Organize sources
Bring relevant documents into a repository and define who needs access. Preserve document context and provenance when preparing information.
Knowledge and documents
Document Intelligence: RAG, AI Extraction & Comparison
Find answers with document references, extract structured fields and compare document versions. Choose the right AI workflow for reports, contracts and business files.
Solutions / NEXTBRAIN AI
THE CHALLENGE
How much work goes into finding information you already have?
Useful information is spread across reports, contracts and different file formats. Finding a paragraph, checking a clause or copying fields into a table takes time and complicates traceability. These tasks do not all call for the same kind of query.
FROM DATA TO DECISION
A process you can understand and evaluate.
Query or structure
Use knowledge retrieval for questions with source references. For repeated fields across many documents, define structured extraction that can be reviewed and reused.
Review and connect
Check answers against source passages, inspect extracted values and connect results to the process. Document comparison supports reviewing changes between versions.
Starting data
- Reports, contracts and files
- Questions or fields to extract
- Sources and access permissions
Decisions you can evaluate
- Answers with references
- Reviewable structured fields
- Document differences
Technology that serves the problem
A RAG repository retrieves relevant context for a language model to prepare an answer. Document extraction produces structured fields; comparison identifies changes. Choosing the right workflow avoids asking conversational search to perform systematic extraction.
DOCUMENT AI / FROM SOURCES TO ACTION
Intelligent Document Processing, RAG and Document Comparison
Searching a collection, extracting repeated fields and comparing versions solve different document problems. The first step is to identify the output your team needs: a supported answer, a structured record or a review of changes. NextBrain provides distinct workflows for those tasks so people can check the result against the original source.
Choose the workflow that fits the document task
| Workflow | Best suited to | Output to review |
|---|---|---|
| Knowledge search with RAG | Asking questions across a collection of reports, policies or reference documents. | An answer with document references and the relevant source passages. |
| Structured document extraction | Collecting the same defined fields from a set of documents. | A consistent set of values, with missing or ambiguous information reviewed. |
| Document comparison | Understanding what changed between two versions of a document. | Differences that a reviewer can inspect in the context of both versions. |
ILLUSTRATIVE WORKFLOW
Worked example: reviewing a revised supplier contract
Consider a team receiving an updated supplier contract. They need to locate the existing terms, capture key fields and understand what changed. This illustrative workflow combines the relevant document capabilities without treating an AI answer as a completed legal review.
- 01
Find the relevant source
Search the approved reference collection for the existing agreement or policy. Open the cited passage to confirm the answer refers to the correct document and version.
- 02
Define the fields to capture
Specify fields such as supplier name, contract dates and payment terms. Review extracted values against the document, including items that are absent, unclear or spread across multiple sections.
- 03
Compare the versions
Review the previous and revised documents together. Inspect the differences that matter to the process, rather than relying only on a generated summary of the change.
- 04
Record the reviewed result
Keep the source versions and the team’s reviewed findings together. The responsible person decides which changes need clarification or follow-up.
How AI turns document content into usable information
Retrieval-augmented generation (RAG) finds relevant document context for a language model to use in an answer. Structured extraction focuses on defined fields, while document comparison helps inspect changes between versions. Source references help the reviewer locate evidence; their presence alone does not establish that an answer is correct.
Evaluate on the documents your team actually uses
- Questions with known answers and questions the collection cannot answer.
- Field-level extraction checks, including missing values and ambiguous wording.
- Version comparisons with meaningful additions, removals and changed terms.
- Document quality, supported formats and access requirements agreed during scoping.
The contract workflow is illustrative. Format coverage and document quality need to be checked with representative files, and material findings need review by the responsible team.
How to evaluate value
Evaluate source support, extraction correctness and end-to-end review effort. Include difficult documents and questions whose answers are absent from the repository.
Questions about this solution
When should I use RAG rather than extraction?
RAG suits knowledge exploration and questions. Extraction suits retrieving the same fields from many documents in a consistent structure.
Can I check where an answer comes from?
The repository interface exposes references and document passages. Evaluation should verify that those references actually support the answer.
Can document search, extraction and comparison work together?
Yes. A workflow can locate reference information, capture defined fields and review differences between document versions. Each output should be checked against the relevant source.
How should document AI quality be evaluated?
Use representative files, known-answer questions, missing-answer questions, field-level extraction checks and meaningful version changes. Review the full process, including the time needed to check outputs.
Your next step starts with a good question.
Tell us which decision is difficult, what data you have and which constraints matter. We will review how the approach fits your problem.