Corpl Tabular Review

When to use

Use this when you must review many documents of the same type — a stack of contracts, leases, employment agreements, or corporate resolutions — and need them comparable side by side. A consistent table turns a document pile into a scannable matrix where outliers and gaps jump out.

Not for: deep single-document analysis of a bespoke agreement; open-ended issue-spotting (use corpl-diligence-issue-extraction on the outputs); building the disclosure schedule (use corpl-material-contract-schedule). Tabular review structures and compares facts; the legal significance of what the table surfaces is assessed by counsel.

Method

1. Define the document set and the columns. Choose the attributes that matter for this review type (parties, term, key clauses, dollar values, flags) before reading — a stable schema is what makes rows comparable.
2. Read each document into a row, filling every column. Decision point: if an attribute is absent or ambiguous, record "not present" or "unclear" — never leave a cell blank or guess, since blanks and guesses both hide risk.
3. Cite the source location (section/page) for load-bearing cells so any value can be traced.
4. Normalize values so columns are truly comparable (consistent date format, currency, yes/no for clause presence).
5. Add a flags column for anything off-market, missing, or notable, so the table doubles as a triage layer.
6. Scan for outliers and gaps across rows — the whole point is that inconsistency becomes visible.
7. Hand the completed table to the relevant workflow (issue extraction, schedule) and to counsel for significance calls.

Example

Review of 25 customer contracts. Columns: counterparty, effective date, term, auto-renew, value/yr, assignment clause, change-of-control, termination-for-convenience, governing law, flags. Filled 25 rows with §-level citations; normalized all values to a common format. Scan surfaced 4 contracts missing any assignment clause and 2 with unusually long 5-year auto-renew terms — flagged. Table routed to issue extraction.

Pitfalls

• Unstable columns. Changing the schema mid-review makes rows non-comparable; lock it first.
• Blank cells. A blank is ambiguous — is it absent, or unreviewed? Use explicit "not present"/"unclear."
• Uncited load-bearing values. A key value with no source location can't be verified later.
• Reading significance into the table. The table shows what is; whether a missing clause is a legal problem is counsel's call.

Output format

TABULAR REVIEW — <document type> | Set: <n documents>
Column schema (locked): <attr1 | attr2 | ... | flags>
  | # | Doc | <attr1> | <attr2> | ... | Source (§/pg) | Flags |
Outliers & gaps: <observation>
Coverage: <reviewed / total>
Fact matrix — significance to be assessed by counsel

Reference

Tabular review is the workhorse of high-volume diligence: one row per document, one column per attribute, a locked schema, explicit "not present"/"unclear" values, and source citations on load-bearing cells. Normalization makes columns comparable so outliers surface on a scan. The table is a fact matrix feeding corpl-diligence-issue-extraction and corpl-material-contract-schedule; legal significance and materiality determinations are made by counsel.