AI for Proposal and Scope Generation for Construction

The system is trained to recognize delivery method as a primary filter when pulling comparable project data. A design-build proposal requires scope narratives that carry design responsibility language and owner-furnished information assumptions that a hard-bid package does not. When a Preconstruction Manager selects the delivery method at the start of a new proposal, the AI draws from the matching subset of historical projects and applies the appropriate scope structure, exclusion language, and subcontractor qualification requirements for that method. Your team reviews and adjusts before anything goes to an owner or CM.

Yes. Revenue Institute integrates with your prequalification records and COI tracking system so that when a scope section references a trade, the proposal flags whether your preferred subs in that division are currently qualified, bonded to the required limit, and carrying the insurance your standard schedule requires. If a sub is expired or unqualified, the system surfaces that before the proposal is finalized rather than after award. This is particularly useful for specialty trades where the qualified sub list is short and bonding capacity is a real constraint.

The system analyzes your historical RFI logs and change order records in Procore to identify which scope items generated disputes or cost overruns on comparable past projects. Those patterns are translated into suggested exclusion or clarification language in the new proposal. For example, if owner-furnished equipment coordination became a change order issue on three prior healthcare projects, that item surfaces as a recommended clarification on the next healthcare bid. Your Preconstruction Manager reviews and accepts or edits each suggestion - the system does not publish language without human review.

No. The system handles first-draft scope narrative, exclusion language, and qualification requirements - the document production work that currently consumes hours before any real preconstruction judgment is applied. The Preconstruction Manager and estimating team still own scope strategy, risk decisions, and client-specific adjustments. The goal is to move the team's time from formatting and reformatting documents to reviewing and refining a substantive first draft. Proposal quality goes up because experienced people are spending their time on judgment calls rather than copy-paste work.

We build the connection to whatever project management platform you run - Procore is common at this scale - pulling project data, RFI logs, submittal records, and change order history without requiring a custom integration build on your side. Setup involves configuring which project types and data fields are in scope, mapping your CSI division structure, and loading your standard insurance and bonding schedule - and the system is drafting real proposals inside the first 100 days. Your project data stays in your system of record - the AI layer reads and synthesizes it rather than replacing your project management workflow.

When historical data for a specific project type is thin, the system flags the gap rather than generating low-confidence scope language without warning. Your Preconstruction Manager is prompted to provide additional input or review the draft more closely before it is used. Over time, as the firm completes more work in a new sector, the system's suggestions for that project type improve. In the interim, the AI still adds value by handling the structural and compliance-related sections - insurance language, prequalification requirements, standard exclusions - even when project-type-specific scope history is limited.