How to Review an AI-Extracted BOQ Before Professional Use

The Importance of Human Review

For commercial teams, convenience must not replace accuracy. AI can assist with supported capture and table structuring, but it cannot apply engineering judgment or commercial context.

Professionals must compare the captured result with the source and confirm that the resulting BOQ reflects the intended commercial record.

Source-to-Output Quality Control Workflow

1. Confirm the source file: Ensure the AI extracted the correct, latest revision of the document.
2. Review section structure: Check that headers, sub-headers, and trade breakdowns were categorized correctly.
3. Compare item descriptions: Spot-check complex, multi-line descriptions for truncated text.
4. Check quantities: Pay special attention to decimal placement and values split across complex table layouts.
5. Check units: Verify that standard units (m, m2, nr) were captured accurately and assigned to the correct items.
6. Identify omitted rows: Look for items that may have been skipped due to page breaks or obscure formatting.
7. Identify duplicated rows: Ensure headers repeating across pages didn't create duplicate items.
8. Review symbols and technical text: Check engineering symbols (Ø, °, ±) for translation errors.
9. Check assumptions and exclusions: Ensure preliminary text and preamble notes were extracted and read.
10. Verify totals and formulas: Re-sum the quantities and amounts to ensure they match the source summary page.
11. Confirm output formatting: Ensure the exported data retains the reviewed structure and values in the target format.
12. Obtain qualified professional approval: A senior estimator or QS must sign off on the reviewed data.

A Practical Example

A hypothetical estimator imports a text-based civil works BOQ containing a multi-line heading above a table.

The heading is captured as a measurable item because the table structure is ambiguous. By checking the section structure and source content, the estimator identifies the issue, corrects the candidate and continues the review.

Limitations of AI Extraction

AI extraction models use statistical patterns and can return incorrect results. They do not verify whether captured quantities are physically correct.

Capture can omit, misread or restructure source content, and it cannot correct a flawed source BOQ without professional intervention.

How Quantara Supports Review

Quantara provides source records, captured information and review actions. Reviewers should compare each candidate with the original source using the available source-review screens; the exact presentation depends on the source and result.

Quantara currently focuses on supported document capture, BOQ structuring, project organization, templates, revisions and professional outputs.