AbraCalc

Document Review Time Calculator

Estimate the time and cost to review a document set based on page count, review speed, and hourly rate.

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APA

AbraCalc. (2026). Document Review Time Calculator [Online calculator]. Retrieved from https://abracalc.com/calculator/document-review-time-calculator/

BibTeX

@misc{abracalc-document-review-time-calculator, author = {AbraCalc}, title = {Document Review Time Calculator}, year = {2026}, howpublished = {\url{https://abracalc.com/calculator/document-review-time-calculator/}} }

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How to use this tool

  1. Enter number of pages, minutes per page and reviewer hourly rate in the fields above.
  2. Results update instantly as you type — or click Calculate.
  3. Read your estimated review time and the full breakdown beneath it.

This is an estimate, not legal advice. Laws vary by jurisdiction and individual circumstances. Consult a qualified attorney before making any legal decisions.

Estimate attorney or paralegal time and cost for reviewing a document production. Actual review speed depends on document complexity, language, and the reviewer's familiarity with the subject matter.

⚠ This tool provides general estimates for education only and is not financial, tax or legal advice. Figures may not reflect your situation — verify with a qualified professional.

Formula

Review hours = (pages × minutes per page) ÷ 60

Review cost = review hours × hourly rate

How it works

This calculator estimates the time and cost to review a document set by converting a per-page review speed (in minutes) into total hours, then multiplying by the reviewer's hourly billing rate. The minutes-per-page input is the primary driver of accuracy; realistic values range from 1–2 minutes for straightforward documents to 5–10 minutes for complex technical or legal materials. The estimate assumes continuous review at a constant pace and does not account for breaks, re-review, privilege logging, or supervision overhead.

Worked example

  1. Total review minutes = 100 pages × 3 min/page = 300 minutes
  2. Review hours = 300 ÷ 60 = 5 hours
  3. Review cost = 5 hours × $200/hr = $1,000

Estimated review time of 5 hours at an estimated cost of $1,000 for 100 pages at 3 min/page and $200/hr.

Common mistakes to avoid

  • Applying a single review speed to all document types -- dense financial records take far longer per page than routine correspondence, so a blended rate produces an inaccurate project estimate.
  • Omitting quality-control time, typically 10-20% of first-pass review hours, from the total cost estimate.
  • Treating review hours as uninterrupted continuous work -- attorney fatigue reduces accuracy on large document sets; production schedules should build in breaks or rotating reviewers.

Key terms

Document review
The process of examining documents (often in litigation or due diligence) to identify relevance, privilege, or key facts.
Review speed (min/page)
The average time a reviewer spends per page; varies by document complexity, language, and reviewer expertise.
Privilege log
A list of documents withheld from disclosure on grounds of attorney-client privilege or work-product protection, adding time to a review project.
Hourly billing rate
The per-hour charge for a reviewer's time, which may differ between junior associates, paralegals, and senior attorneys.
e-Discovery
Electronic discovery — the process of identifying, collecting, and reviewing electronically stored information for legal proceedings.

Frequently asked questions

How many pages per hour can a reviewer process?
Speeds vary widely. A basic relevance review may achieve 60–100 pages per hour; a detailed privilege and issue-code review may be as slow as 15–30 pages per hour. Technology-Assisted Review (TAR) can increase throughput substantially.
What is e-discovery?
E-discovery (electronic discovery) is the process of identifying, collecting, and reviewing electronically stored information (ESI) for litigation. It often involves enormous document volumes measured in gigabytes or terabytes.
Can I reduce review costs?
Yes. Culling documents using search terms, date filters, and deduplication before review can dramatically reduce the review population. TAR/predictive coding tools further reduce manual review volume.

References & sources