AI multi-document processing for complete file sets
Extract the same answers from every document without skipping files or silently dropping missing fields. Continua treats each uploaded document as an independent extraction target, checks it against every requested question and returns one consistent, cited comparison matrix.
No credit card required · Uploaded files deleted after the task
What is AI multi-document processing?
AI multi-document processing applies the same extraction or review questions to a collection of files. Continua creates one row per document and one column per requested field, searches the full document for every question, combines relevant passages when needed and preserves missing answers as “Not found” or “Cannot determine.”
One row per document. One answer per question.
Illustrative batch contract extraction
| Document | Renewal term | Termination for convenience | Liability cap | Change of control | Evidence status |
|---|---|---|---|---|---|
| Vendor A Agreement | Auto-renews for 12 months | 30 days’ notice | Fees paid in prior 12 months | Consent required | All fields cited |
| Vendor B MSA | No automatic renewal | Not found | 2× annual fees | Notice required | One field not found |
| Vendor C Order + Terms | 24-month initial term | 60 days after first year | Cannot determine from provided files | Not found | Missing referenced schedule |
Review finding: Vendor C’s order form incorporates a liability schedule that is not present in the uploaded set. The document remains in the matrix, and the unresolved fields stay visible for follow-up.
This is an illustrative example. Continua keeps extracted answers connected to the relevant document, page, section, sheet or source location.
Batch extraction fails when recall is hidden
Many extraction workflows produce a polished table without making omissions visible. A file may disappear because it lacks an obvious match. One question may be answered for the first few documents but not the rest. A value may sit in a schedule, definition, footnote or amendment rather than the expected section.
A complete batch review must answer four coverage questions:
- Was every input document processed?
- Was every question applied to every document?
- Were all requested subfields considered?
- Does each material answer have supporting evidence?
Continua is designed to keep those coverage dimensions visible before composing the final matrix.
What multi-document processing includes
Document recall
Keep every input document represented in the output, including files with missing or indeterminate answers.
Question recall
Apply every user question to every document rather than stopping after a partial match or treating the collection as one blended source.
Field-level extraction
Break compound questions into their requested components and return a consistent field structure across the document set.
Full-document search
Look beyond obvious headings to definitions, tables, schedules, exhibits, appendices, footnotes, amendments, certificates, notices and body text.
Missing-value preservation
Use clear statuses such as “Not found,” “Cannot determine” or “Conflicting evidence” instead of dropping a row or filling the gap with a guess.
Evidence coverage
Attach citations to material answers and combine multiple passages when a conclusion depends on more than one location.
Consistent comparison matrix
Return exactly one row per document and one column per question, making the result easier to compare, filter or export.
Ask the complete folder one extraction question
Process every uploaded document independently. Create exactly one row per document and one column for each requested question or subfield. Search the full document—including definitions, tables, schedules, exhibits, appendices, footnotes, amendments and body text—for every field. Combine relevant evidence when needed. If an answer is missing, ambiguous or unsupported, keep the document in the matrix and mark the field “Not found,” “Cannot determine” or “Conflicting evidence.” Cite every material answer and include a coverage check showing that every document and question was processed.
Multi-document processing use cases
Contract abstraction
Extract renewal, termination, pricing, liability, assignment, notice and change-of-control fields from every agreement.
Application and submission review
Compare eligibility, qualifications, requested amounts, missing forms and stated evidence across a folder of applications.
Invoice and statement extraction
Create a consistent matrix of dates, counterparties, totals, terms, exceptions and missing fields across financial documents.
Policy and procedure review
Compare scope, owner, effective date, approval, required controls and exceptions across a policy library.
Research-paper extraction
Capture population, sample size, methods, interventions, outcomes, limitations and dates from every study before synthesis.
Proposal or vendor comparison
Extract answers to the same commercial, technical, security and implementation questions from each response.
From a folder to a cited comparison matrix
- 01Upload the complete document set. Include every file that should appear as a row in the result.
- 02Define the columns. List the questions, fields and subcomponents to extract from each document.
- 03Process every file independently. Continua searches the full content of each document for every requested field.
- 04Review coverage and exceptions. Inspect missing, ambiguous, conflicting and multi-source answers.
- 05Open evidence and export. Verify source citations and export the consistent matrix for further review.
Multi-document processing vs. sampling or folder-wide chat
| Sampling or blended folder chat | Continua multi-document processing |
|---|---|
| May answer from the strongest matches | Treats every document as an extraction target |
| Blends files into one narrative | Preserves exactly one row per document |
| Can apply questions inconsistently | Checks every document against every question |
| Often drops missing values | Keeps missing and indeterminate fields visible |
| May stop at the first matching passage | Combines relevant evidence across the full file |
| Makes coverage difficult to verify | Includes source citations and explicit coverage |
Questions a document matrix can answer
- Did every uploaded document receive a complete review?
- Which files contain—or do not contain—a requested field?
- How do the same terms or values differ across documents?
- Which answers require evidence from multiple passages?
- Which referenced schedules or attachments are missing?
- Where is the evidence ambiguous or contradictory?
- Which documents require follow-up?
- Can the result be reviewed as one consistent table?
Supported files, security and scope
Continua can analyze PDF, DOCX, XLSX and CSV files together. Uploaded files are deleted after the task and are not used to train AI models. Material extracted values remain linked to supporting evidence.
“Not found” means the field was not located in the uploaded file; it does not prove that the information does not exist elsewhere. Scanned or poorly structured source files may also require human verification of critical values.
Related Continua workflows
Frequently asked questions
What happens if one document is missing an answer?
The document remains in the matrix. The missing field can be marked “Not found” or “Cannot determine,” with any relevant missing attachment or ambiguity noted for follow-up.
Can I ask multiple extraction questions at once?
Yes. Each question or requested subfield can become a matrix column, and every document is checked against each one.
Does Continua stop after the first matching passage?
No. The workflow is designed to search the full document and combine relevant passages when the answer depends on multiple sections or attachments.
Can it process PDFs and spreadsheets together?
Yes. Continua supports PDF, DOCX, XLSX and CSV files in the same task, although the requested fields should be defined consistently for meaningful comparison.
Does every extracted value include a citation?
Material answers can link to the supporting document and source location. If a conclusion requires multiple passages, the report can preserve all relevant citations.
Is AI multi-document processing the same as OCR?
No. OCR converts images of text into machine-readable text. Multi-document processing applies questions and structured extraction across complete files and then organizes the results for comparison.
Process the whole folder, not a sample of it
Upload the complete file set and get one consistent matrix with every document, every question, every missing field and the supporting evidence visible.
No credit card required · Zero document retention
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