AI bidding software for construction. The AI reads the specification, finds the scope, measures the drawings, prices against your cost data, reads the quotes that come back and drafts the proposal — and every item it produces cites the page it came from, so an estimator reviews the work instead of trusting it.
There are two ways to build AI into bidding. One is to ask a model for a number and put it in the bid. The other is to use AI for the work that is slow, repetitive and mechanical — reading four hundred pages of specification, finding every instance of a symbol across a plan set, extracting what a subcontractor's quote excluded — and to leave the judgement to the estimator. The first approach demonstrates well and cannot be defended in a scope meeting. BidcoreAI is built on the second.
Concretely: the AI reads the specification book and drawing set and returns the scope it found, each item citing the section or sheet it came from. It measures the drawings and proposes quantities, each shown on the sheet at the scale it was measured. It reads incoming subcontractor quotes and extracts inclusions, exclusions, substitutions and alternates. It drafts the proposal from the estimate that was actually priced. At every one of those steps a person confirms, corrects or rejects the output before it moves downstream.
The pricing itself is deliberately not a model output. Costs come from your own historical unit rates, or a regional benchmark where you have no history — a database lookup, not a prediction. This matters because an invented price is indistinguishable from a real one until the job is running, and because your own costs are more predictive of your costs than any model trained on somebody else's.
What this buys is speed with an audit trail. The slow parts of a bid — reading, measuring, comparing — compress dramatically. The parts that require judgement, and the parts you will have to defend later, stay with the estimator and stay traceable. That is a narrower claim than "AI writes your bid", and it is the one that holds up when a number is challenged six months after award.
Each one is a task that is mechanical, verifiable and enormously time-consuming by hand.
Specification books, drawings and addenda read end to end, with scope returned as a checkable list.
Repeating conditions found and measured across the whole set, then presented for review.
Contract and specification terms that change what a job is worth, extracted with citations.
Subcontractor quotes parsed for what they actually cover, which is rarely what the total suggests.
Written from the priced estimate, in the structure the client or solicitation requires.
The part that makes the rest defensible, and the part most AI demos leave out.
Stated as specifically as possible, because vagueness is how AI claims usually get made.
The full specification and drawing set read, with scope returned as a list where every item cites its page.
Measurements proposed across the set and displayed on the drawing, at the scale used, for confirmation.
Door, fixture, equipment, finish and bar schedules parsed into structured data and reconciled against the plans.
A re-issued set compared against the previous one, with the differences listed rather than left to be spotted.
Clause-level contract and compliance risk pulled out with citations, so the review is checking rather than reading.
Incoming subcontractor quotes read for inclusions, exclusions, substitutions and coverage against the package sent.
A first draft written from the priced estimate in the required structure, which you then edit rather than write.
Costs looked up in your database, never generated. An invented price is the one output that cannot be reviewed.
Every AI output has a confirm, correct or reject step, recorded against the person who made it.
Sheet, section and page references carried from extraction all the way into the Excel export.
A second estimator can review AI-proposed quantities before they are priced, with the changes recorded.
Your drawings, costs and estimates are your data. Cost history is used to price your jobs, not to train a public model.
The reading list an estimator is supposed to get through in the week before a bid is due.
The document load is what scales with project size — and it is exactly the part AI removes.
Every step produces something a person then checks.
Large gains in the mechanical work, no change in who is accountable for the number.
A specification book that takes two days to read properly is read in full every time, including on the bids where nobody would otherwise have had the hours.
Confirming a proposed quantity on the sheet is faster and more accurate than producing it from scratch at eleven at night.
Citations on every scope item and sheet references on every quantity mean a challenged number can be shown rather than argued.
Contract review that used to happen only on the big pursuits happens on all of them, because it costs minutes rather than an afternoon.
The binding constraint is estimator hours. Compressing the mechanical work is what turns three properly bid jobs a week into eight.
Pricing stays a database lookup. It is the one place where an AI answer would be unverifiable, so it is the one place AI is not used.
Because a bid is a commitment, and commitments have to be checkable.
Every extracted scope item and every measurement carries the page or sheet it came from, right through to the export.
Confirm, correct or reject at every step. Nothing reaches a price without somebody accepting it.
Pricing comes from your database rather than from a model, because a generated price cannot be reviewed.
Your drawings, costs and estimates are used to run your projects, not to train a public model.
Solicitations, FAR-aware risk review and evaluation-criteria proposals — see federal bidding software.
Where you want finished work rather than assistance, estimating services run in this same platform.
AI assistance changes what one estimator can cover. It does not help on a week where the capacity simply is not there. Estimators working in this same platform will measure and price a package for you and hand it straight into your bid, typically within 24 to 72 hours.
One platform, one cost database, one set of drawings — whichever scope you are pricing today.
Bring a set you have priced. The useful question is not whether it is fast — it is what it found that you did not.
