Missing-action detection
Flag language that appears to require a deliverable even when it does not use a tidy “submit” phrase.
AI submittal register generator
An AI submittal register generator with the source attached.
SubmittalsHQ combines deterministic specification processing with targeted AI review. The result is a draft your team can inspect, correct, and defend.
A controlled use of AI
Interpretation with an audit trail.
Construction specifications are structured documents, not loose marketing text. SubmittalsHQ first uses deterministic processing to find sections, headings, paragraphs, and explicit directives. AI is then applied selectively where interpretation adds value.
That distinction matters: the original requirement remains available for review, and generated suggestions never become invisible facts.
Human review remains part of the workflow.
AI may suggest a better title, identify a likely type, or flag related requirements. The reviewer can accept, edit, merge, split, or reject the suggestion.
Where assistance helps
Clearer register titles
Convert vague source wording into a concise description while preserving the quoted evidence.
Grouping and classification
Suggest types, parties, merges, and splits for review instead of flattening every match into a row.
Why a hybrid approach
Not every part of specification processing is an AI problem.
Section numbers, page locations, paragraph identifiers, exact source text, and document hierarchy should be reconstructed and retained deterministically wherever possible. These are facts about the source document and form the audit trail.
Interpretive tasks are different. A short child paragraph may need a clearer action title. Two requirements may describe one logical package. Responsibility may be implied by trade context. These are places where targeted AI assistance can be useful—as long as the suggestion is attached to evidence and remains reviewable.
The operating principle
Use deterministic processing to establish what the document says and where it says it. Use AI selectively to help interpret what the requirement means for a working register. Use a person to approve the result.
A suggestion should come with enough context to judge it.
Review elementPurpose
Original source textPreserves the contract language rather than replacing it with generated copy.
Section, page, and paragraphProvides a direct location for independent verification.
Interpreted action titleTranslates the requirement into a concise, project-usable description.
Suggested type and partyHelps organize the draft while remaining editable.
Confidence and reasonSignals where language, hierarchy, classification, or grouping is uncertain.
Available decisionConfirm, edit, merge, split, suppress, or return the item for further review.
Turning a vague label into a useful title
Source hierarchies often place “Shop Drawings” in a parent paragraph followed by short children such as “Wiring diagrams” or “Control schematics.” Copying only the child loses the action; copying only the parent loses the subject. A suggested title can combine both: “Power, signal, and control wiring diagrams.” The source hierarchy remains visible for confirmation.
Finding a requirement without a simple keyword
A paragraph may direct the contractor to “provide certified test reports” or state that qualification data “shall demonstrate five years of experience.” The likely deliverable can be flagged even when the wording does not match a narrow search for “submit.” A reviewer then determines whether it belongs in the register and at what stage.
Recognizing repeated evidence
The same product data requirement may appear in a summary paragraph and again beneath a product-specific heading. Instead of silently deleting one or creating two rows, the system can show the related evidence and suggest a merge. The reviewer decides whether the repetition is truly duplicative.
What the generator should not do
- Present inferred requirements as unquestionable facts
- Invent source language, paragraph numbers, or responsibilities
- Hide low-confidence items to make the output look cleaner
- Assume every keyword match belongs in the register
- Decide contractual scope on behalf of the project team
What a good result looks like
The draft is concise enough to review, specific enough to assign, and complete enough to expose likely gaps. Every entry can be traced to the project manual, and every material interpretation can be accepted or changed by a person.
That is a more useful standard than the number of rows generated or the speed of the first pass.
AI submittal register questions
Does AI read the entire project manual?
The product processes the project manual as a structured document. Deterministic extraction and targeted AI review work together; it is not simply a single prompt sent to a general chatbot.
Can AI guarantee that every requirement is found?
No responsible review tool should make that promise. Document quality, unusual authoring, cross-references, drawings, addenda, and project-specific interpretation all affect completeness. The product helps teams perform and document a stronger review.
Can generated titles be changed?
Yes. Titles, types, parties, and grouping suggestions are reviewable and editable. The supporting source remains attached.
Why not generate a spreadsheet and skip the review interface?
Because the important work is not merely extracting rows. Reviewers need to resolve uncertain language, duplicates, hierarchy, and grouping before the spreadsheet becomes a dependable project register.
Review a generated register with us.
Evaluate an AI-generated register before relying on it.
Manually check representative sections
Select straightforward and complex technical sections. Compare the draft with the complete section, not only the extracted evidence.
Follow uncertain items to the source
Validate the section, page, paragraph, hierarchy, and exact language. A plausible title is not enough if its evidence is wrong.
Review counts and classifications
Look for unusual concentrations, missing types, duplicated actions, empty sections, and responsibility suggestions that do not match the project.
Why row count is a poor benchmark
A system can generate more rows by treating every keyword match or child bullet as a separate item. That may look comprehensive while increasing duplicates and hiding relationships. A better benchmark combines recall, precision, title usefulness, source accuracy, and the amount of human cleanup required.
Why a perfect-looking output is also suspicious
Real project manuals contain ambiguity. A trustworthy system should expose uncertain actions, malformed hierarchy, cross-references, and grouping choices. Review flags are evidence of controlled interpretation, not necessarily product weakness.