner doc parsing. It closes a vital hole within the agent workflow. Most level instruments or DIY scripts stall on the handoffs, breaking when layouts shift or pipelines increase.
This integration is a part of an even bigger shift: transferring from toy demos to brokers that may motive over actual enterprise data, with governance and reliability inbuilt to allow them to get up in manufacturing.
Which means you may build, operate, and govern agentic applications in one place, with out juggling separate parsers, glue code, or fragile pipelines. It’s a foundational step in enabling brokers that may motive over actual enterprise data with confidence.
From bottleneck to constructing block
Unstructured knowledge doesn’t need to be the step that stalls your agent workflows. With Aryn now built-in into DataRobot, brokers can deal with PDFs, Phrase recordsdata, slides, and scans like clear, structured inputs — no brittle parsing required.
Join a supply, parse to structured JSON, and feed it into RAG or instruments the identical day. It’s a easy change that removes one of many largest blockers to production-ready agents.
One of the best ways to grasp the distinction is to strive it by yourself messy PDFs, slides, or scans, and see how a lot smoother your workflows run when construction is preserved finish to finish.
Start a free trial and expertise how rapidly you may flip unstructured paperwork into structured, agent-ready inputs. Questions? Reach out to our team.
