Asked by real desks. Answered without the brochure voice.

Each page answers one question the way we would across a counter: answer first, mechanism after, numbers with their qualifiers attached.

• What is omission detection in construction takeoff?

  Omission detection is the practice of cross-checking the independent sources of truth in a drawing set, the schedule, the plan symbols, and the legend, to catch items that one source contains and another misses. Quoting.ai Takeoff automates this as the 4 Eyes check: it counts all three sources separately and lists every disagreement for a human decision. It matters because a missed opening is not a counting error; it is installed cost, materials plus labor plus schedule impact, that the bidder eats months after the bid.

• How do distributors reduce quote turnaround time?

  Distributors we talk to average about 30 minutes of desk work per quote, and total turnaround stretches to hours or even days once clarifications and availability checks stack up. The fixes that actually move the number: route every channel into one queue with a catch-all inbox, automate line-item extraction and SKU matching against the real item file, price from ERP price levels automatically, and keep a human approving in an inbox instead of retyping. Desks running this pattern draft quotes in minutes and spend their time on judgment calls, not data entry.

• What is AI quoting software for building materials distributors?

  AI quoting software reads a distributor's inbound RFQs in whatever form they arrive, email, PDF, WhatsApp, fax, or voice note, extracts line items, matches each line to a SKU in the distributor's own item file, applies that customer's price levels from the ERP, and drafts a quote a human approves before it goes out. Real systems finish the job with ERP write-back: the approved quote becomes a document in the system of record. The economics: distributors we talk to put roughly $110 of labor into a 100-line quote and about 30 minutes of desk time into each one. The software turns that half hour into minutes of review.

• How does AI order entry work for distributors?

  AI order entry runs a pipeline in front of your ERP. Every channel, email, PDF, WhatsApp, fax, and voice, routes into one catch-all inbox. Each message is classified first: new order, quote request, or follow-up. Line items are extracted and matched against your own item file, not a generic catalog, then priced from your customer price levels in the ERP. A human approves each drafted order in an inbox-style queue, and anything ambiguous is flagged for a person, never guessed. On DDI Inform and Spruce, approved orders write back to the ERP automatically. One metric keeps it honest: Human Edit Rate, how often a reviewer corrects a draft.

• Can AI read RFQs from WhatsApp messages and voicemails?

  Yes. Quoting.ai Supply takes RFQs from WhatsApp messages and voice notes, voicemails, faxes, email, and PDF attachments, and transcribes voice in English, Spanish, Hebrew, and Yiddish. Every request lands in the same approval inbox as a drafted quote: line items matched against your item file, customer price levels applied from the ERP, and a human confirming before anything sends. That last part answers the real objection. A WhatsApp order is flimsy when it lives in one salesperson's phone; routed into an auditable queue with a written quote sent back, it is a documented transaction like any emailed RFQ.

• Will AI confuse a quote request with a purchase order?

  It should not, because classification comes first. Before Quoting.ai Supply extracts a single line item, it decides what a message is: an RFQ, a purchase order, a follow-up on an open quote, or noise. Each type gets its own handling, and anything ambiguous stops in the approval inbox for a human call, not a guess. Every classification sits next to the original message, and the Human Edit Rate tracks how often reviewers correct the system. Distributors we talk to raise this objection early, and the honest answer is a system built to ask when it is not sure.

• Is AI quoting accurate enough for real distributor pricing?

  No, and no honest vendor claims 100 percent. The right question is whether errors are visible and controlled before a customer sees them. Quoting.ai Supply prices from your ERP's customer price levels, a deterministic read rather than a model guess, and flags any line it cannot resolve instead of filling it in. Every draft lands in an approval inbox, and the Human Edit Rate, how often your reviewer corrects a draft, is measured and shown. You choose the mode, Assist, Guarded, or Autopilot, and raise autonomy only when your own numbers earn it. Accuracy here is a metric you watch, not a claim you take.

• What does manual quoting actually cost a distributor?

  Manual quoting costs a distributor three ways. Direct labor: distributors we talk to put roughly $110 of labor into a 100-line quote, at about 30 minutes of desk time each. Declined work: distributors we talk to report busy desks turning away 30 to 50 percent of inbound RFQs for capacity, revenue handed back before anyone reads the request. Headcount: the manual fix is hiring more inside salespeople, and even then distributors we talk to describe one salesperson doing about 100 quotes a day and still working three hours past close. The real total is labor plus declined revenue plus the salaries bought to keep up.

• Why do distributors decline RFQs, and how do you stop?

  Distributors decline RFQs because the desk runs out of hours, not because the work is unprofitable. Busy desks decline 30 to 50 percent of inbound RFQs for capacity, distributors we talk to report: big customers get quoted, messy requests get silence. Every declined RFQ is revenue handed back and a customer trained to send the next one elsewhere. The fix is not tighter triage. It is removing the desk work from each quote: one queue for every channel, automated drafting against your item file and price levels, a human approving instead of retyping. Same-day answers become the default for every request, not the favored few.

• How long does AI quoting software take to set up?

  Less than the fear suggests, if the software sits in front of your ERP instead of replacing it. Quoting.ai Supply setup is two steps: connect the inbox that already receives your RFQs (Gmail, Outlook, or WhatsApp) and upload your inventory file. Then you book a kickoff call, included, where a human gets your desk live and sets the rollout mode. It is not zero-touch, on purpose. ERP write-back runs on a separate track: live today for DDI Inform and Spruce, in development for Epicor, NetSuite, and SAP. On other systems you start in ERP-light mode and add write-back when your ERP is supported.

• What is human-in-the-loop quoting?

  Human-in-the-loop quoting is an automation pattern where AI drafts the quote and a person approves it before anything reaches a customer or the ERP. The system reads the RFQ, matches line items against the item file, and applies the customer's price levels; the human reviews the draft in an approval inbox, fixes what is wrong, and sends. Every edit is counted as the Human Edit Rate, so trust becomes a measured number instead of a feeling. As the rate falls, review gets lighter, stepping from Assist to Guarded to Autopilot, with the human in command at every rung.

• AI takeoff vs manual takeoff in Bluebeam: what actually changes?

  What changes is the mechanical layer: counting symbols, tagging sheets, retyping the schedule. Estimators we work with put 70 percent of door estimating time into data entry, and that is the part AI takeoff compresses. What stays is everything that wins the bid: reading scope, pricing strategy, hardware judgment, deciding which jobs to chase. The bigger shift is verification: Quoting.ai Takeoff cross-checks the schedule against plan symbols against the legend with the 4 Eyes check and lists every disagreement for a human call. Honest limit: live trades today are doors and windows, with walls and framing in early access.

• How do you count doors from architectural plans automatically?

  Automatic door counting reads the same three sources an estimator reads. The door schedule is parsed as a table, the door symbols are detected on every plan sheet, and the legend is indexed as the set's type dictionary. The three counts are then reconciled, and every disagreement becomes a listed discrepancy for a human decision. Quoting.ai Takeoff runs this in the browser on the full drawing set and numbers each opening by apartment (1A01, 1A02) so the count carries straight into submittals. The manual baseline: estimators we work with report 15 to 20 hours per job at roughly 2,500 openings, 70 percent of it data entry.

• Why do door schedules and plan counts disagree?

  Because the schedule and the plans are edited separately, under deadline, by different hands. A revision lands mid-bid and updates the sheets but not the schedule. A row gets added that nobody ever places as a symbol. A symbol gets copied with a duplicated unit plan and never gets a row. The legend keeps types that drifted from both. Estimators we work with spend 15 to 20 hours per job at roughly 2,500 openings, and hand reconciliation at that scale is sampling, not checking. The reliable fix is counting all three sources independently and reconciling the differences.