Report · estimate
Sort 500 Physical Archived Document Boxes for Litigation Relevance
“Manually sort through 500 physical boxes of archived documents to identify and extract those relevant to a lawsuit”
Summary · Manually review 500 physical boxes of archived documents to identify and extract materials relevant to litigation — a classic e-discovery/document review task applied to physical paper records.
The task is inherently physical — 500 boxes of paper cannot be touched by AI. Even with full digitization as a prerequisite (a large, costly project in itself), AI-assisted review is a meaningful accelerant but not a replacement for attorney judgment on privilege and relevance calls. Courts may not accept fully automated privilege determinations. AI is a useful augment post-digitization, not a direct solution to this task as stated.
Where AI helps most
Digitizing the documents first and then using AI-assisted predictive coding (TAR) to prioritize the review set, so human attorneys only closely review the highest-probability-relevant documents rather than every page.
10× / week
120 hrs
saved per week using AI
Worker comparison
six profiles| Worker | Time | Cost | What you actually get | Conf. |
|---|---|---|---|---|
|
01
Solo Individual
DIY on your own time, no contract, no schedule
|
8–16 weeks full-time (rough, exhausting, error-prone) | $0 direct cost if self-performed, but extreme opportunity cost; if hiring a temp, $15–20/hr blended out-of-pocket | A non-specialist will have no framework for relevance determinations, privilege identification, or chain-of-custody documentation — all of which matter enormously in litigation. They won't know what a litigation hold is, may inadvertently destroy or mishandle privileged materials, and could expose the party to sanctions. Fatigue sets in fast with physical document review, dramatically increasing error rates over time. No audit trail. No defensible process. Opposing counsel can challenge the entire review. Revision is essentially impossible since documents may have already been disturbed. Genuinely dangerous for a lawsuit context. | medium |
|
02
Solo Expert
Hire a freelance specialist, day rate, scoped per job
|
3–6 weeks (a single experienced document review attorney or paralegal working methodically) | $4,000–$15,000 depending on hourly rate ($40–$80/hr paralegal or $100–$200/hr contract attorney, full-time effort) | An experienced reviewer will bring a review protocol, relevance criteria checklist, privilege log discipline, and chain-of-custody awareness. Quality is substantially better than a layperson. However, a single reviewer is a bottleneck: fatigue, illness, or schedule conflicts can stall a deadline-sensitive matter. No second-eye review means privilege calls and relevance judgment are unchecked. Calendar time is long — expect weeks of wall-clock time. Scope creep risk is real if the relevance criteria are poorly defined upfront. Disputes about what was or wasn't reviewed are hard to resolve after the fact. | medium |
|
03
Small Team
Coordinate 2 or 3 freelancers, handoffs and gaps
|
1–3 weeks with 2–3 reviewers working in parallel | $8,000–$30,000 depending on reviewer seniority and hours | Parallel review cuts calendar time significantly, but introduces consistency risk — reviewers may apply relevance and privilege standards differently without tight calibration sessions. A team lead needs to reconcile conflicts, which adds coordination overhead. Physical logistics (shared workspace, box tracking, who reviewed what) need to be managed explicitly or documents get reviewed twice or missed entirely. Engagement friction includes aligning schedules, securing a shared physical workspace, and negotiating scope before work begins. Defensibility is better than a solo reviewer if a QC protocol is documented. | medium |
|
04
Agency
Account-managed, billable hours, formal scope and SOW
|
1–2 weeks with a staffed document review project | $20,000–$60,000+ depending on volume, staffing, and whether a privilege log and production set are included | Legal staffing agencies and litigation support firms have done this before: they'll bring project managers, reviewers in batches, standardized relevance coding forms, and defensible audit logs. Physical document handling, Bates numbering, and privilege logging can all be scoped in. However, agency billing can escalate sharply as scope expands (additional boxes, second-pass review, privilege disputes). Client must invest significant time upfront in a detailed relevance protocol briefing — without it, reviewers code incorrectly and rework is expensive. Turnaround commitments on paper review are harder to hold than digital; physical logistics add unpredictability. Contracts often have minimum billing commitments. | high |
|
05
Enterprise
RFP, procurement, multi-stakeholder approvals
|
2–6 weeks due to procurement, approvals, and internal coordination overhead | $50,000–$150,000+ when fully loaded with internal legal team coordination, outside counsel supervision, and vendor fees | Enterprise review adds layers of defensibility — outside counsel supervision, corporate legal sign-off, vendor contracts, and formal litigation hold compliance — but also significant process overhead. Procurement of a vendor can take days to weeks before any box is touched. Internal approvals, conflict checks, and billing arrangements add friction. The actual document review is often outsourced to a contract review firm anyway, so enterprise mostly adds supervision and compliance cost. Scope changes (common in litigation) trigger change orders and approval cycles. The upside is a fully defensible, well-documented production that opposing counsel has a hard time attacking. | medium |
|
AI
AI (Claude / Agent)
AI plus competent human review
|
AI cannot physically handle paper boxes; if documents are first scanned and digitized (a significant prerequisite project), AI can then assist review in hours — but digitization of 500 boxes may itself take 2–6 weeks | Digitization: $10,000–$40,000 (scanning, OCR, quality control); AI review software: $2,000–$10,000; human attorney review and validation: $5,000–$20,000 | This task is fundamentally physical — AI cannot open boxes, handle paper, or identify unlabeled or misfiled documents in the real world. Any AI involvement requires a full digitization step first, which is itself a substantial project with its own logistics, cost, and time. Once digitized, AI-assisted review tools (TAR/predictive coding, large language model classification) can dramatically accelerate relevance and privilege screening, but a supervising attorney must validate the AI's calls, especially on privilege — courts have not universally accepted AI-only privilege review. Failure modes include OCR errors on aged documents, handwritten materials that AI cannot read reliably, and misfiled or unlabeled boxes that AI cannot flag without human eyes first. The AI profile here should be understood as 'digitization + AI-assisted review + attorney QC' — not a plug-and-play solution. | medium |
This task is a poor fit for AI. See goodaitask.com to check what is worth handing to AI.
Check on Good AI Task →Time, visually
scale 0–38400 minRelated tasks
same categoryDraft a basic freelance services agreement that covers project scope, payment terms, intellectual property ownership, and a kill fee (compensation if the client cancels mid-project). All four elements are standard in freelance contract law and represent a moderately well-defined drafting task.
Condense a 45-page quarterly earnings report into a polished 500-word executive summary covering key financial metrics (revenue, margins, EPS, guidance) and strategic insights for a C-suite or investor audience.
Translating a 2,000-word legal contract from Spanish to English requires both fluent bilingual ability and command of legal terminology in both jurisdictions. Errors in legal translation can change meaning and enforceability, making review critical regardless of method.
Negotiating a 20% discount on a commercial office lease renewal requires market research, leverage identification, strategic communication, and iterative back-and-forth with a broker or landlord over several weeks. The outcome depends heavily on local vacancy rates, timing, and relationship dynamics — making it fundamentally a human-driven process even where AI can assist.