If you happen to’ve ever burned hours wrangling PDFs, screenshots, or Phrase information into one thing an agent can use, you understand how brittle OCR and one-off scripts could be. They break on format 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 era (RAG) pipelines mature, they’re turning into “structure-aware,” as a result of flat OCR collapse beneath the load 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 possibility: 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 affect exhibits up in three key methods:
Straightforward 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 sooner 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 end result: easier 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, decreasing 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 kind.
Structure preservation for exact retrieval
Doc hierarchy and tables are retained, so solutions reference the fitting sections and cells as a substitute of collapsing into flat textual content. Retrieval stays grounded, and citations really level to the fitting spot.
Seamless downstream use
Outputs move instantly into DataRobot workflows for retrieval, prompting, or perform 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 important hole within the agent workflow. Most level instruments or DIY scripts stall on the handoffs, breaking when layouts shift or pipelines develop.
This integration is a part of a much bigger shift: shifting from toy demos to brokers that may cause over actual enterprise data, with governance and reliability in-built to allow them to rise up in manufacturing.
Which means you’ll be able to construct, function, and govern agentic functions in a single place, with out juggling separate parsers, glue code, or fragile pipelines. It’s a foundational step in enabling brokers that may cause 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 information, 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 greatest blockers to production-ready brokers.
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.
Begin a free trial and expertise how rapidly you’ll be able to flip unstructured paperwork into structured, agent-ready inputs. Questions? Attain out to our crew.
