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Keying documents stops being a job
Whatever arrives — scanned PDFs, tables, photographs, awkward multi-column layouts — gets read and turned into structured data, without a template per supplier.
4 Documents AI Agents, Live in Production.
4 live documents agents across 2 processes — extraction and classification across real-world file formats.
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Document Comparison
Compare two versions of a document side by side and list every substantive change, separating what alters meaning from what only alters wording.
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Document Summarization
Upload a document and get a structured summary with key metadata and topics. Long documents are summarized section by section with rolling context.
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Document Translation
Translate a document section by section, carrying terminology forward so the whole reads as one piece rather than a set of fragments.
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OCR Data Extractor
Read a scanned invoice or receipt and pull out the header fields and line items as structured data.
Document AI Agents: Reading Long Documents Without Losing the Thread
Long documents defeat both people and naive automation, for opposite reasons. A person reading a hundred-page agreement for the third time stops noticing the changes; a tool that summarises it in one pass loses everything past its context limit and produces a summary weighted toward the beginning. The same problem shows up in translation, where handling a document section by section without carrying terminology forward produces text where the same term is rendered three different ways — technically correct throughout and unusable as a single document.
The agents here are built around that specific failure. Summarisation processes long documents section by section with rolling context, so the later sections are summarised knowing what came before. Translation carries terminology forward for the same reason. Comparison does the thing a person reliably cannot do across two versions of a long document — find every substantive change — and then does something more useful than listing them all, which is separating what alters meaning from what only alters wording. That distinction is the entire value of a comparison: a redline with four hundred entries is not a comparison, it is a diff, and someone still has to read all of it.
Document Intelligence
Separating changes that alter meaning from changes that only alter wording
What this moves
- Review time per document version
- Substantive changes are separated from cosmetic ones, so a comparison produces a short list to read rather than a four-hundred-line diff.
- Summary quality on long documents
- Sections are summarised with rolling context, so the later half of a document is not lost to a context limit or under-weighted.
- Terminology consistency in translation
- Terms are carried forward across sections, so the result reads as one document rather than a set of independently translated fragments.
Data Extraction
Pulling header fields and line items out of a scan as structured data
What this moves
- Manual keying per scanned document
- Header fields and line items are extracted as structured data, so a scan becomes a record without someone reading it into a form.
- Documents that can enter an automated process
- A scanned invoice or receipt becomes machine-readable, which is the prerequisite for every downstream check that currently cannot run on paper.
What this changes
What documents agents do for the business
In plain terms, without the engineering detail. The individual agent pages carry the technical specifics.
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Uncertain values never reach your systems
Every field carries a confidence score. Anything below the threshold goes to a person instead of flowing through, which is the difference between useful extraction and a data-quality incident a month later.
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It survives format changes
Template-based tools break the moment a supplier redesigns an invoice. These read documents by meaning and position, so a new layout is not a new project.
- Handles format variation as it arrives
- No templates
- Confidence scoring, not per document
- Per field
- Where low-confidence values go
- To review
Where to start
Start with whichever document you receive most of. Volume is what makes this pay, and the accuracy question is easiest to settle when you have hundreds of past examples to check the agent against.
Next Step
Deploying documents agents into your stack
These run as-is, and most engagements adapt one to the way your process actually works — different source systems, different tolerances, a different approval path. The first call establishes which base agent fits and what has to change.