AI Sandbox

Test document summaries against the passages that support them

Reviewers spend significant time extracting decisions, obligations and differences from lengthy documents. A sandbox tests a fixed summary format and makes every important statement traceable.

Intended result: Measured evidence about extraction accuracy, source traceability and reviewer time saved.
Human approval: Required
Integration: Not necessarily

Situation

Reviewers spend significant time extracting decisions, obligations and differences from lengthy documents. A sandbox tests a fixed summary format and makes every important statement traceable.

Current problem

Reviewers repeatedly locate key clauses, changes and obligations across long or inconsistent documents.

Desired change

The prototype produces a structured summary with source passages, missing sections and uncertainty markers.

Possible workflow

Define

Set the review questions

Choose the fields, obligations, differences and risk signals that must be extracted.
Generate

Create traceable summaries

Parse documents and link structured statements to supporting passages.
Compare

Evaluate against expert review

Measure omissions, incorrect interpretations, citation quality and time saved.

Human control

A qualified reviewer checks completeness, interpretation and the final decision.

Technology and data

Document parsing: Extraction that preserves pages, sections, tables and source locations.
Structured generation: A language model constrained to an agreed summary format.
Evaluation set: Representative documents with expert-reviewed expected findings.

Boundaries

A concise summary may omit important surrounding context.
Scans, tables and complex layouts need separate parsing checks.
Unclear source passages must be marked rather than resolved by guessing.
The reviewer remains accountable for interpretation.
Summaries can omit material context
Document parsing quality varies
A qualified reviewer must verify the result