If you happen to’ve ever burned hours wrangling PDFs, screenshots, or Phrase information into one thing an agent can use, you know the way brittle OCR and one-off scripts might be. They break on structure modifications, lose tables, and gradual launches.
This isn’t simply an occasional nuisance. Analysts estimate that ~80% of enterprise information is unstructured. And as retrieval-augmented era (RAG) pipelines mature, they’re changing into “structure-aware,” as a result of flat OCR collapse beneath the burden of real-world paperwork.
Unstructured information is the bottleneck. Most agent workflows stall as a result of paperwork are messy and inconsistent, and parsing rapidly turns right into a aspect venture 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 influence 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 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: 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, reducing upkeep overhead and reducing 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.
Format preservation for exact retrieval
Doc hierarchy and tables are retained, so solutions reference the fitting sections and cells as an alternative of collapsing into flat textual content. Retrieval stays grounded, and citations really level to the fitting spot.
Seamless downstream use
Outputs move straight 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 crucial hole within the agent workflow. Most level instruments or DIY scripts stall on the handoffs, breaking when layouts shift or pipelines broaden.
This integration is a part of an even bigger shift: transferring from toy demos to brokers that may purpose over actual enterprise information, with governance and reliability inbuilt to allow them to rise up in manufacturing.
Meaning you possibly can 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 purpose over actual enterprise information with confidence.
From bottleneck to constructing block
Unstructured information 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 attempt 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 possibly can flip unstructured paperwork into structured, agent-ready inputs. Questions? Attain out to our workforce.
