Key Takeaways

  • Legora designed Tabular View to turn unstructured data rooms into spreadsheets where each contract forms a row and each extraction query forms a column.
  • Batch inference across thousands of documents creates compounding failure loops, so Legora forces a mandatory test run on ten rows before scaling to the full dataset.
  • Every extraction produces direct citation anchors linked to visual bounding boxes in the original PDF files so attorneys can verify source text in seconds.
  • After filling a matrix of up to one million cells, agents scan columns to surface anomalous clauses and write deal memoranda automatically.
  • Legal teams run this structured audit pattern using the Legora Tabular Due Diligence Workflow.

The Legora Tabular Due Diligence Workflow

Step 1: Document Bulk Ingestion

Drop all relevant project documents (e.g., 10,000 employment or supply contracts) into the agent or tabular view interface, populating each document as a distinct row.

Step 2: Column Query Definition

Define extraction criteria per column by phrasing specific analytical questions (e.g., 'What is the termination period?', 'Is there an IP assignment clause?', 'What is the severance compensation?').

Step 3: Sample Row Verification

Execute the fan-out extraction across an initial test subset of the first 10 rows to verify extraction accuracy, prompt formulation, and output validity before running full batch execution.

Step 4: Full Matrix Fan-Out and Citation Extraction

Run parallelized model inference across the entire dataset (up to a million cells), auto-populating fields while generating deep citation links tied to exact PDF bounding boxes.

Step 5: Agentic Synthesis and Risk Flagging

Task the agent with scanning matrix outputs to highlight strategic red flags (e.g., abnormal bonuses, unusual liabilities) and synthesize the extracted tabular findings directly into a client-ready draft memorandum.

When This Works (and When It Doesn't)

This workflow shines in high-volume document audits where documents share a similar underlying purpose. Think M&A due diligence, vendor audits, or fundraising reviews across hundreds or thousands of agreements. Lauritzen noted that when teams work on large transactions, “Each row is one employment contract. And then for cells, you ask questions basically.” (play from 34:49) Setting queries as columns gives developers deterministic bounds. When you need to pull termination notice periods across 10,000 vendor agreements, a grid gives lawyers immediate visibility over what is missing and what looks strange.

It fails when files lack structural or topical unity. If you dump a mixed folder of Board meeting minutes, tax receipts, patent filings, and email chains into the rows, single-column questions break down. Asking “What is the severance package?” (play from 12:06) against an IRS tax return returns junk or hallucinations. The ten-row test run prevents large token spend on flawed prompts, but it cannot fix poor document curation. Lauritzen explained the guardrail plainly: “Our tabular view actually will run the first 10 rows. Just check, right? Does this make sense? Did it show us what we thought it was going to show us?” (play from 37:36) If the sample run fails, stop and re-index your folder before queueing the remaining 9,990 files.

Furthermore, hallucination risk remains fatal in legal review without exact verification. Lauritzen built the interface around visual breadcrumbs: “All of the stuff that we produce also has citations back to source documents and so you can basically review it super easily if you want to just make sure that everything's right.” (play from 36:01) Without bounding-box citations that snap to the exact sentence in a 90-page PDF, junior lawyers spend more time hunting down phantom quotes than reading the original contracts.

What to Do With This

Apply this pattern if you are preparing your company for an acquisition audit or Series B diligence this week.

Gather your 80 customer order forms and place them in a single folder. Treat each agreement as a row in your pipeline. Instead of asking a generalist model to summarize each contract, set four strict column prompts: renewal term, liability cap, payment terms, and governing law.

Run the extraction on the first five agreements. Read the model outputs side by side against the source clauses. If the model misinterprets mutual indemnification as a standard liability cap, tweak your column prompt text before processing the rest. Once the prompts return exact figures on the test slice, trigger the run across the remaining 75 files. Finally, ask your agent to sort the completed table by liability cap and list any contract that exposes the business to uncapped damages.