AI Product Intelligence for Specs, Minus Busywork

The RFQ grind, automated

Spec review should be pattern recognition, not paper shuffling. An AI layer reads RFQs, drawings, and submittal templates, then maps requirements to a manufacturer’s catalog in minutes. It flags what matters, from dimensions and fire ratings to warranty language, so sales and technical teams focus on fit, not hunting.

Basis‑of‑design with receipts

Named products guide the spec, yet they hide in dense sections. The system extracts basis‑of‑design SKUs, identifies the manufacturer, and surfaces head‑to‑head attributes. It proposes closest‑fit alternatives from your lineup, with a short rationale tied to the exact spec line so reviewers can trust the suggestion like a good film editor who keeps every cut.

Compliance scanning that actually helps win

Rules shift by market. The layer scans for banned substances, VOC limits, recycled content asks, and Buy Clean triggers. California’s program lists seven GWP limits across steel, glass, rebar, and insulation, with updates taking effect in 2025, which makes auto‑checks practical instead of theoretical (DGS, 2025).

EPD‑aware from the first read

EPDs age, PCRs update, and submittals get rejected when details are stale. Expert systems read EPD PDFs and machine‑readable fields, then tag validity date, program operator, and PCR version. Most EPDs carry a five‑year validity window that must be monitored across the pipeline, not just at bid time (EPD International, 2025). Many PCRs follow a five‑year review cycle too, which means renewal timing should be part of every pursuit plan (UL Solutions, 2024).

Auto‑fill that learns

Questionnaires repeat across owners, GCs, and rating systems. The AI pre‑answers standard forms, points out missing attributes, and writes short justifications linked to the originating document. Each submital becomes training data, so accuracy improves and the number of clarifications drops on the next project.

Security that procurement can sign

Data lives behind clear boundaries. The model runs in a tenant that keeps your catalog, RFQs, and competitive intel separated from other firms. Access controls mirror roles, logs capture who saw what, and every AI suggestion preserves source snippets for audit. We push for proof over promises, because compliance teams do not accept magic.

What expert‑level AI looks like

• Provenance everywhere, every answer links to the exact page and paragraph.
• Catalog‑first matching, competitor‑aware and rules‑driven substitutions.
• EPD metadata checks, validity date, PCR version, and operator noted on every match.
• Structured exports for submittals and portals, including Buy Clean and LEED v5 document sets.
• Admin controls for redlines, exception handling, and change history.

The feedback loop product teams crave

The same layer records why reps rate an RFQ as A or D‑minus, which objections stalled, and which attributes keep losing on alternates. Patterns become roadmaps. That closed‑loop view turns belly‑feel into backlog decisions for EPD renewals, SKU tweaks, and faster documentation.

ROI, without the fuzzy math

Some programs even return cash to keep momentum. Caltrans will reimburse up to 4,500 dollars per plant per EPD for asphalt and concrete mixtures under its FHWA Climate Challenge through January 30, 2026, subject to funds, which offsets part of the documentation lift (Caltrans, 2026). Exact time savings vary by scope, yet teams routinely move from days to hours because the reading and retyping is no longer manual.

From triage to targeting

When specs arrive, the winning posture is simple, respond faster with proof. An AI product intelligence layer narrows the list to winnable fits, fills the paperwork with traceable answers, and keeps EPD details current so nothing derails late in review. The result is clarity on what to chase now and what to fix next in the portfolio.