AI RFQ response for coatings and construction specs

Why RFQs are harder now

Sustainability is no longer a side note. Buildings operations and construction account for roughly 37 percent of energy related CO₂ emissions, which keeps carbon on every spec reviewer’s checklist (GlobalABC, 2024). That reality shows up in RFQs that ask for product specific EPDs, recycled content, VOC limits, and named alternates.

Traditional keyword search misses the mark when performance, substrate, exposure class, and environmental criteria interact. Teams spend hours hunting links, reconciling versions, and asking tech services to fill gaps. It’s busywork that drags response time and win rate.

What a good RFQ model actually does

Think of it as a seasoned spec writer in fast forward. It reads the full RFQ and attachments, extracts performance clauses and environmental requirements, interprets intent, then ranks closest fit products in your portfolio. It flags gaps where no product meets a threshold and suggests compliant alternates you already make.

Crucially, it returns evidence. Every recommendation comes paired with the latest EPD, product data sheet, and safety data sheet, plus citations to test methods and listings. No vague guesses, just traceable documents you can drop into a submittal.

Governed data beats clever prompts

Accuracy is a data problem, not a prompt problem. The model should only see an approved, role based dataset that mirrors how your company tells the truth. That means frozen product names and SKUs, verified performance values, current EPD metadata, and official documents in a single source of record.

We like a simple rule. If a claim can’t be tied to a controlled field or a file URL, it can’t ship in a response. Hallucinations vanish when the model has nothing ungoverned to invent.

Build the product library once, use it everywhere

Start with the commercial catalog, then enrich it with test reports, listings, and environmental credentials. For EPDs, store program operator, PCR reference, declared unit, plant or averaged scope, publication date, and validity date. EPDs typically have a five year validity window under common operator rules, so renewal tracking belongs in the library (IBU, 2024).

Link each product to its product data sheet and the 16 section OSHA compliant SDS so submittals assemble cleanly every time (OSHA, 2024). Add performance notes by system build, substrate, and exposure to keep recommendations context aware for coatings, sealants, membranes, and composites.

Competitive context, without copying claims

RFQs often name competitive products. Public competitor data can help the model understand the performance tier an RFQ expects, which improves matching. Use it as a compass, not cargo. Never let external claims enter your own datasheets or EPD fields. Keep a clear boundary between outside reference and inside assertions.

From answer draft to submittal packet

A strong workflow returns a response draft with three parts. First, the rationale that maps RFQ clauses to selected products and shows where criteria are exceeded or not met. Second, the submittal bundle with the correct EPD, data sheet, and SDS for each line item. Third, a checklist for missing items to obtain from the requester.

This is where governed data saves the day. If the EPD on file is close to expiry, the model can warn the team to avoid late cycle surprises. If a product has a plant specific and a company average EPD, the model can choose the one that aligns with the RFQ scope.

Controls that keep legal and technical comfortable

Give each role a lane. Sales sees prices, lead times, and final documents. Technical services controls performance fields and approvals. Sustainability controls EPD metadata and renewal status. Legal approves any templated language. Every AI suggestion should carry a short explanation and a link back to the governing source.

Simple review gates help. Changes to performance data require two person approval. New EPD uploads trigger a quick metadata check. Nothing slows a submittal like a mismatched declared unit or a stray PDF version.

How to measure impact without gaming the numbers

Track cycle time from RFQ receipt to first compliant draft. Track spec retention where the named product stays through submittals. Track rate of returns for missing or outdated environmental documents. Reliable cross industry benchmarks are sparse today, so compare plants, teams, and product families inside your own portfolio before declaring victory.

Implementation in weeks, not months

Most of the lift is data wrangling. A good partner will handle the collection across plants and systems, normalize formats, and reconcile legacy names so engineers are not buried in spreadsheets. The model training is the easy part once the product library is clean. That’s how AI turns into a practical submittal engine rather than another pilot stuck in limbo. It’s not glamours work, but it pays off quickly.

A short starter plan for coatings and construction

Pick five high velocity product families where RFQs are frequent. Build the governed product library and link EPDs, data sheets, and SDS files. Configure the model to read one market’s common RFQ templates and test on real jobs. Roll forward by family and region once response quality holds steady.

Win the spec by making it easy for reviewers to say yes. Give them the right product, the proof, and the paperwork, all in one tidy package.